Journal of the science of food and agriculture, tập 90, số 11, 2010

Significant differences between fruits of mycorrhizal and non-mycorrhizal plants were found for the majority of phenolic compounds and for some minerals in plants treated with 6 mmol L −

Review

Received: 26 October 2009 Revised: 12 May 2010 Accepted: 17 May 2010 Published online in Wiley Interscience: 16 June 2010(www.interscience.wiley.com) DOI 10.1002/jsfa.4041

Genetic evaluation of dairy cattle using

a simple heritable genetic ground

Abstract

The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index Automated genotyping enables the determination of many single-nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied) However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors A one-step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population An augmented pedigree–genomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals.

c

2010 Society of Chemical Industry

Keywords: genomic breeding value; methods; QTL; SNP; linear model; genomic relationship

INTRODUCTION

Laboratory techniques and mathematical and statistical methods

for the evaluation of animal breeding values (BV) are undergoing

continuous improvement Molecular genetic data can be analysed

for associations with production traits.1However, the relationships

between farm animal production traits and molecular-genetic

information are often measured imprecisely Many studies of the

relationships between genetic markers and quantitative traits

are methodologically flawed and do not reflect contemporary

breeding practices; sometimes even the basic context of breeding

and farming conditions are not taken into consideration This

type of research requires very careful experimental design that

considers pedigree structure and generates an adequate quantity

of data using sophisticated mathematical and statistical methods

The general objective of each evaluation is to explain the

variability of the characteristics studied and determine why

animals or groups of animals differ from one another Farm

animal productivity is simultaneously influenced by many genetic

and non-genetic factors, and it is practically impossible to

plan a completely balanced experiment Therefore, sophisticated

statistical procedures must be used

Recently, the evaluation of animal performance based on

molecular-genetic information has become more widespread

Dairy cattle populations evaluated for several groups of traits

of moderate and low heritability (production, conformation,

reproduction) using genetic markers have been presented by

several authors2 – 8 as well as in Interbull Bulletin No 39.9In pig

and poultry populations, whole-genome scanning and genetic

diversity analysis are quite extensive.10,11The methodologies used

may be generalised across species, but several facts influence

methodological advancements in relation to dairy cattle: the cost

of genotyping is favourable relative to the price of each animal;

advanced reproductive methods are routinely applied and the BV

of sires is therefore highly reliable; there is a huge global marketfor sperm and breeding animals encompassing many companiesand breeder associations; and worldwide workshops on animalevaluation are frequently organised through publications such as

the Interbull Bulletin.9The aim of this review is to provide a survey of the proceduresused to evaluate animal production traits using simple heritablegenetic markers Some basic methodological approaches will beemphasised, particularly those that connect genomic breedingvalue (GEBV) to traditional methodologies

ANIMAL EVALUATION

An overview of the standard procedures used worldwide inthe genetic evaluation (BV prediction) of production traits infarm animals, as well as new developments, are continuouslypublished by ICAR, Interbeef, Interbull, Interstallion and otherinternational organisations A number of countries cooperate

in the international evaluation of dairy cattle, which invokesinternational inspection of the methods used to estimate BV indomestic country.9Here, special attention is paid to the continuousupdating of national and international evaluation procedures

∗ Correspondence to: Jindrich Citek, Department of Genetics, Faculty of

Agriculture, South Bohemia University, Studentska 13, CZ 370 05 ˇ Cesk´e Budˇejovice, Czech Republic E-mail: citek@zf.jcu.cz

a Institute of Animal Science, CZ 104 01 Praha 10-Uhrineves, Czech Republic

b Department of Genetics, Faculty of Agriculture, South Bohemia University,

Studentska 13, CZ 370 05 Ceske Budejovice, Czech Republic

J Sci Food Agric 2010; 90: 1765–1773 www.soci.org
2010 Society of Chemical Industryc

Generally, mixed linear models of best linear unbiased prediction

(BLUP) in an animal model (AM) are used, and a pedigree of three

or more generations of ancestors is taken into account according

to the model equation:

where Y is the vector of measured performances, X and Z are

known matrices that relate performance to the systematic effects

of the breeding environment and the animals, b and u are the

estimated vectors of fixed systematic environmental effects and

the random effects of an animal (BV) with the additive numerator

relationship matrix (A), and e is the vector of random error.

Using this model equation, a system of normal equations is

constructed in which the unknown constants (b) and (u) are

estimated These systems of equations are vast, and special

algorithms are required for their solution.12

Most of the variability in any measured production trait is

caused by systematic environmental effects The influence of

the herd–year–season, or herd–test-day, which identifies a

contemporary group of animals kept under the same conditions,

is usually the most important factor

Evaluations are generally oriented to the MT-AM (multi-trait

animal model), RR-TDAM (random regression test-day animal

model), AM-maternal, and nonlinear methodologies for survival

(kit) analysis.13 – 23

It is important to find a method of evaluation that minimises

residual error and simultaneously considers all of the effects that

may influence the performance variable being measured From

a genetic perspective, it is important to ask: What proportion

of variability is explained by the statistical model used? Is this

proportion different in a model that does not account for genetic

effects? Is this model the best (optimal) of all the possibilities

tested? The proportion of variability explained by the statistical

model used (R2) and other information criteria for testing the

suitability of the model, such as Akaike’s information criterion

(AIC), Bayes information criterion (BIC), Bayes factor (BF) and the

likelihood ratio test (LRT), are very important in answering these

questions.24 – 28

Molecular-genetic information can be used to improve selection

programmes.29Animals are evaluated more accurately when their

entire genetic value is partitioned into causal factors and

within-family genetic components are exploited The use of

molecular-genetic markers in breeding is the inclusion of additional criteria in

the selection indices These markers increase selection differences

relative to existing traditional breeding programmes by decreasing

the correlation among sib individuals, increasing the accuracy of

animal selection, allowing the utilisation of genetic variability

that is usually included in non-utilisable residuum, and allowing

a shortening of the generation interval (because they may be

analysed in young animals) The use of selection markers is

conditional upon the timely laboratory analysis of the entire

subpopulation subjected to pre-selection (e.g., young bulls) and

rapid application before the determined gene linkages change

This requires frequent updates of selection indices, as shown

below (Eqn (2)) The consistent application of genomic selection

markedly reduces the cost of a selection programme.3,30

However, data analysis becomes more complicated, the number

of estimated parameters becomes higher, and a modified

information criterion (mBIC) is necessary for the selection of a

suitable model of evaluation.31,32

In order to select individuals for breeding, marker-assisted

selection (MAS) may be applied if several genetic markers are to

be used Alternatively, genomic selection utilises high numbers ofmarkers that densely cover the whole genome.3,33BV is usuallycalculated in two steps In the first step, the regression coefficients

(v) (substitution effects of the alleles of a considered locus) are

determined in a reference population with known performanceand highly reliable BVs This reference population usually includesonly a part of the population under selection From the first step,quantitative trait loci (QTL) effects are estimated Subsequently,

BV is determined for all of the young animals in the evaluated(sub)population by means of a selection index, as described inEqn (2).30

The reference population and the evaluated population areseparated by at least one generation Therefore, the relationshipsbetween markers and QTLs determined in the older generationmay not be fully applicable to the younger evaluated population,

as the QTLs are not fully covered by study markers Furthermore,the influences of selection, mutation, immigration of sires usedintensively in artificial insemination, changes in environment, andthe development of the commercial population under selectioncan also affect the applicability of QTL data across generations

Therefore, it is necessary to periodically redetermine u in Eqn (1), allele frequencies (q) in Eqn (5), their inherence in the genotypes

of individual animals (T), and regression coefficients (v) in (3) so

that the gap between the reference and the evaluated populationwill be as small as possible.3,30,34

The GEBV of a given trait is calculated based on known loci andremaining polygenes according to the selection index:

GEBVj = k1DGVj + k2u∗j (2)where GEBVj is the genomic (total) BV for an individual (j) determined based on the genomic information at the locus (i)

and remaining polygenic effect DGVjis the direct genetic value,calculated as the sum of BVs for a particular loci:

where T ij (with regard to Eqn (9)) is the ith element in the jth row

of the known incidence matrix correlating the genetic effects of

particular alleles to the observed individual, v ij is the vector of

genetic marker effects, u∗jrepresents the BV calculated based on

the remaining polygenes, and k1and k2 are the weights of theinformation sources in the index.35

If the GEBV is calculated for young animals without their own

production records, u∗j represents only information about theirparents In cases where a high density of genetic markers is

available, the u∗jin Eqn (2) is frequently omitted.

GENETICALLY CONDITIONED VARIABILITY

unpredictable residual (σ2 ) components, plus covariance caused

by genotype/environment interaction (2σGE).36It is generally sumed that the genotype/environment interaction is negligible.Therefore:

genetic effects are also reflected in performance, and if they are

omitted the results of the evaluation may be distorted

One locus

Some genes may have a direct impact on quantitative production

traits, and therefore efforts are made to utilise them directly in

breeding These candidate genes (or quantitative trait loci (QTL), if

describing markers) explain a portion of the genetic variability in

the trait being considered

At two alleles in a locus, the portion of the additive genetic

variance conditioned by one gene (i) can be approximated by a

binomial distribution:36

where q i is the frequency of the studied allele at locus i and v iis

the additive substitution effect of alleles at locus i.

The portion of the variance caused by a dominant allele at a

given locus is

σ2Di = (2q i(1− q i )d i)2 (6)

where d i is the dominance effect in locus i Often, it is assumed

that d i= 0

In the case of genes with major effects on the trait being

studied, the analysis is slightly easier because animals carrying a

desirable allele frequently exceed the normal variable range for the

measured production trait This is obvious from the distribution

function of the estimated BV of the evaluated trait (additional

peaks, outliers), which indicates that a special genetic effect is

occurring and should be included as a separate factor in the

model.37

Several loci

Correlations may exist between the genes in question Therefore,

the variability of an observed trait that is explained by several genes

depends on the variability caused by each gene and combinations

thereof (λ).38The additive covariance between two loci can be

expressed as

cov(Ai, Ai)= (1 − 2λ)2(σ Ai σ Ai) (7)

The theory of selection indices is used to determine the shares of

several loci in the total genotype.39,40

Genes interact with one another, and any gene may have

pleiotropic effects These interactions are mostly unknown and

may be quite extensive This implies genetic epistatic variability

(σ2

I) based on two or more interactions among all loci studied

It is expected that multi-generational, similarly oriented ongoing

selection in commercial breeds will lead to the stabilisation of

favourable genetic combinations The fixation of desirable alleles

could also occur at a number of loci in an improved breed However,

breeding conditions change constantly, and combinations of

genes are disturbed by selection, mutation and by the immigration

of sires from other populations Therefore, inter-gene interactions

within some families may be expressed differently for a certain

period before the gene linkages are again stabilised This can be

exploited in selection

When studying the influence of a selected gene on performance,

the effects of nearby (linked) genes will also be included; thus the

result does not correspond only to the studied gene or to the

studied marker–QTL relationship Therefore, when a low number

of sparsely located markers is analysed, the effects of any givenmarker are frequently overestimated.33The effects calculated foreach genetic parameter strongly depend on the number anddensity of genetic parameters included in a simultaneous analysis.One locus can also have an epistatic effect on several traits, whichmay be either positively or negatively correlated It is thereforenecessary to distinguish between pleiotropic and closely linkedQTL effects.41

Polygenic effects: the ‘infinitesimal model’ (pol)

A large number of unknown genes are assumed to affect themajority of production traits, and their overall influence onperformance and its variability is the object of interest In general,the components of variance are currently determined as in Eqn (1),

by REML methods or by applying the Bayesian approach usingthe Gibbs sampling method.12These methods require the analysisand adjustment of input datasets so that specific components ofvariance (for example, within families, between families, caused

by different effects of genes) can be estimated.19,21

Joint effects of particular loci with remaining polygenes

The overall influence of genetic effects on the observed production

trait is expressed by the coefficient of heritability (h2= σ2

A/σ2 ).The specific roles of the genes which exert these effects generallyremain unknown

The total additive genetic variability is the sum of the knownloci according to Eqn (5), adjusted for mutual linkages (7) and

‘residual’ additive genetic variability caused by the remaining

EXPERIMENTAL DESIGN FOR THE EVALUATION

OF GENETIC MARKERS

The objective is to estimate the genetic contribution to specificproduction traits However, the experimental design should ensurethe reliable estimation of all factors that influence performance.The power of the evaluation of data depends on the structureand the size of the experiment, and the minimum number ofobservations required to achieve adequate predictive power can

be calculated.46Large datasets spanning progeny from many siresare usually necessary.2,3,30,43Generally, thousands of animals areincluded in any one experiment

Laboratory analyses are expensive, and therefore the decision

of which animals from which generation should be genotypedshould be made carefully, to achieve the highest possible reliabilitywith the lowest possible cost Several methods based on therelationship matrices between animals have been developed forthis purpose.49

Both the screening of allele frequencies and the evaluation oftheir relationship to production traits require a pedigree analysis.Sires, especially those imported from other populations for artificialinsemination, can dramatically change the frequencies of alleles

in a herd or an entire population in a short period of time

There are differences in the methodologies used to evaluateF1/F2 generation-designed experiments in which extremely

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different breeds are crossed38,50 and studies involving stably

selected commercial populations, where alleles are expected to

be in favourable interactions The second case, which is connected

with the continuous improvement of already productive breeds,

is generally of greater interest to breeders

DD + GDD

Sib animals, belonging to the same families, generally have similar

performance capabilities They share both the observed genes

and background polygenes It is crucial to determine whether

performance is influenced by the studied locus or by other

polygenes

Study designs that incorporate data from multiple generations

have been developed for the analysis of small numbers of markers

These daughter design (DD) and granddaughter design (GDD)

analyses allow estimation of the effects of the studied loci within

families, i.e., within groups of sib animals with a similar genetic

background.38,51In this type of analysis, the initial generations of

sires (i.e., parents or grandparents) must be heterozygous at the

studied locus In this way, each initial animal gives rise to two

genetically different groups of progeny with respect to the alleles

studied

In the proposed GDD experiment, only generations of ancestors

without their own performance measurements can be genotyped

Their performance scores are assigned by means of progeny

testing from a large set of non-genotyped progeny This

considerably decreases the number of individuals that must be

genotyped despite achieving a high reliability of evaluation The

total number of animals required for the experiment is relative

to the proportion of genetic variability influenced by the locus,

allele frequencies, and the level of recombination between QTL

and the marker However, only the additive effects of genes can

be estimated in this design The GDD and general pedigree design

analysis of QTL in dairy cattle have been compared in simulation

studies.52

Design with a large number of markers (SNP)

An increased number of markers introduces more complexity The

size of the reference population of sires with highly reliable BV

estimates is particularly important.2,3,43Larger numbers are better,

and several thousands of sires are desirable

A MODEL FOR ESTIMATING GENETIC EFFECTS

The principle of evaluation consists in the separation of the

effects of purely heritable loci from the effects of other genetic

background.43,47,48,53 The BLUP method and similar approaches

are the best procedures that can be used to adjust measured

BV values Consistent with Eqns (1) and (2), the evaluation can be

formally expressed by a modified mixed linear model:

where T is the known matrix of the experiment design that links

an animal to the genetic effects of particular alleles Each row

may include columns according to particular loci and several

genetic effects of each locus with values for additive effects

(t Ai¤ <1, 0, −1 >), dominance effects (t Di¤ <0, 1 >),

two-locus epistatic interactions between loci (t Iii = t Ai t Ai);32u∗is the

estimated vector of the random ‘residual’ polygenic effects for

each animal (i.e., the partial BV after the effects of the studied loci

are excluded) with the additive relationship matrix; and v is the

estimated vector of the effects of genetic markers This may alsocomprise several loci and encompass additive, dominance andepistatic effects

Genetic markers (v) can be considered to have fixed32,53 orrandom effects In the latter case, either the diagonal genetic

matrix alone, Iσ2

Ai, is considered54for each random effect (i), or

the complete covariance structure and its relationship with the

identity by descent matrix (IBD), IBDσ2

Ai, is taken into account.IBD describes the probable positional relationships between eachmarker/QTL pair and the probability of inheriting the paternal

or maternal QTL allele The construction of IBD depends onwhether linkage analysis (LA), linkage disequilibrium (LD) or acombination of both methods (LDLA) was used to determinelinkage status.8 In this context, several teams have developedalgorithms for the construction of an IBD matrix.41,44,55They havealso derived genotype values for non-genotyped sib animalswhose performance data may be then used to identify candidategenes

DATA FOR EVALUATION

Several types of data describing performance can be used forthese evaluations Either direct performance records or adjustedvalues may be used For the second approach, data are adjustedfor non-genetic noise as precisely as possible, when BV with highreliability is estimated in large populations This yields adjusted(pseudo) values for BV, yield deviation (YD) or daughter yielddeviation (DYD) that may be used in further analyses

Direct individual performance

If genetic parameters (markers) are determined directly in animals

from their performance records, the evaluated trait (Y) according

to Eqn (9) is their recorded performance

Given the pedigree structure and design of the experiment, it

is possible to estimate additive, dominance and epistatic genetic

effects, all of which could be included in v However, in practice

relatively few performance values are known for each animal

Therefore the values of vectors u∗, v and other effects b inEqn (9) can be estimated only with considerable error.56,57

Breeding value

Animals with highly reliable BV are used for evaluation (usuallysires whose value has been proven by progeny testing) In BVanalysis, the effects of selected loci on major traits associated withmilk performance are determined58 – 60according to the followingmodel:

where ˆu is the vector of BV determined by a routine method

BLUP-AM according to Eqn (1) based on all polygenes

The BV of an animal summarises the data on performancedeviations of the contemporaries of all sib animals The expected

BV of the progeny (uO) is related to the BV of sires (uS) and mothers

(uM) and to the random Mendelian sampling of parental gametes(MS)

One half of the additive genetic variability of (uO) is caused by

MS Therefore the result of Eqn (10) significantly depends on thevolume and sources of information that contributed to the BV Areliable input BV, which can be achieved only for animals with a

large set of progeny, is a condition for a correct evaluation This,

however, implies that it is possible to evaluate only the additive

genetic component

We must take into account the fact that BV represents a

random effect (regressed value) and its value directly depends

on the reliability of estimation (r2) The variability of BV (σ2 ) is

therefore higher at higher reliabilities, as shown by the following

relationship:

This demonstrates that for BV estimates with low, unbalanced

reliabilities the animal rank may change and the results of markers

analysis are not very reliable

Daughter yield deviations

DYD computed from Eqn (1) are used in most routine evaluations

of markers.45 Initially, yield deviations (YD) adjusted for all

non-genetic effects are determined according to the following

equation:61

where YD is the vector of yield deviations The average deviations

of sires’ daughters (DYD) is then determined and adjusted for 0.5

BV of their mothers:

DYD= Z

S[YD− 0.5Z

where ZSis the known matrix that relates daughter performance to

the sire; ZMis the known matrix that relates daughter performance

to the mother; uMis the vector of mothers’ BV; and N is the diagonal

matrix that describes the number of daughters per sire

The values of DYD are independent of the reliability of sires BV

estimates, and therefore are more comparable between sires with

different reliabilities of BV estimation In agreement with Eqn (11),

DYD comprise 0.5 BV of a sire, including MS and random error The

alternative of DYD is de-regressed BV.3

Performances adjusted in this way are evaluated by weighted

analysis according to (9), where the vector Y is substituted by the

vector DYD, and vector b may encompass additional fixed effects.

DYD values are the means for n daughters of sires Taking

into account the number of contemporaries connected to each

daughter in DYD, and the structure of the entire dataset, we

can generate the effective daughter contribution (EDC), which is

determined based on the reliabilities of the estimation of sires’ BVs

(r2):

EDC= (r2

/(1 − r2))((4− h2)/h2) (15)

The weight (w) for weighted analysis, which is the inverse of DYD

variance, corresponds to the value of EDC The exact derivation

of the weight factor for special situations has been described

previously.61,62

DYD has been used in several GDD studies; one evaluated 39

markers in a set of 4993 sires and another evaluated 263 markers

in a set of 872 sires.7,63

As in the evaluation of BV, only the additive genetic component

can be determined by DYD If the number of progeny per sire is

large then they prevail in his BV, r2is high and balanced, and the

sire’s MS is almost completely contained both in BV and in DYD

The correlation between BV and DYD is in this case high, and the

results of evaluation for genetic markers on the basis of BV and

DYD are similar.32

METHODS FOR EVALUATING GENETIC MARKERS

When only a small number of genes is studied, it is notpossible to evaluate the experiment correctly without splittingthe genotype influence into the part played by singular observedgenes and the part played by the other (residual) polygenic

‘genetic background’.7,33,64,65On the other hand, single observedgenes also contribute to the additive effects of all genes, and

the polygenic effect (u) is the sum of these additive effects.

Therefore, it is not easy to distinguish between the influence ofthe polygenic ‘genetic background’ and the effects of individualgenes; in these cases the effect of the individual observed genes

is frequently reduced.66 Therefore careful experimental design,particularly with respect to the size of the experiment, is necessary

to estimate the effects of genetic markers

Several connected questions must be asked in any evaluation ofgenetic markers: (A) What proportion of the genetic variability ofthe evaluated trait is explained by the studied genetic factors?(B) What is the genetic correlation between the influence ofthe factors studied and the influence of the ‘remaining’ geneticbackground on the evaluated trait? (C) Do the results from a modelthat considers only polygenic effects and a model that includesboth QTL and remaining polygenic effects differ from each other?(D) What is the influence of each allele? (Note that it does notmake sense to answer (D) without first answering (A).) (E) Do thestudied genetic factors have similar effects in all groups of relatedanimals?

A small number of QTLs

When a small number of loci are evaluated, QTLs are often used

to represent fixed effects of the genotype in a linear model,for example in GLM/SAS.67 – 69The evaluation model also reflectssystematic effects of the breeding environment or of groups ofanimals according to their relationships With this method, it isnot possible to wholly avoid the influence of correlated loci, andthe effects of individual loci are therefore usually significantlyoverestimated.33 The model can be improved by including aparameter for the random effects of the parents of genotypedanimals.56,57

The effect of the studied locus depends on the genetic ground of the animal and could differ between populations.43,65The BLUP, REML and Bayesian analysis methods incorporate com-mon fixed effects for particular loci and ‘residual’ random effects ofremaining polygenes to provide more exact results.7,43,45Anotherapproach for obtaining more exact results is also to use particularloci as random effects with IBD to account for their variability.8,55

back-A large number of SNPs

Production traits depend on a large number of mutually linked,interacting genes that may be distributed across the entiregenome Currently, it is possible to sequence tens to hundreds ofthousands of single-nucleotide polymorphisms (SNPs) for manyindividual animals, densely covering the entire genome A multipleregression analysis of all SNP markers describes their relationships

to the production trait in question Thus this analysis can be used

to find the DGV and GEBV according to Eqns (3) and (2) Because

a large number of SNPs is considered, there is less emphasis onthe quantitative relationships between individual markers and therelevant QTL; instead, the overall relationship to the productiontrait in question is important

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The high density of markers also allows the generalisation of

effects Relationships no longer need to be calculated individually

within particular families and the effects of alleles are assumed to

be consistent across all families for simplification.33

While the breeding values of young dairy cattle can be predicted

with a reliability of about 30% by pedigree value on the basis of

polygenes, an increase in reliability (to 50–70%) can be expected

when large number of SNPs are evaluated.2,3,33

Computational strategies used to evaluate SNP data

Generally, techniques based on the BLUP and BayesB methods

are used to evaluate large numbers of SNPs.33 Depending on

the total number of SNPs sequenced, it is usually necessary to

calculate many genetic regression relationships between a given

production trait and the studied alleles.54,70These relationships

may be formally solved according to Eqn (9) Compared to a

general AM calculated on the basis of polygenes only, the size

of the vector DYD is relatively smaller (thousands of sires) but

the size of the vector v is large (tens of thousands of regression

coefficients) When there is a high density of SNPs across the

entire genome, the term u∗is often omitted from the solution

and only SNPs are used (vector v).43 In practice, however, it is

expected that even when a high density of markers is obtained

some QTLs will not be covered and the polygene effect is therefore

still considered.3 Only additive effects are evaluated due to the

large number of SNPs; the inclusion of non-additive effects would

increase the number of effects in the model enormously After

simplification, the computation model can be expressed as

where Xb describes the total mean and fixed effects included in

this step

Often, the majority of SNPs do not have any information content

If the relationships between the markers and QTLs are already

known, it is possible to reduce the number of regressors in the

model, which will simplify the solution and also reduce the cost of

laboratory analyses.71

Because of the large number of independent variables, these

systems of equations (16) are poorly conditioned and cannot

always be solved Therefore, the systems of equations and

algorithms of solutions must be rearranged For example, ridge

regression may be applied, which means that SNPs are treated as

random effects At the same time, numerical values are added to

the diagonal of the matrix of the system to ensure the solubility of

the equations.34The added values are the inverse of the genetic

variabilities of each SNP These values are not usually known for

many genetic parameters, so other simplifications must be used

when constant components of variance are required for all SNPs.33

The sum of components across all loci yields the total

additive-genetic variability of the studied trait σ2

A Matrix T in Eqn (16) has f rows corresponding to the number of evaluated sires with

known daughter performance If only additive gene effects are

considered, matrix T has m columns corresponding to the number

of SNP markers considered (m > f ) Therefore, a system of the

matrix size m × m at least is solved, and tens of thousands of

regression coefficients are estimated.54

DIRECT ESTIMATION OF GEBV

Based on T, a genomic relationships matrix (G) can be

determined.72,73 This requires the introduction of the matrix Q,

which describes deviations of allelic frequencies in the basic

‘non-selected’ population The ith column of Q contains the deviation

of the frequency of the second allele in locus i from the expected value (0.5) multiplied by two Q i = 2(q i − 0.5).72The dimensions

of matrix Q correspond to those of matrix T The matrix G has the

form

G = ([T − Q][T − Q])/(2
q i(1− q i)) (17)which is analogous to the generally used numerator relationship

matrix (A) in Eqns (1) and (9) Its dimensions are f × f, where

the diagonal indicates the number of homozygous loci in theevaluated animal and the elements off the diagonal indicate thenumbers of alleles shared by sib animals

The diagonal residual covariance matrix Rσ2 of dimensions

f × f is then constructed This matrix corresponds to the residual effect (e) in Eqn (16) Relative to Eqn (15), the elements on diagonal

R are connected with the reliabilities of BV estimates for particular

sires, but only on the basis of their progeny from which DYD werecomputed (excluding other sources of information):

Based on the theory of selection indices, it is then possible

to determine the direct genetic value of sires with knowndaughter performance (DGVS)72by adapting 2DYD for the vector

of observations:42

where k = σ2 /σ2A.The genomic covariance matrix between proven sires (S) andyoung unevaluated animals (O) is

C = ([TO− QO][TS− QS])/(2
q

where TOand TSare the known matrices assigning particular loci

to the young animals and proven sires and QO and QS are Q

matrices that have been modified according to the number ofyoung animals and proven sires included

The predicted direct genetic value for young animals (DGVO) is agenomic regression based on proven animals with already knownBV:

DGVO= CG−1DGV

The solution by means of the selection index according toEqns (17)–(21) is identical to the preceding solutions in Eqns (16)and (3) but the dimensions of the matrices are substantially smaller,

corresponding to the numbers of genotyped animals (f ).72,73Theestimation of genetic regression coefficients according to the

particular loci (v) may also be omitted Hence the solution is

simplified and does not require iterative methods.72 Therefore,

a direct determination of DGV estimate reliability is feasible Forsires with known daughter performance (S), reliability estimatescorrespond to the diagonal elements of the term:

For young animals without known performance (O), reliabilityestimates correspond to the diagonal elements of the term

A similar solution can also be obtained by the weighted analysis

of a linear model as in Eqn (1) with DYD substituted for input data

and weighted according to Eqn (18) In this case, A is substituted

by G and Xb covers only the general mean.72,73

A one-step approach

The process of evaluation described above has several

disadvan-tages, namely that it is influenced by the input parameters used

in a multi-step procedure Inaccuracies in these parameters may

bias the evaluation It is also difficult to compare genotyped and

ungenotyped animals evaluated by different procedures This may

be overcome by incorporating all parts of the evaluation into a

one-step procedure

From Eqn (19), it follows that molecular-genetic information

is collected in G The additive numerator relationship matrix

(A) is probability based and deviates from expected values

due to random Mendelian sampling.74 The realised genomic

relationship matrix (G) should therefore be more precise and

lead to more precise selection.73A single-step evaluation using

original measured performances (Y) as input has been proposed,

in which the pedigree-based numerator relationship matrix (A)

covering all evaluated animals is augmented by a contribution

from (G) with genotyped animals.75

A matrix H has been derived, which is substituted for the

usual matrix (A) in Eqn (1).76Further, a computational procedure

has been developed for the solution of animal models directly

from the accumulated measured data of all genotyped and

non-genotyped animals in large commercial populations.6,75The

essential component of the system of equations constructed

according to Eqn (1) is the inverse of the relationship matrix, in

where H is the pedigree–genomic relationship matrix, λ is a scaling

factor and A22is a block of A that corresponds to the genotyped

animals

This one-step procedure eliminates several assumptions that

must be made for multi-step procedures It is less biased and allows

the evaluation of large commercial populations even when only

some individuals in the population are genotyped This improves

evaluation accuracy both for genotyped and ungenotyped animals

and generates a single common rank for all animals This model

further enables the use of multi-trait AM and models with different

complexities, which are now common in animal evaluations.6

CONCLUSIONS

The majority of traits are conditioned in a complex way; it can

be assumed that a production trait will only rarely be genetically

conditioned in a simple way by a small number of independent

genes In practice, a large number of markers and a large number

of animals with accurate performance estimates are necessary for

the reliable evaluation of animals.2,3

Simplifications are often used in evaluations, but at the cost

of lower reliability of results A description of the variability

of the evaluated data and the validity of the model are

therefore necessary Considerable attention should be paid to

the development of ‘traditional’ methods of BV estimation on

the basis of polygenes, which enables the correct adjustment of

performance for environmental effects

Typically, a two-step evaluation of genotyped animals is

performed The first step is the estimation of ‘traditional’ BV

for recorded traits according to BLUP-AM or a similar method

Based on these results, DYD are computed for particular sires with

a highly reliable BV In the second step, GEBV is determined foryoung animals without performance records

Marker-assisted selection is gradually being replaced bygenomic selection, in which a huge number of genetic markers(SNPs) that densely cover the entire genome are analysed Inthis case, polygenic effects can be eliminated from the solutionwithout strongly affecting the results

Molecular-genetic information can be gathered in the genomic

relationship matrix G in order to estimate DGV and GEBV with

computationally simpler procedures

A one-step procedure that combines the animal model with apedigree–genomic relationship matrix can be used to evaluate allanimals in a population This protocol is useful as it produces moreaccurate evaluations than do other methods and also generates acommon rank for all genotyped and ungenotyped animals in thepopulation

The methods described here have significant practical tance in animal breeding

impor-ACKNOWLEDGEMENTS

This work was supported by the Ministry of Agriculture of the CzechRepublic (MZe 0002701404) and by the Ministry of Education ofthe Czech Republic (MSM6007665806) We gratefully acknowledgethe helpful comments of anonymous reviewers

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Received: 26 May 2009 Revised: 25 March 2010 Accepted: 29 March 2010 Published online in Wiley Interscience: 22 June 2010(www.interscience.wiley.com) DOI 10.1002/jsfa.3998

Root colonisation by the arbuscular

mycorrhizal fungus Glomus intraradices alters

ananassa Duch.) at different nitrogen levels

Abstract

BACKGROUND: Arbuscular mycorrhizal fungi (AMF) increase the uptake of minerals from the soil, thus improving the growth

of the host plant Nitrogen (N) is a main mineral element for plant growth, as it is an essential component of numerous plant compounds affecting fruit quality The availability of N to plants also affects the AMF–plant interaction, which suggests that the quality of fruits could be affected by both factors The objective of this study was to evaluate the influence of three N treatments (3, 6 and 18 mmol L −1) in combination with inoculation with the AMF Glomus intraradices on the quality of strawberry fruits.The effects of each factor and their interaction were analysed.

RESULTS: Nitrogen treatment significantly modified the concentrations of minerals and some phenolic compounds, while mycorrhization significantly affected some colour parameters and the concentrations of most phenolic compounds Significant differences between fruits of mycorrhizal and non-mycorrhizal plants were found for the majority of phenolic compounds and for some minerals in plants treated with 6 mmol L −1 N The respective values of fruits of mycorrhizal plants were higher.

CONCLUSION: Nitrogen application modified the effect of mycorrhization on strawberry fruit quality.

c

2010 Society of Chemical Industry

Keywords: mycorrhizal; nitrogen; strawberry; quality; fruit

INTRODUCTION

Most land plants benefit from their interaction with symbiotic

soil-borne fungi known as arbuscular mycorrhizal fungi (AMF)

In this symbiosis the AMF receives carbon from the plant, while

the fungus takes up nutrients with its extraradical mycelium and

provides them to the host plant.1

The uptake of nitrogen (N) by the extraradical mycelium has

been shown before and this N is available to the host plant,2,3

so the AMF improves the N status of the host.4,5Nevertheless, it

also has been reported that the N availability in the soil affects the

dynamic of plant–AMF association.4,6

Nitrogen is an essential element for plant growth Owing

to its role in the synthesis of proteins, nucleic acids, various

coenzymes and many products of secondary plant metabolism,7

it is important for strawberry fruit quality It has been shown

that a leaf N concentration below 19 g kg−1(deficiency) causes

chlorosis of strawberry leaves, thus decreasing the leaf area, fruit

size and anthocyanin concentration,8whereas an excess of foliar N

(∼40 g kg−1) promotes vegetative growth, delays fruit maturation

and causes a loss of firmness in fruits, thus reducing quality.9,10

Strawberry quality and consumer preference for strawberry

fruits are determined by parameters such as size, firmness, levels

of soluble sugars and acid concentration, the last of which affects

the aromatic compounds that impart flavour and aroma.11,12

Strawberry fruits possess antioxidant activity owing to their highcontent of anthocyanins, flavonoids, phenolic acids and othercompounds.13

Recent data suggest that mycorrhization not only has a positiveeffect on various plant growth parameters but can also affectthe quality of crop products For example, root colonisation bydifferent AMF enhances the essential oil concentration in a number

of plants from different plant families such as oregano (Origanum vulgare),14basil (Ocimum basilicum L.),15,16menthol mint (Mentha

∗ Correspondence to: Vilma Castellanos-Morales, Estaci´ on Experimental del

Zaid´ın, Prof Albareda 1, Apdo 419, E-18008 Granada, Spain.

E-mail: vilma c 99@yahoo.com

a Instituto de Investigaciones Agropecuarias y Forestales, Universidad

Michoacana de San Nicol´ as de Hidalgo, Km 9.5, Carretera Morelia-Zinap´ecuaro,

CP 58880, Tar´ımbaro, Michoac´ an, Mexico

b Instituto de Investigaciones Qu´ımico-Biol´ ogicas, Universidad Michoacana de San Nicol´ as de Hidalgo, CP 58000, Ciudad Universitaria, Morelia, Michoac´ an, Mexico

c Federal College and Research Institute for Viticulture and Pomology,

Wienerstrasse 74, A-3400 Klosterneuburg, Austria

d Estaci´ on Experimental del Zaid´ın (CSIC), E-18008 Granada, Spain

Effects of AMF and N level on quality of strawberry fruits www.soci.org

arvensis)17and coriander (Coriandrumsativum L.).18In others plants

such as alfalfa (Medicago sativa L.),19 – 21 barrel medic (Medicago

truncatula),22red clover (Trifolium pratense)23and soybean (Glycine

max L.),24increases in flavonoid levels after mycorrhization have

been reported

There are several reports on strawberry plants concerning

inoculation with AMF and its effects on plant growth It has been

shown that AMF root colonisation stimulates plant growth,25

modifies the production of runners,26enhances photosynthesis27

and increases the number of fruits.28 However, to the best

of our knowledge, there are currently no data on how AMF

root colonisation in combination with different N levels affects

strawberry fruit quality parameters such as colour, soluble sugars,

acids, minerals and phenolic compounds

MATERIALS AND METHODS

The experiment was conducted in a ‘shade’-type greenhouse

with 30% shade at the Instituto de Investigaciones Agropecuarias

y Forestales (IIAF), Universidad Michoacana de San Nicol ´as de

Hidalgo (UMSNH), Morelia, Michoac ´an, Mexico Maximum and

minimum temperatures in the greenhouse varied between 28 and

32◦C and between 8 and 18◦C respectively.

Plants of the strawberry cultivar ‘Aromas’ were used that had

previously been grown in a sterilised (95◦C water/steam, 40 min)

substrate of coconut fibre/perlite (1 : 3 v/v) under greenhouse

conditions Before the experiment was established, the absence of

AMF in the roots was verified by the ink and vinegar technique,29

modifying the duration of immersion in KOH and ink/vinegar

solution (7 and 5 min respectively) Before planting, roots were

disinfected by submerging them for 20 s in 20 g L−1 sodium

hypochlorite solution and rinsing them in water

The inoculum was prepared with spores of Glomus intraradices

cultivated in liquid medium (3.5× 106spores L−1, 90% viability;

Premier Tech Biotechnologies Company, Quebec, Canada), which

was diluted with fitagel (Sigma P-8169, Saint Louis, MO, USA)

solution at 50 g L−1to obtain a final concentration of about 5×104

spores L−1 The viability of spores was determined according to

the method of An and Hendrix.30

Eighteen days after setting up the experiment, each plant

received 2 mL of inoculum applied directly to the recently formed

roots One month later, after staining,29the percentage of root

colonisation was determined by the gridline intersect method.31

The experiment was organised as a full factorial, completely

randomised design with two factors: inoculation (two levels:

mycorrhizal and non-mycorrhizal plants) and N concentration

in the nutrient solution (three levels: 3, 6 and 18 mmol L−1).

The six treatments were replicated four times, producing 24

ex-perimental units with ten plants each Every second day, all plants

were irrigated up to substrate saturation Nitrogen was supplied

Mg2+, 1.5 mmol L−1 They were increased in the 18 mmol L−1N

treatment: K+, 6.5; Ca2 +, 7.5; Mg2 +, 3.25 mmol L−1 In all nutrient

solutions the concentration of phosphorus (P) was 0.3 mmol L−1.

The other nutrients in the solutions were: H3BO3, 20; CuSO4·5H2O,

0.5; Fe-EDTA (Ethylenediaminetetraacetic acid iron (III) sodium

salt), 15; MnSO4·H2O, 12; (NH4)6Mo7O24·4H2O, 0.05; ZnSO4·7H2O,

3µmol L−1 The pH was adjusted to 5.5 at every application date.

Mature fruits of each experimental unit were collected between

140 and 160 days after setting up the experiment At sampling

time the fruits were separated into two equal batches One batchwas used for the determination of fruit fresh weight, diameter,length and Brix grade (total solids) The last measurement wasdone at 25◦C using a refractometer (ATAGO CO., LTD) (N-1α) Theother batch was frozen in liquid nitrogen and stored at−20◦C.Prior to chemical and colour analyses, these samples were ground

to a fine powder (Retsch MM200 mill, Thomas Scientific, NewJersey, United States) in liquid N2and then freeze-dried

Titratable acidity is a measure of organic acids in a sample and

is determined by adding enough alkali of known molarity to thesample to neutralise all acids present For the measurement oftitratable acidity, 0.1 g of freeze-dried fruit was mixed with 5 mL

of distilled water and shaken, then 0.05 mol L−1NaOH was added

up to a pH of 8.1 The results are expressed as % citric acid

For macro- and micro-nutrient determination, 10 mL of distilledwater was added to 0.2 g of ground sample The mixture wassonicated (FS30H, Fisher Scientific, Pittsburgh, United State)and then centrifuged (2744× g, 10 min) The supernatant was

filtered through a 0.45µm membrane (Millipore, Thebarton, SouthAustralia) For macronutrient measurement, 9 mL of 0.5 mol L−1HCl and 200µL of lanthanum oxide were added to 1 mL of thefiltrate For micronutrient determination, 200µL of concentratedHCl was added to 9 mL of the filtrate All samples were shaken on

a vortex for 5 min and their mineral contents were quantifed

by atomic absorption (Solar 939, ATI Unicam, Basingstoke,U.K)

Soluble sugars were extracted by the method of Gomez

et al.,32 with some modifications All extractions were carriedout at 4◦C Briefly, 4 mL of methanol/water (1 : 1 v/v) and 1 mL

of chloroform were added to 15 mg of lyophilised sample Themixture was shaken on a vortex for 2 min and then on a horizontalagitator (Libline 4638, Melrose Park, Illinois) at medium speedfor 30 min After centrifugation (1585× g, 30 min), two liquidphases separated by the plant powder were obtained A 2.8 mLvolume of the methanol/water supernatant was recovered anddried in a vacuum evaporator (Labconco 7810000 Speed-Vac,Kansas City, Missouri) The resulting pellet was stored at−20◦Covernight Next day it was redissolved in 2 mL of distilled water

by shaking on a vortex for 20 min The aqueous extract was thenpoured into a tube with 0.015 g of polyvinylpyrrolidone (SigmaP6755) to remove residual phenols by crosslinking After shaking

on a vortex for 20 min, the tube was centrifuged (1585× g,

90 min) The supernatant was recovered using a 1 mL insulinsyringe and stored at −20◦C for the direct measurement ofglucose and the indirect measurement of fructose and sucrose

by the enzymatic method33 with a photometer (MultiskanAscent 354, Thermolabsystem, Finlandia imported by Labtech,Mexico) at 340 nm, using a calibration curve in the range0–0.2 g L−1 glucose (Baker 1916-01, Xalostoc, Edo M ´exico) Toverify the correct measurement of soluble sugars, controls offructose (Sigma F0127) and sucrose (Sigma S7903) were used.Before measurement the extract was diluted with distilled water(1 : 20 v/v)

Total phenols and the anthocyanins cyanidin-3-glucoside andpelargonidin-3-glucoside were extracted by the method ofMarkakis,34with some modifications Briefly, 5 mL of methanol/HCl(1 : 5 v/v) was added to 0.1 g of lyophilised sample The mixturewas sonicated for 300 s and then centrifuged (2744× g, 10 min).

The supernatant was filtered (0.45µm membrane, Millipore) andthe filtrate obtained was used for the measurement of totalphenol and anthocyanin concentrations Colour parameters andthe absorbance at 500 nm were also measured in the same filtrate

J Sci Food Agric 2010; 90: 1774–1782
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa

Total phenol concentration was quantified by the Folin–

Ciocalteu method,35 with minor modifications The volumes of

sample, Folin–Ciocalteu’s phenol reagent and sodium carbonate

were reduced to one-tenth of those used in the original method,

giving a final volume of 20 mL The measurement was made

at 765 nm (Spectrophotometer – Cintra 10e, GBC, Dandenong,

Victoria, Australia), using a linear calibration curve of caffeic acid

(0–250 mg L−1) to calculate the total phenol concentration.

Strawberry fruit colour was determined by measuring the

absorbance at 500 nm36 with a spectrophotometer (Shanghai,

Analytical Instrument LTD, China) (HP-8452A, Cheadle Heath,

Stockport Cheshire, UK) Additionally, colour was measured using

a photometer (Licor-2000, DR Lange, Dusseldorf, Germany) in

terms of L∗, a∗and b∗values, where L∗defines lightness (from

white= 100 to black = 0), a∗defines red/greenness (from−60

to+60) and b∗defines blue/yellowness (from−60 to +60) From

the a∗and b∗values the following colour parameters were also

calculated: colour evolution (a∗/b∗), shade (tan−1(b∗/a∗), ranging

from 0◦ (red) to 90◦ (yellow) to 270◦ (blue)) and chromaticity

(C∗= (a2+ b2)1/2, indicating the vividness of colour and ranging

from 0 (discoloured) to 60 (powerful))

Phenolic acids and flavonols were extracted by acid hydrolysis.37

Briefly, 7.5 mL of 5.33 g L−1 ascorbic acid solution, 12.5 mL of

methanol (liquid chromatography/mass spectrometry grade) and

5 mL of 6 mol L−1HCl were added to 0.25 g of sample The mixture

was sonicated for 2 min, the air in the mixture was replaced with

gaseous N2(1–1.5 min) and the mixture was shaken on a horizontal

agitator (35◦C) for 16 h The cold sample was filtered (0.45µm

membrane, Millipore), concentrated in a rotavapor (35◦C) and

redissolved in 1 mL of methanol This solution was filtered (0.45µm

membrane, Millipore) and 10µL of the filtrate was used for the

measurement of phenolic acids and flavonols

Phenolic compounds (anthocyanins, phenolic acids and

flavonols) were quantified by reverse phase high-performance

liquid chromatography (RP-HPLC)38using an Agilent 1090

Amino-quant HPLC system (Waldbrot, Germany) Each 10µL sample was

injected for separation on two narrow-bore HP-ODS Hypersil

RP-18 columns (Shandon, U.K) (5µm, 200 mm × 2.1 mm and 5 µm,

100 mm× 2.1 mm) linked in series A linear gradient of 5 g L−1

formic acid (pH 2.3) and methanol at a flow rate of 0.2 mL min−1

was used The column temperature was 40◦C and detection was

achieved at 320 nm for all compounds The standards used and the

concentration ranges of their calibration curves were as follows:

callistephin (Extrasynthese 0907S, Lyon, France), 1–200 mg L−1;

kuromanin (Extrasynthese 0915S), 1–200 mg L−1; gallic acid

monohydrate (Roth 7300, Karlsruhe, Germany), 10.9–545 mg L−1;

p-coumaric acid (Roth 9908), 26.2–1308 mg L−1p-coumaric acid;

ferulic acid (Roth 9936), 9.8–490 mg L−1; ellagic acid (Sigma

E2250), 8.1–81.4 mg L−1; quercetin dehydrate (Extrasynthese

1135S), 8.7–436 mg L−1; kaempferol (Fluka 60010, Saint Louis,

MO, USA), 8.5–426 mg L−1; catechin (Roth 6200), 9.2–460 mg L−1.

The results are presented as means of four replicates (each

replicate consists of fruits from all plants in one experimental

unit) Statistical analyses were performed using SYSTAT for

Windows, Version 9.01 (Systat Software versi ´on 9.01, Cranes

Software International, LTD) The effects of each single factor

(N concentration and inoculation) and their interaction (N

concentration × inoculation) were evaluated using two-way

analysis of variance (ANOVA) Multiple comparisons were made

using Tukey’s test Differences at P < 0.05 were considered

significant

plants inoculated with Glomus intraradices and fertilised with different

nitrogen concentrations in irrigation water Factor Fresh weight (g per fruit) Diameter (cm) Length (cm) Nitrogen concentration (mmol L−1)

Inoculation

Interaction (nitrogen concentration × inoculation)

Each value represents the mean of four replicates Two-way ANOVA was applied for each parameter; when statistical differences were found,

a Tukey test (P < 0.05) was conducted independently for nitrogen

concentration (3, 6 and 18 mmol L−1), inoculation (mycorrhizal (M) and non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 ×

M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM) For each factor, means with the same letter in a column do not differ significantly.

RESULTS

Tables 1–5 show the results for the effects of the two factors andtheir interaction on the variables evaluated At the end of theexperiment the extent of AMF colonisation ranged from 65 to80% None of the treatments affected the fresh weight, diameterand length of fruits (Table 1)

In terms of colour, different N concentrations resulted in tistically significant effects only on fruit lightness and absorbance

sta-at 500 nm (Table 2) Lightness was significantly higher and sorbance was significantly lower in fruits of plants fertilised with

ab-3 mmol L−1 N than in fruits of plants treated with 6 mmol L−1

N, but both values did not differ from those in fruits of plantsfertilised with 18 mmol L−1 N Mycorrhization resulted in statis-tically significant effects on all colour parameters except colourevolution and shade Fruits of mycorrhizal plants showed a 2.0%increase in lightness and 14.3, 12.9, 13.9 and 21.2% decreases

in red/greenness, blue/yellowness, chromaticity and absorbancerespectively compared with fruits of non-mycorrhizal plants In-creasing N concentration in the irrigation solution did not lead

to statistically significant differences in colour parameters tween fruits within each mycorrhizal treatment Nor were theresignificant differences between fruits of mycorrhizal and non-mycorrhizal plants fertilised with the same N concentration(Table 2)

be-Titratable acidity, glucose, fructose and Brix grade were lowest

in fruits of plants fertilised with 3 mmol L−1 N (Table 3) Theirtitratable acidity, glucose and fructose values were significantlylower than those in fruits of plants treated with 6 mmol L−1N,while their Brix grade was significantly lower than that in fruits ofplants treated with 18 mmol L−1N Mycorrhization modified onlyfructose concentration, with fruits of mycorrhizal plants containing8.5% less fructose than those of non-mycorrhizal plants When theapplied N was increased, a significant difference in titratable aciditybetween mycorrhizal and non-mycorrhizal plants treated with the

Effects of AMF and N level on quality of strawberry fruits www.soci.org

and absorbance at 500 nm of fruits of strawberry plants inoculated with Glomus intraradices and fertilised with different nitrogen concentrations in

irrigation water

Nitrogen concentration (mmol L−1)

Inoculation

Interaction (nitrogen concentration × inoculation)

Each value represents the mean of four replicates Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey

test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1), inoculation (mycorrhizal (M) and non-mycorrhizal

(NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM) For each factor, means with different letters

in a column differ significantly.

grade of fruits of strawberry plants inoculated with Glomus intraradices

and fertilised with different nitrogen concentrations in irrigation water

Titratable Soluble sugars (g kg−1DM)

acidity (%

Factor citric acid) Glucose Fructose Sucrose Brix grade

Nitrogen concentration (mmol L−1)

Inoculation

Interaction (nitrogen concentration × inoculation)

Each value represents the mean of four replicates Two-way ANOVA was

applied for each parameter; when statistical differences were found,

a Tukey test (P < 0.05) was conducted independently for nitrogen

concentration (3, 6 and 18 mmol L−1), inoculation (mycorrhizal (M) and

non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 ×

M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM) For each factor,

means with different letters in a column differ significantly.

same N concentration was observed only in the treatment with

3 mmol L−1N.

Some nutrient concentrations were significantly different

be-tween fruits of plants treated with 3 and 18 mmol L−1N (Table 4).

Fruits from the treatment with 3 mmol L−1N contained 9.4, 13.3,

61.0 and 48.0% more K, Mg, Fe and Zn respectively and 11.3% less

Ca than fruits of plants fertilised with 18 mmol L−1N The Mn centration in fruits of plants fertilised with 3 mmol L−1N was signif-icantly higher than that in fruits of plants treated with 6 mmol L−1

con-N Fruits of mycorrhizal plants had higher K and Cu concentrationsbut lower Mn concentration than fruits of non-mycorrhizal plants.Mycorrhization significantly modified the Ca, Mg, Fe, Cu, Zn and Mnconcentrations in fruits when the N applied was changed from 3

to 18 mmol L−1, and the K concentration in fruits when N changedfrom 3 to 6 mmol L−1 With the exception of Ca, the concentrations

of all elements studied were higher in fruits of plants fertilised with

3 mmol L−1N Significant differences between fruits of mycorrhizaland non-mycorrhizal plants of the same N treatment were foundfor Cu, Zn and Mn concentrations Fruits of mycorrhizal plants had38.0 and 39.3% more Cu and Zn respectively in the 6 mmol L−1

N treatment and 39.6% less Mn in the 18 mmol L−1N treatmentthan their non-mycorrhizal counterparts

Nitrogen treatment significantly affected the concentrations

of total phenols, gallic acid, ferulic acid, ellagic acid, 3-glucoside, quercetin and kaempferol in fruits (Table 5) Fruits

cyanidin-of plants fertilised with 3 mmol L−1N had 20.5, 31.2 and 11.4%lower concentrations of total phenols, gallic acid and cyanidin-3-glucoside respectively and 21.0, 50.0 and 61.5% higher con-centrations of ellagic acid, quercetin and kaempferol respectivelythan fruits of plants treated with 18 mmol L−1N Fruits of plantsfertilised with 6 mmol L−1 N had a significantly higher con-centration of ferulic acid than fruits of plants treated with 3and 18 mmol L−1 N Mycorrhization significantly modified theconcentrations of all phenolic compounds except pelargonidin-3-glucoside and catechin Fruits of mycorrhizal plants had 20.0,

15.0, 50.0 and 28.6% higher concentrations of p-coumaric acid,

cyanidin-3-glucoside, quercetin and kaempferol respectively and29.0, 50.0 and 11.0% lower concentrations of gallic acid, ferulicacid and ellagic acid respectively than fruits of non-mycorrhizalplants

Fruits of mycorrhizal plants fertilised with 6 mmol L−1N had

a lower gallic acid concentration than fruits of mycorrhizal plants

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nitrogen concentrations in irrigation water

Nitrogen concentration (mmol L−1)

Inoculation

Interaction (nitrogen concentration × inoculation)

Each value represents the mean of four replicates Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey

test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1), inoculation (mycorrhizal (M) and non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM) For each factor, means with different letters

in a column differ significantly.

different nitrogen concentrations in irrigation water

Phenolic compounds (mg kg−1DM)

Flavonoids

Factor Total phenols (g kg−1DM) Gallic p-Coumaric Ferulic Ellagic Cya-3-glu Pel-3-glu Quercetin Kaempferol Catechin Nitrogen concentration (mmol L−1)

Inoculation

Interaction (nitrogen concentration × inoculation)

Each value represents the mean of four replicates Two-way ANOVA was applied for each parameter; when statistical differences were found, a Tukey

test (P < 0.05) was conducted independently for nitrogen concentration (3, 6 and 18 mmol L−1), inoculation (mycorrhizal (M) and non-mycorrhizal (NM)) and nitrogen concentration × inoculation (3 × M, 3 × NM, 6 × M, 6 × NM, 18 × M and 18 × NM) For each factor, means with different letters

in a column differ significantly.

a Cya-3-glu, cyanidin-3-glucoside; Pel-3-glu, pelargonidin-3-glucoside.

treated with 18 mmol L−1N and a higher cyanidin-3-glucoside

concentration than fruits of mycorrhizal plants treated with

3 mmol L−1 N, the difference being significant in both cases.

Significant differences between fruits of mycorrhizal and

non-mycorrhizal plants of the same N treatment were found for

all phenolic compounds except pelargonidin-3-glucoside and

catechin Fruits of mycorrhizal plants had higher p-coumaric acid,

cyanidin-3-glucoside, quercetin and kaempferol concentrationsand lower gallic acid, ferulic acid and ellagic acid concentrationsthan fruits of non-mycorrhizal plants when fertilised with

Effects of AMF and N level on quality of strawberry fruits www.soci.org

6 mmol L−1 N, and a lower ellagic acid concentration when

fertilised with 3 mmol L−1N (Table 5).

DISCUSSION

In fruit production, parameters such as fruit fresh weight, diameter

and length are important for fruit quality In a study of the effect of

N application on peaches, Crisosto et al.39observed that different

N levels did not affect the size of peach fruits,39indicating that the

N levels tested were not a determining factor for this parameter

In our study, neither N treatment nor mycorrhization had an effect

on these parameters of strawberry fruits

Colour is another important determinant of fruit quality Shade

and chromaticity are two parameters used to quantify purity,

while red intensity is used for the description of colour.40,41The

values of these variables determined in the present study are

within the ranges observed previously in strawberry fruits.42 In

our experiment, some colour parameters were modified by N

treatment Fruits of plants fertilised with 6 mmol L−1N had lower

lightness and higher absorbance at 500 nm than fruits of plants

treated with 3 mmol L−1N.

Changes in chromaticity due to mycorrhization have been

re-ported previously in Capsicum annuum L by Mena-Violante et al.,43

who found that fruits of mycorrhizal plants had a lower

chromatic-ity than fruits of non-mycorrhizal plants Interestingly, our study

showed a similar effect of mycorrhization on chromaticity, with

a lower chromaticity being found in fruits of mycorrhizal plants

than in fruits of non-mycorrhizal plants These data suggest that

the effect of mycorrhization on chromaticity is a general one and

not fruit-specific It has been proposed that the colour of

straw-berry fruits is closely linked with the synthesis and/or expression

of pelargonidin-3-glucoside and cyanidin-3-glucoside, two

prin-cipal anthocyanins.44 In our context, this means that the colour

changes we observed as a result of mycorrhization are possibly

due to changes in the levels of these two anthocyanins

The flavour of strawberry fruits is determined by the balance

of sugars and acids.12Glucose, fructose and sucrose are the most

important sugars for the sensory quality of strawberry fruits,

representing 99% of the total carbohydrate content.45Moreover,

citric acid and malic acid are the most important acids in strawberry

fruits.46 Besides their impact on flavour, acids are important

because they affect the gelling properties of pectin Brix grade

is a composite parameter reflecting sugars, acids, salts and others

compounds soluble in water and is measured as the total soluble

solids present in the fruit

In our study, titratable acidity and Brix grade varied between

1.21 and 1.38% and between 4.81 and 6.17 respectively in all

treatments These values are wthin the ranges reported by

Perkins-Veazie and Collins47 for titratable acidity (0.5–1.87%) and Brix

grade (5–12) Glucose and fructose concentrations were higher

than sucrose concentration in all cases and the fructose/glucose

ratio was about 1 : 1, in agreement with values reported previously

for strawberry fruits.42

The level of applied N had a significant effect on titratable

acidity, glucose and fructose concentrations and Brix grade Fruits

of plants fertilised with 6 mmol L−1N were more acidic and their

glucose and fructose concentrations were higher in comparison

with fruits from other treatments, without significant differences

in Brix grade These data indicate that fruits from the 6 mmol L−1

N treatment had the best quality according to Mitcham.48In our

experiment, foliar area was also measured (data not shown) The

higher production of sugars in the 6 mmol L−1N treatment could

be explained by the enhanced foliar area of these plants (35.9and 25.3% higher than that of plants in the 3 and 18 mmol L−1Ntreatments respectively) when fructification started

Fruits of mycorrhizal plants had a lower fructose concentrationthan fruits of non-mycorrhizal plants, indicating that mycorrhiza-tion reduced the accumulation of this carbon compound in thefruits This could be explained by the fact that AMF act as carbonsinks (4–20% of the total carbon fixed by the plant).49

Around 4% of the dry matter of plants comprises mineralelements, which, owing to their role in enzymatic reactionsessential for fruit development and its cold conservation, areimportant for fruit quality.50In this study we observed that different

N levels modified the concentrations of some minerals in the fruits.Fruits of plants fertilised with 3 mmol L−1N showed higher K, Mg,

Fe and Zn levels than fruits of plants treated with 18 mmol L−1

N These results suggest that the roots of plants fertilised with

3 mmol L−1N took up higher amounts of these minerals.

It has been shown previously that a low availability of N in thesoil affects root growth Tolley-Henry and Raper51suggested thatunder conditions of low N availability the roots have priority to

N compared with other plant organs and therefore root growth

is promoted Rufty et al.52demonstrated that a low availability of

N in the soil increases the amount of photosynthates addressed

to the roots, thus being available for enhanced root growth Inour experiment, root dry weight and volume were also measured(data not shown) The root dry weight of plants fertilised with 3and 18 mmol L−1N was 2.0 and 1.7 g per plant respectively, whilethe root volume of these plants was 22.0 and 14.8 cm3per plantrespectively These data suggest that a higher soil volume wasexplored by plants fertilised with 3 mmol L−1N compared withplants treated with 18 mmol L−1N, which could explain the higher

K, Mg, Fe and Zn levels in fruits of plants of the 3 mmol L−1Ntreatment

To date, no adequate data are available on the effect

of mycorrhization on macro- and micronutrients in fruits

In our experiment, mycorrhization significantly modified theconcentrations of K, Cu and Mn, with fruits of mycorrhizal plantshaving higher K and Cu levels and a lower Mn level Althoughmycorrhizal root colonisation frequently increases macro- andmicronutrient accumulation in the leaves and stalks of plants,53,54

Liu et al.55found lower Cu, Zn, Mn and Fe concentrations in theshoots of mycorrhizal corn plants The inconsistent results onnutrient acquisition by mycorrhizal plants have been attributed tochanges in the rhizosphere due to increased N levels in the soil,which affect mycorrhizal development.56

Fruits of mycorrhizal plants fertilised with 3 mmol L−1N hadhigher concentrations of those minerals than fruits of mycorrhizalplants treated with 18 mmol L−1N Similar results of lower, equal

or higher acquisition of macro- and/or micronutrients dependent

on the level of mineral fertilisation have been reported in lettuce

inoculated with Glomus mosseae.4The lack of a beneficial effect ofmycorrhization in terms of mineral acquisition in our 18 mmol L−1

N treatment could be attributed to a negative effect of this Nconcentration on the extraradical mycelium development of theAMF The suppressive effect of high N levels on the formation

of extraradical mycelium has been described previously and hasbeen linked with reduced nutrient acquisition in mycorrhizalplants.57 Fruits of mycorrhizal plants fertilised with 6 mmol L−1

N had significantly higher Cu and Zn concentrations than fruits

of non-mycorrhizal plants fertilised with the same N level Thisindicates a positive effect of mycorrhization and N treatment on

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the acquisition of Cu and Zn by the roots and their translocation

to the fruits

Our results on the effect of N fertilisation on phenolic

compounds in strawberry fruits show that from the 3 mmol L−1

N treatment to the 18 mmol L−1N treatment the concentrations

of ellagic acid, quercetin and kaempferol decreased while the

concentrations of total phenols, cyanidin-3-glucoside and gallic

acid increased

A decrease in quercetin and kaempferol concentrations at high

N levels has been reported in tomato fruits,58while an increase

in ellagic acid concentration at low N levels has been found in

strawberry fruits.59In addition, Keller and Hrazdina60reported that

the N concentration in the soil had different effects on the total

phenol concentration in grapes The application of high N levels

led to low accumulation of flavonols, whereas the proportion of

anthocyanins was similar to that at low N levels Our results could

be explained by the effect that N has on the biosynthetic pathways

of phenolic compounds Phenylalanine ammonia-lyase (PAL) is the

principal enzyme of the phenylpropanoid pathway.61This enzyme

catalyses the transformation of the amino acidL-phenylalanine by

deamination to trans-cinnamic acid, which is the first product

necessary for the synthesis of phenolic compounds Interestingly,

it has been reported that at low N levels the enzymatic activity of

PAL is increased, liberating N for the amino acid metabolism, and

whereas the carbon products are diverted via 4-coumaroyl-CoA

into the flavonoid biosynthetic pathway.62In our study, this could

be an explanation for the increase in concentrations of some

flavonoids (cyanidin-3-glucoside, kaempferol and quercetin) in

fruits of plants fertilised with 3 mmol L−1N.

Mycorrhization modified the levels of most phenolic

com-pounds The cyanidin-3-glucoside, p-coumaric acid, quercetin and

kaempferol concentrations were higher and the gallic acid,

fer-ulic acid and ellagic acid concentrations were lower in fruits of

mycorrhizal plants than in fruits of non-mycorrhizal plants To our

knowledge, there are no data on the effect of AMF on phenolic

compound accumulation in fruits However, there are reports on

changes in the levels of p-coumaric acid and ferulic acid in the roots

of mycorrhizal onion plants,63changes in the levels of biochanin A,

formononetin, genistein and daidzein in the roots of mycorrhizal

alfalfa (M sativa L.)19 – 21and barrel medic (M truncatula)22 and

changes in the level of glyceoline in mycorrhizal soybean (G max

L.).24Most recently, it has been shown that through

mycorrhiza-tion the levels of phenols can also be altered in plant shoots.23Our

results extend these observations, showing that mycorrhization

can induce changes in phenolic compound levels even in fruits

An increase in applied N modified the concentrations of

some phenolic compounds between fruits of mycorrhizal and

non-mycorrhizal plants Differences were determined in fruits

of plants fertilised with 6 mmol L−1 N These results indicate

that N fertilisation modifies the response of the strawberry

plant to the AMF G intraradices This could be attributed to

changes in the rhizosphere due to N levels in the soil, which

affect mycorrhizal development56 and thus the acquisition of

other nutrients necessary for the production of phenols To our

knowledge, we have provided the first evidence that, depending

on the N level applied, the accumulation of phenolic compounds

is altered in fruits of mycorrhizal strawberry plants

CONCLUSION

Mycorrhization did not modify the weight, diameter or length

of strawberry fruits but had a negative effect on most colour

parameters Moreover, fruits of mycorrhizal plants had higher Kand Cu concentrations and showed greater accumulation of mostphenolic compounds The results indicate that the 3 mmol L−1

N treatment had a positive effect on the accumulation of someminerals in strawberry fruits, and fruits of mycorrhizal plants hadsignificantly higher phenolic compound, Cu and Zn concentrationsthan fruits of non-mycorrhizal plants when they were treated with

6 mmol L−1N In recent years, much interest has focused on theintake of phenolic compounds from the human diet and thehealth benefits due to their antioxidant nature It is therefore ofinterest to produce crops rich in flavonols without the need forgenetic modification Although previous studies have identified

a link between nutrient deficiency and phenolic compoundaccumulation in plant tissue, the present study provides evidencethat mycorrhization and N application in strawberry plants can beone strategy for increasing phenolic compound concentrations inthe fruits In addition, up-regulation of the flavonoid biosyntheticpathway in strawberry fruits may afford protection againstpathogen attack or light-induced damage Further studies arerequired to test this theory

ACKNOWLEDGEMENTS

The authors are grateful to ‘Fondos Mixtos CONACyT – Gobiernodel Estado de Michoac ´an’, Mexico for support of project 12268

‘Optimization of nitrogen and water in the strawberry crop

(Fragaria × ananassa Duch.) by the use of arbuscular mycorrhizal

fungi’ and to CONACYT for provision of a PhD grant to VilmaCastellanos Moreover, we thank Dr Philippe Lobit, Sandra andSilvia Velasco L ´opez, Flor Lorena Reyes S ´anchez and Alejandrino

L ´opez Hern ´andez from UMSNH, Morelia, Michoac ´an, Mexico andVeronica Schober, Monika Marek and Karin Korntheuer from theChemistry Laboratory of the Federal College and Research Institutefor Viticulture and Pomology, Klosterneuburg, Austria for theirsupport

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Research Article

Received: 28 September 2009 Revised: 6 April 2010 Accepted: 9 April 2010 Published online in Wiley Interscience: 25 May 2010(www.interscience.wiley.com) DOI 10.1002/jsfa.4012

Effect of different light transmittance paper

bags on fruit quality and antioxidant capacity

loquat cultivars, Baiyu and Ninghaibai (Eriobotrya japonica Lindl.), were used for materials One-layered white paper bags

(OWPB) with ∼50% light transmittance and two-layered paper bags with a black inner layer and a grey outer layer (TGDPB) with ∼0% light transmittance were used as treatments and unbagged fruits were used as the control (CK) in this experiment Fruit quality was determined by physicochemical characteristics, the quantity of sugar, total phenolic, flavonoid, carotenoid and vitamin C The antioxidant capacities of the methanol extracted from the pulp were tested using three different assays.

RESULTS: The results showed that bagging decreased the weight of fruit but promoted the appearance of loquat fruits The total sugar content in the fruit bagged with OWPB was higher than in controls and in fruit bagged with TGDPB The total phenolic and flavonoid contents were decreased by both bagging treatments, with the lowest occurring in the fruit bagged with TGDPB Bagging also decreased the total antioxidant capacity of the fruit pulp, which was again lower in TGDPB-treated fruits than in those bagged using OWPB Correlation analysis showed a linear relationship between total antioxidant capacity and the content of total phenolic and flavonoid.

CONCLUSION: The results showed that different light transmittance bags had different effects on fruit quality and antioxidant capacity In particular, bags with low light transmittance (TGDPB) decreased the inner quality and total antioxidant capacity of loquat fruit All results indicated that bagging with OWPB was more suitable for maintaining the quality of the loquat fruit than bagging with TGDPB.

c

2010 Society of Chemical Industry

Keywords: antioxidant capacity; bagging; Eriobotrya japonica Lindl; flavonoid content; loquat; total phenolic content

INTRODUCTION

Loquat (Eriobotrya japonica Lindl.) is widely cultivated in

subtrop-ical regions of Asia and other continents Ripe loquat fruits are

spherical or oval in shape, orange/yellow or white in colour and

have a soft and juicy flesh During their growth and maturation,

they are susceptible to insect pests, birds, diseases and mechanical

damage, which reduce their commercial value Bagging, a

phys-ical protection technique commonly applied to many fruits, not

only improves fruit visual quality,1 – 4by promoting fruit coloration

and reducing the incidences of fruit cracking and russet, but can

also change the microenvironment of fruit development, which

has multiple effects on the inner quality of fruits Sugars and

or-ganic acids are the major determinants of fruit taste and flavour

However, varied results have been obtained from experiments on

the effects of bagging on the sugar and organic acid contents of

fruits Chundawat et al.5showed that bagging generally reduces

the sugar content of fruit, whereas Hussein et al.6reported that

bagging significantly increased the total sugar content Huang

et al.7reported that bagging treatments did not affect the total

soluble sugar content, but decreased the organic acid contents of

fruit Kim et al.8showed that titratable acids tended to increase

after bagging with yellow paper of low light transmittance

In addition to sugars and organic acids, loquat fruits also containdiverse nutrient and non-nutrient molecules, such as phenolics

(especially the flavonoids), vitamin C, and β-carotene, many of

which have antioxidant properties These compounds exert arange of biological effects including antibacterial, antiviral, anti-inflammatory, antithrombotic and vasodilatory actions.9,10 Theyalso have pronounced antioxidant and free-radical-scavengingactivities.11 – 13 However, most of the studies on bagging fruitfocus on the appearance and general qualities of the fruit andstudies of the effects of bagging on antioxidant compounds andantioxidant capacity of fruits are rare Therefore, this study wascarried out to examine the effect of different light transmittancepaper bags on fruit quality and antioxidant capacity in two loquatcultivars

∗ Correspondence to: Jun-wei Chen, Institute of Horticulture, Zhejiang Academy

of Agricultural Sciences, Hangzhou, Zhejiang, 310021, China.

MATERIALS AND METHODS

Standards and chemicals

ABTS [2,2-azino-bis (3-ethylbenzthiozoline-6-sulfonic acid)],

DPPH (the 1,1-diphenyl-2- picrylhydrazyl radical), TPTZ

[2,4,6-tri(2-pyridyl)-s-triazine], Trolox

(6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), DPIP

(phenolindo-2,6- dichlorophenol), rutin, β-carotene, HPLC-grade sucrose,

glucose, fructose and sorbitol were all purchased from

Sigma-Aldrich (Shanghai, China) All reagents were of analytical grade

unless indicated otherwise

Plant material

Two loquat cultivars, Baiyu and Ninghaibai (Eriobotrya japonica

Lindl.), grown in a commercial orchard in Qingpu District, Shanghai,

China were used to test the bagging treatment Approximately

100 fruitlets that were similar in appearance and size and which

received sunlight uniformly were randomly selected for each

bagging treatment Approximately 100 unbagged fruitlets that

were also similar in appearance and size were tagged as controls

(CK) Bagging treatments were conducted after fruit thinning in

early April At maturity, 50 fruits of each treatment were used for

fruit quality and antioxidant capacity analyses Fruit maturity and

ripeness were assessed based on fruit firmness and skin colour

Two types of bag were used in this study: (1) 25 cm× 36 cm

one-layered white paper bags (OWPB) with∼50% light transmittance;

(2) 25 cm× 36 cm two-layered paper bags with a black inner layer

and a grey outer layer (TGDPB) with∼0% light transmittance All

bags were coated with wax and supplied by Shangyu Jiali Paper

Bag Product Co., Ltd (Ningbo, China)

Physicochemical characteristics analyses

The fruit mass of 30 loquats from each bagging treatment was

measured by using an electronic balance (0–210 g ± 0.001 g;

model C-600-SX; Cobos, Barcelona, Spain) These fruits were

randomly divided into five groups (replicates) with six fruits in each

group The fruits were then manually peeled, cut into small pieces

and juiced together The soluble solids concentration (SSC) was

measured in the filtered juice by using a hand-held refractometer

(Atago, Tokyo, Japan) and calibrated with distilled water The

juice was also analysed for titratable acidity (TA) by titration

with 0.01 mol L−1NaOH, using phenolphthalein as an indicator.

Twenty fruits of each treatment were selected for surface colour

determination using a chromameter (ADCI-60-C, Beijing, China)

calibrated with a manufacturer-supplied white calibration plate

Results were expressed as lightness (L∗), redness (a∗), yellowness

(b∗) and hue angle (hab = tan−1[(b∗)(a∗)−1]) The colour reading

was taken fourth at the equatorial region of each fruit and averaged

to give a value for each fruit After surface colour determination,

the fruits were manually peeled, cut into small pieces and the

composite fruit samples ranging from 2 to 10 g were weighed and

frozen in liquid nitrogen, then stored at−80◦C until analysis

Determination of sugar content

The quantity and types of sugar were determined as described

by Chen et al.14 Soluble sugars were extracted by grinding 5 g

frozen fruit in five volumes (w/v) of methanol : chloroform : water

as 12 : 5:3 (v/v) Extracts were centrifuged at 5000×g for 5 min The

extraction was performed three times Water and chloroform were

then added to bring the final methanol : chloroform : water ratio

to 10 : 6:5 and the chloroform layer was removed The remaining

aqueous–alcohol phase was adjusted to pH 7.0 using 0.1 mol L−1

NaOH, then dried in a vacuum and redissolved with distilledwater The sugar in water solution was analysed by using HPLC(Waters 1525; Waters, Milford, Massachusetts, USA) The columntemperature was 90◦C and 80% acetonitrile was used as an elution

at a flow rate of 1 mL min−1 Fructose, glucose, sucrose and sorbitolwere identified and quantified by comparing the retention andintegrated peak areas of external standards

Total carotenoid content analysis

The total carotenoid content was determined as described by

Reyes et al.15Carotenoids were extracted from 2 g frozen fruit byhomogenising with 25 mL of acetone : ethanol (1 : 1) containing

200 mg L−1 butylated hydroxytoluene (BHT) The homogenatewas filtered through a Whatman no 4 filter, washed with the sol-vent (∼60 mL) and diluted to 100 mL using the extraction solvent.Extracts were transferred to a plastic container to which 50 mLhexane was added The container was then shaken and allowed

to stand for 15 min after which 25 mL of nanopure water wasadded The container was shaken again and the contents were al-lowed to separate for 30 min The spectrophotometer was blankedwith hexane and absorbance of the samples in 1-cm quartz cu-vettes was measured at 470 nm Carotenoid was quantified as

(1–4µg mL−1) Results were expressed asµg β-carotene

equiva-lent g−1fresh weight.

Vitamin C content analysis

The vitamin C content of the fruit extracts was determined

by the 2,6-dichloroindophenol titrimetric method.16 Briefly, thesamples were mixed with 40 mL of buffer (1 g L−1oxalic acid plus

4 g L−1 anhydrous sodium acetate) and were titrated againstthe dye solution containing 295 mg L−1 DPIP (phenolindo-2,6-dichlorophenol) and 100 mg L−1 sodium bicarbonate Thestandard curve was generated with concentrations of 0.2, 0.4,0.6, 0.8 and 1 mg of standard L-ascorbic acid (AnalaR; BDH,Buffalo, New York, USA) The ascorbic acid content in the sampleswas determined from the standard curve and the results wereexpressed asµg ascorbic acid equivalent g−1fresh weight.

Extracts for phenolic and antioxidant capacity measurement

To analyse the total phenolic and antioxidant activity, fruit extracts

in methanol were prepared using the method of Swain andHillis,17 with some modifications A 10 g sample of fruit werehomogenised in 25 mL absolute methanol using a Waring blender.The homogenates were kept at 4◦C for 12 h and then centrifuged

at 15 000× g for 20 min The supernatants were collected, and

extraction of the residue was repeated using the same conditions.The two supernatants of methanol were combined and dividedinto two equal aliquots and then stored at−20◦C until analysis.The first supernatant was used for the quantitative analysis ofphenolic compounds and the second was used to determine theantioxidant activity

Total phenolic content analysis

The Folin–Ciocalteu reagent assay18was used to determine thetotal phenolic content A 0.1 mL sample aliquot was mixed with

5 mL of 0.2 mol L−1Folin–Ciocalteu reagent The solution wasallowed to stand at 25◦C for 5 min before adding 4 mL of 15%(w/v) sodium carbonate solution in distilled water The absorbance

at 765 nm was read after the initial mixing and then for up to 90 min

Effect of bagging on loquat fruit quality and antioxidant capacity www.soci.org

until it reached a plateau Gallic acid was used as a standard for

the calibration curve Results were expressed as µg gallic acid

equivalent g−1fresh weight.

Total flavonoid content analysis

The flavonoid content was measured using a colorimetric assay

developed by Jia et al.19Plant extract (2.0 mL) or standard solutions

of rutin (Sigma) were added to a 10 mL volumetric flask Distilled

water was added to make a volume of 5 mL At zero time, 0.3 mL

of 5% w/v NaNO2was added to the flask After 5 min, 0.6 mL of

10% w/v AlCl3was added and after 6 min, 2 mL of 1 mol L−1NaOH

was added to the flask, followed by 2.1 mL distilled water The

absorbance was read at 510 nm against the blank (water) and the

flavonoid content was expressed asµg rutin equivalent g−1fresh

weight

Antioxidant capacity determinations

Free radical scavenging activity on DPPH

The free radical scavenging activity of the extracts, based on

the scavenging activity of the stable 1,1-diphenyl-2-picrylhydrazyl

(DPPH) free radical, was determined by the method described by

Braca et al.20Plant extract (0.1 mL) was added to 3 mL of a 0.004%

MeOH solution of DPPH Absorbance at 517 nm was determined

after 30 min, and the percentage inhibition activity was calculated

from [(A0− A1)/A0]× 100, where A0 is the absorbance of the

control, and A1is the absorbance of the extract/standard Results

were expressed asµmol Trolox equivalent g−1fresh weight.

Antioxidant activity using the ABTS assay

The ABTS•scavenging ability of extracts was determined according

to the method described by Re et al.21ABTS•was generated by

reacting an ABTS aqueous solution (7 mmol L−1) with K2S2O8

(2.45 mmol L−1, final concentration) in the dark for 16 h and

adjusting the absorbance at 734 nm to 0.700 with ethanol A

0.2 mL aliquot of appropriate dilution of the extract was added

to 2.0 mL ABTS• solution and the absorbance was measured

at 734 nm after 15 min Results were expressed asµmol Trolox

equivalent g−1fresh weight.

Ferric reducing/antioxidant power assay

The FRAP assay was used as described by Benzie and Strain22with some modifications The stock solutions included 300 mmolacetate buffer (3.1 g C2H3NaO2·3H2O and 16 mL C2H4O2), pH 3.6;

10 mmol TPTZ (2,4,6-tripyridyl-s-triazine) solution in 40 mmol HCl,

and 20 mmol FeCl3·6H2O solution The fresh working solution wasprepared by mixing 25 mL acetate buffer, 2.5 mL TPTZ solution,and 2.5 mL FeCl3·6H2O solution and then warmed at 37◦C beforeuse 150µL of fruit extracts or methanol (for the reagent blank) wasreacted with 2850µL of the FRAP solution at 37◦C for 30 min in thedark (in a water bath) Readings of the coloured product (ferroustripyridyltriazine complex) were then taken at 593 nm Resultswere expressed asµmol Trolox equivalent g−1fresh weight.

Statistical analysis

The significance of the results and statistical differences wereanalysed using SYSTAT version 10.0 (SPSS, Chicago, IL, USA).Analysis of variance (ANOVA) of the data was performed tocompare mean values for each variable under different cultivars.The least significant difference test (LSD) was used to determine thedifferences between means at a 5% significance level Correlationcoefficients of DPPH, TEAC and FRAP with respect to total phenolic,total flavonoid, total carotenoid and vitamin C contents wereevaluated

RESULTS AND DISCUSSIONThe effects of bagging on the physicochemical characteristics

of loquat fruit

Bagging is already known to affect the size and weight ofpomegranate,6,23apple24and banana.25In this study, all baggingtreatments decreased the weight of loquat fruit compared withcontrols (Table 1) And, fruits treated with TGDPB were smaller thanthat treated with OWPB The total soluble solids remained constantand titratable acid decreased in Baiyu fruits treated with OWPB,whereas total soluble solid significantly decreased and titratableacid markedly increased in Baiyu fruits treated with TGDPB ascompared with Baiyu controls However, there was little effect onthe total soluble solids in Ninghaibai fruits bagged with either

CK, control (unbagged); OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey outer layer.

J Sci Food Agric 2010; 90: 1783–1788
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa

CK, control (unbagged); OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey outer layer.

OWPB or TGDPB, although the titratable acid content increased

significantly compared with Ninghaibai controls

Surface colour is an important marketable (consumer

accep-tance) quality attribute and is a measure of L∗, a∗, b∗ and h ab.

Table 1 shows that bagging improved fruit surface lightness as

L∗ was higher in the bagged than in the control fruits of both

Baiyu and Ninghaibai Fruit treated with TGDPB had the highest

lightness values Reduced light is known to promote the

degra-dation of existing chlorophyll and inhibits carotenoid synthesis in

fruit peel,26and this resulted in unbagged fruits having a lower

h aband a more red than yellow hue (i.e were more orange in

colour compared with bagged fruits) In addition, fruits treated

with TGDPB had higher h abthan did those treated with OWPB

The effects of bagging on sugar content

Sugar content is considered to be an important quality

charac-teristic of fresh fruit However, bagging with different materials

can exert different effects on the composition of soluble sugars

For example, Padmavathamma and Hulamani23found that total

sugars varied significantly with bag colour, whereas Yang et al.27

observed that bagging tended to reduce sugar content slightly,

although the sugar content was not significantly affected by bag

type Table 2 indicates that the effects of bagging type on sugar

content varied between cultivars OWPB treatment increased the

sucrose, glucose, fructose and sorbitol content and significantly

increased the content of total sugar in Baiyu fruit However, OWPB

treatment increased the sucrose content, did not affect the

glu-cose, fructose and total sugar content but decreased the sorbitol

content in Ninghaibai fruit Total sugar contents in Baiyu and

Ning-haibai after TGDPB treatment were reduced by 5.6% and 11.3%,

respectively, as compared with the controls

The effects of bagging on antioxidant compounds

and antioxidant capacity

Numerous studies have shown that fruit and vegetables are

sources of diverse nutrient and non-nutrient molecules, many of

which have antioxidant properties The present study determined

the antioxidant capacities of loquat fruit and analysed fruit extracts

for compounds (total phenolic, flavonoid, carotenoid and vitamin

C) that might contribute to the antioxidant activity Table 3 showsthat the total phenolic and flavonoid contents decreased afterbagging treatment Following OWPB and TGDPB treatment, thetotal phenolic content of Baiyu fruit was reduced by 9.5% and45.6%, respectively, and that of Ninghaibai fruit was reduced by5.0% and 26%, respectively This indicates that bagging influencesthe metabolism of phenolic compounds, of which the flavonoidsare the dominant family The pattern of variation in flavonoidcontent was similar to that observed for total phenolic, with

maximum levels occurring in unbagged Baiyu (28.2 ± 4.4 µg g−1)

and Ninghaibai (51.0 ± 6.4 µg g−1) fruits The flavonoid contentwas also lower in TGDPB-treated fruits than in OWPB-treated fruit.Carotenoids and vitamin C are also the antioxidant compounds

in loquat The study found that the carotenoid and vitamin Ccontents increased after bagging fruit with OWBP, but decreased

in fruit bagged with TGDBP Our other experiments have alsoobserved that the carotenoid and vitamin C content variessignificantly with bag type; however, both decreased markedlywhen light was excluded during the maturation of loquat ascompared with that of control fruit (data not shown)

Three independent methods, the DPPH, TEAC and FRAP assays,were used to compare the antioxidant capacity of fruit extracts.The results presented in Table 3 show that the antioxidantpotential of loquat fruit extracts was significantly affected bylight transmittance The highest antioxidant potential in both ofBaiyu and Ninghaibai loquat fruits was under full sunlight andlowest under bagging with TGDPB

Table 4 indicates that the total phenolic content and antioxidant

capacity are well correlated (DPPH, r = 0.64; TEAC, r = 0.77; FRAP, r = 0.90) Fruits with the highest phenolic content

(unbagged fruits of Baiyu and Ninghaibai) had the highestantioxidant potentials whereas fruit extracts characterised bylow total phenolic levels exhibited a poor antioxidant capacity.Numerous studies have reported similar linear relationshipsbetween antioxidant activities and phenolic content.28 – 30 Agood correlation was also observed between total flavonoid and

antioxidant capacity (DPPH, r = 0.84; TEAC, r = 0.87; FRAP, r = 0.99) (Table 4) Flavonoids are low-molecular-weight polyphenolic

compounds that are widely distributed in fruit and vegetables,31and many have been shown to have antioxidant32and anticancer

Effect of bagging on loquat fruit quality and antioxidant capacity www.soci.org

assays of loquat fruit after bagging

Cultivar Treatment

Total phenolic ( µg gallic acid

g−1FW)

Flavonoid ( µg rutin g−1FW)

Carotenoid ( µg

β-carotene g−1FW)

Vitamin C ( µg ascorbic acid g−1fresh weigh)

DPPH ( µmol Trolox g−1FW)

TEAC ( µmol Trolox g−1FW)

FRAP ( µmol Trolox g−1FW)

∗Values are significant at P = 0.001.

CK, control (unbagged); FW, fresh weight; OWPB, one-layered white paper bags; TGDPB, two-layered paper bags with a black inner layer and a grey

outer layer.

respect to total phenolic, flavonoid, carotenoid and vitamin C content

properties.33 Bagging resulted in lower irradiation, which has

an important role in the synthesis of phenolic compounds

Bakhshi and Arakawa34 reported that light irradiation could

induce phenolic compound biosynthesis in the flesh of apples

Some studies have observed that enhanced light conditions could

activate the expression of the flavonoid biosynthetic genes.35 – 38In

addition, several studies have reported changes in the qualitative

and quantitative composition of flavonoids as a consequence of

high solar radiation.39 – 41 Therefore, reduced light irradiation by

bagging is the probable cause of the decreased total phenolic and

flavonoid contents recorded in both loquat cultivars

However, the total carotenoid level does not contribute

significantly to the antioxidant potential of loquat, as shown

by the poor correlations between the DPPH, TEAC and FRAP

measurements of antioxidant capacity and total carotenoid

content Similarly, vitamin C also makes a minor contribution to

antioxidant capacity Wang et al.42reported that the contribution

of vitamin C to the total antioxidant activity of a fruit is <15% Prior

et al.28observed that the influence of ascorbate on the antioxidant

capacity of lowbush and highbush blueberries is as low as 2.3%

and 1.5% of the total capacity, respectively It seems probable,

therefore, that the overall antioxidant capacity of loquat fruits

is linked mainly to their high phenolic content, with flavonoids

making a major contribution

CONCLUSIONS

Bagging experiments using two cultivars of loquat showed thatbagging could improve fruit commercial value by improvingfruit visual quality, however, the light transmittance levels ofthe bags significantly affected fruit inner quality Bags with lowlight transmittance (TGDPB) significantly increased the content oftitratable acid but decreased the fruit weight and the total sugar,phenolic, flavonoid, carotenoid and vitamin C contents as well

as the antioxidant capacity of the fruit However, bagging withOWPB had less influence on fruit quality and antioxidant capacity.Therefore, using a bag with appropriate light transmittance isnecessary to maintain fruit quality and antioxidant capacity, andOWPB was more suitable for loquat bagging than was TGDPB

ACKNOWLEDGEMENTS

This work was supported by Natural Science Foundation ofZhejiang Province in China (Y307577 and Y306128) and ImportantItem of Science and Technology Department of Zhejiang Province

pears (Pyruspyrifolia Nakai cvs Gamcheonbae and Yeongsanbae).

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Research Article

Received: 16 February 2010 Revised: 8 April 2010 Accepted: 15 April 2010 Published online in Wiley Interscience: 25 May 2010(www.interscience.wiley.com) DOI 10.1002/jsfa.4014

Preparation of a monoclonal antibody and

development of an indirect competitive ELISA

for the detection of chlorpromazine residue in

chicken and swine liver

Abstract

BACKGROUND: Chlorpromazine is a typical antipsychotic drug used to make food-producing animals calm and promote growth

as feed additives Accumulation of chlorpromazine in animal bodies would cause side effects in the circulatory and nervous systems, and have adverse effects on blood cells, the skin and the eye To detect the chlorpromazine residue in food producing animals, an indirect competitive enzyme-linked immunosorbent assay (ELISA) was developed based on preparation of an anti-chlorpromazine monoclonal antibody.

RESULTS: The antibody generated from immunogen of cationic bovine serum albumin (cBSA) coupled with chlorpromazine showed high sensitivity toward chlorpromazine with an IC 50 value of 0.73 ppb The ELISA method was applied to detect swine liver and chicken samples spiked by chlorpromazine and satisfactory results were obtained The recovery rates in chicken and

swine liver were in the range of 88–95% and 86–95%, respectively; the intra-assay coefficients of variation were both <15.3% and <13.5%, respectively.

CONCLUSION: An indirect competitive ELISA method based on a monoclonal antibody towards chlorpromazine with excellent sensitivity and specificity has been successfully developed The immunoassay provided in this study was a hopeful alternative

to chromatography spectrometry for regulatory analysis of chlorpromazine residue in food-producing animals.

c

2010 Society of Chemical Industry

Keywords: chlorpromazine; drug residues; ELISA; monoclonal antibody (MAbs)

INTRODUCTION

Phenothiazines are sedatives that act on the central nervous

sys-tem They are widely used as medicines for humans to treat and

prevent psychotic diseases, and as veterinary drugs for animals.1 – 4

Sedatives cause calmness, sleepiness and insensitive to

sur-roundings, and therefore, are commonly used to reduce adverse

influence during the transportation of food-producing animals to

food market Chlorpromazine (Fig 1, commercially called

Winter-min) is a classic phenothiazine antipsychotic drug.5 – 7Daily use of

chlorpromazine as a feed additive could promote growth of the

an-imals; however, the residues of chlorpromazine are a potential risk

to the health of the consumer by inducing orthostatic hypotension,

obstructive type of jaudice, leukocytosis, leukopenia and

derma-tological reactions, as evaluated by the European Agency for the

Evaluation of Medicinal Products (EMEA) and the Joint FAO/WHO

Expert Committee on Food Additives (JECFA) in 1991.8,9Taking into

consideration the relevant toxicity of chlorpromazine in humans,

JECFA suggested that chlorpromazine should not be used in

food-producing animals Also, chlorpromazine was prohibited at

de-tectable levels in veterinary medicinal products in foodstuffs of

an-imal origin by the European Union [Annex IV of Council Regulation

(EEC) No.2377/90].10On 24 October 2002, the Ministry of

Agricul-ture of the People’s Republic of China officially prohibited mazine addition in foodstuffs of animal origin (No 235) To monitorchlorpromazine residue in foodstuffs of animal origin, it is neces-sary to establish simple, rapid and effective detection methods

chlorpro-Up to now, a variety of methods for detecting residues of promazine in biological matrices has been developed Comparedwith other antipsychotic agents, the investigation of chlorpro-mazine detection is still in the initial stages, and a nationalstandard for pure chlorpromazine detection has not been es-tablished yet To detect chlorpromazine residues, classic analyticalmethods, such as liquid chromatography coupled with ultravioletspectroscopy,11,12 coulometric detection13 and gas chromatog-

chlor-∗ Correspondence to: Rimo Xi and Meng Meng, College of Pharmacy, Nankai

University, Tianjin, 300071, P.R China E-mail: xirimo2000@yahoo.com; mengmeng@nankai.edu.cn

a The School of Chemistry and Chemical Engineering, Shandong University,

Jinan, Shandong, 250100, P.R China

b College of Pharmacy, Nankai University, Tianjin, 300071, P.R China

c Beijing Wanger Biotechnology Co., LTD., Beijing, 102206, P.R China

J Sci Food Agric 2010; 90: 1789–1795 www.soci.org
2010 Society of Chemical Industryc

S N

N

S N

N Cl

F

O

N N N

OH

N N N

N

Promethazine O

Figure 1 Chlorpromazine and other structurally related sedatives analysed in this study.

raphy–mass spectrometry14 – 16have been used These methods

require extensive sample preparation, sometimes expensive

ap-paratus, highly trained personnel to operate sophisticated

instru-ments and interpret complicated results, and can only determine

a limited number of samples at one time Therefore these methods

are not suitable as screening tests In contrast, because of the

rapid-ity, mobilrapid-ity, convenience, high sensitivrapid-ity, and low detection limit,

enzyme-linked immunosorbent assay (ELISA) methods have been

used for the detection of various drug residues in real systems.17 – 22

A key factor for an ELISA test is whether a polyclonal antibody

or monoclonal antibody (MAb) towards the compound detected

was used Compared with a polyclonal antibody, the application

of a monoclonal antibody is advantageous in terms of better

purity, satisfactory sensitivity and high specificity.23 – 26 In this

paper, an ELISA test kit based on a monoclonal antibody toward

chlorpromazine was developed and applied to detect

chlorpro-mazine spiked in swine liver and chicken samples This study is

the first to prepare a monoclonal antibody of chlorpromazine

and develop an immunoassay based on an antibody to detect

residues of chlorpromazine in swine liver and chicken

EXPERIMENTAL

Chemicals and materials

Bovine serum albumin (BSA), ovalbumin (OVA) and goat

anti-mouse IgG–horseradish peroxidase (HRP) conjugate

were provided by Beijing Wanger Biotechnology Co., Ltd

(Beijing, China) o-Phenylenediamine (OPD) and 3,3,5,5

-tetramethylbenzidine (TMB), N,N-dicyclohexylcarbodiimide (DCC)

and N-hydroxysuccinimide (NHS) were purchased from Xinjingke

Biotechnology (Beijing, China) Freund’s complete (cFA) and complete adjuvants (iFA) were obtained from Sigma–Aldrich (St

in-Louis, MO, USA) n-Hexane, hydrochloric acid, dimethylsulfoxide

(DMSO), sulfuric acid, sodium hydroxide, acetonitrile, hydrogenperoxide (30% H2O2) and other reagents used were provided byGuangmang Chemical Co (Jinan, China) Chicken samples werepurchased from commodity exchange and swine liver sampleswere from a supermarket in Jinan City, China

Instrumentation and supplies

ELISA was performed on polystyrene 96-well microtitre plates (BioBasic Inc., Ontario, Canada) and spectrophotometrically read with

a GF-M3000 microplate reader (Ruicong Shanghai TechnologyDevelopment Co., Ltd, Shanghai, China) Centrifugation wascarried out with a Biofuge Stratos refrigerated centrifuge (Heraeus,Hanau, Germany) Protein dialyses were performed using dialysisbags from Aibo Economic & Trade Co., Ltd (Jinan, China) LC-MSwas performed on a LC/MS-2010A instrument from Shimadzu(Kyoto, Japan)

Buffers

Ultra-pure deionised water was used for the preparation of allbuffers and reagents for the immunoassays, unless especiallyindicated Phosphate-buffered saline (PBS, pH 7.4) consisted of

138 mmol L−1NaCl, 1.5 mmol L−1KH2PO4, 7 mmol L−1Na2HPO4and 2.7 mmol L−1KCl The wash buffer (PBST) was a PBS buffer

Detection of chlorpromazine in swine liver and chicken by ELISA www.soci.org

added 0.05% (v/v) Tween 20 0.05 M carbonate buffer (15 mmol L−1

Na2CO3and 35 mmol L−1NaHCO3, pH 9.6) was used as a coating

buffer The blocking buffer was a solution of PBS mixed with

1% of OVA and 0.05% (v/v) Tween 20 The substrate buffer was

0.1 mol L−1sodium acetate/citrate buffer (pH 5.0) Eighty millilitres

of acetonitrile added to 20 mL of 0.1 mol L−1HCl was used as the

extractive solution To prepare the substrate solution, 10 mg of

TMB+ 5 mL DMSO was defined as substrate solution A and 5 µL of

H2O2[30% (w/w)]+ 15 mL citrate buffer was defined as solution

B The stopping solution was 2 mol L−1H2SO4.

Preparation of immunogen and coating antigen

Modification of hapten

As described in Fig 2 (Scheme 1), the mixture of acepromazine

(compound I) (10 mg, 0.031 mmol) in 2.5 mL of ethanol and

hydroxylamine hydrochloride (2.5 mg) in 1.0 mL of distill water

was stirring under reflux in water bath for 2.5 h A solution of NaOH

(1.0 mL, 0.05 mol L−1) was added dropwise during the procedure.

An acetate buffer (1.0 mL, pH 4.0) was added dropwise followed

by adding 4 mg of ice until a white precipitate appeared The

white solid was isolated by centrifugation (10 000× g) after 1 day.

The white solid was dissolved in DMF (2.5 mL) and the mixture

was kept reaction at room temperature for 2 h after succinic

anhydride was added The reaction was continuing for 4 h after

100 mL of triethylamine was added to yield chlorpromazine hapten

(compound III)

Preparation of immunogen

The immunogen of chlorpromazine was prepared via a mixed

acid anhydride reaction (Fig 2, Scheme I) In this procedure, 15µL

of isobutyl chloroformate was added into the 1.0 mL of hapten

prepared above at 10◦C to obtain solution A The solution B

was prepared by dissolving 36 mg of BSA in 2 mL of sodium

carbonate (50 mmol L−1) Under stirring, the solution A was added

dropwise into solution B with molar ratio of hapten : BSA =

10 : 1 for 4 h at 10◦C to get the immunogen of cationic BSA

(cBSA)–chlorpromazine (compound V) The mixture was stirred

overnight at 4◦C and dialysed for 3 days against PBS (0.01 mol L−1),

exchanging the dialysis solution twice each day The solution

obtained was stored at−20◦C for future use

Preparation of coating antigen

The mixture of 1.0 mL of hapten (compound III) obtained in

procedure Scheme I, 20 mg of DCC, and 12.5 mg of NHS in 0.5 mL

of DMF was stirring for 24 h at room temperature to obtain solution

C The solution D was prepared by dissolving 50 mg OVA in 3.5 mL

of PBS (0.01 mol L−1, pH 7.2) The solution C was added dropwise

into solution D and the mixture obtained was stirred at room

temperature for 3 h to remove small amounts of impurities The

precipitation was removed by centrifugation at 13 000× g for

30 min and the supernatant was collected to obtain the coating of

cationic OVA (cOVA)–chlorpromazine (compound VII), which was

stored at−20◦C for future use

Immunisation, cell fusion and purification of MAbs

BALB/c mice were initially immunised by an intraperitoneal

injec-tion of 150µg of cBSA–chlorpromazine in an equal volume of cFA

Two weeks later, a booster injection was performed using the same

amount of cBSA–chlorpromazine in iFA More double boosts were

continued with 100 mg cBSA–chlorpromazine given in the tail vein

at an interval of 2 weeks After the immune response had been idated, the splenocytes from immunised mice were fused with alogarithmically growing hypoxanthine–aminopterin–thymidine(HAT)-sensitive mouse myeloma cells Sp2/0 (7 : 1) by the polyethy-lene glycol (PEG) method The hybridoma cells were screened byindirect ELISA and cloned by the limited dilution method Thehybridoma cells were cultured in the medium (pH 7.4) contain-ing 0.2% NaHCO3and RPMI1640 added 20% newborn calf serum

val-in 37◦C to obtain MAbs for chlorpromazine The purification wasperformed according to the acid–ammonium sulfate method, andthe purified MAbs obtained was stored at−20◦C for future use

Antibody titre determination by indirect competitive ELISA

The antibody titre was tested by indirect ELISA The procedurewas carried out as described below The microplates were coatedwith coating antigen cOVA–chlorpromazine at 1/500, 1/1000 and1/2000 by overnight incubation at 4◦C Plates were washed withwash buffer three times and blocked with 250µL well−1of blockingbuffer, followed by incubation for 1 h at room temperature Plateswere washed three times again, then the appropriate dilution

of antisera was added, and the plates were incubated for 2 h atroom temperature After the plates had been washed three times,goat anti-mouse IgG-HRP (1 : 1000, 100µL well−1) was added,followed by incubation for 2 h at room temperature Plates werewashed three times and TMB substrate solution A and B was added(50µL well−1) in turn After that, the plates were incubated foranother 15 min at room temperature The colour developmentwas inhibited by adding stopping solution (100µL well−1), andabsorbances were measured at 450 nm Absorbance values werecorrected by blank reading Pre-immune withdrawal serum (theserum before immunisation) was used as a negative control, andthe antibody titre was defined as the reciprocal of the dilution thatresulted in an absorbance value of twice the blank value

Development of indirect competitive ELISA

The checker-board procedure was used to obtain the mised coating antigen and the primary antibody concentra-tions To each well of a 96-well plate, 100µL of 10 µg mL−1ofcOVA–chlorpromazine in bicarbonate buffer (0.05 mol L−1, pH9.6) was added, and the mixture was incubated overnight at 4◦C.The plate was washed with wash buffer three times between eachstep, and blocked with 250µL well−1of blocking buffer, followed

opti-by incubation for 1 h at room temperature After the blockingsolution was removed, 100µL of primary antibody was added toeach well followed by the addition of PBST buffer or competitor

in PBST buffer, and the plate was incubated for 2 h Then, the goatanti-mouse IgG HRP (1 : 1000, 100µL well−1) was added, followed

by incubation for 2 h at room temperature Substrate solutions

A and B were added (50µL well−1) in turn, and the plate wasincubated for another 15 min at room temperature The colour de-velopment was inhibited by adding stopping solution (2 mol L−1

H2SO4, 100µL well−1), and absorbance was measured at 450 nm.Absorbance was corrected by blank reading Pre-immune with-drawal serum was used as a negative control The result wasexpressed in % inhibition as follows: % inhibition= %B/B0, where

B is the absorbance of the well with competitor and B0 is theabsorbance of the well without competitor

Standard curve generation

The cOVA–chlorpromazine (1/1000) was used as coating antigen,and indirect cELISA was performed as described above The

J Sci Food Agric 2010; 90: 1789–1795
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa

S N

N O

NH2OH⋅HCl

S N

N

N OH

S N

N

N O C CH2CH2COOH O

O O

N

N O C CH2CH2COCOOCH2CH(CH3)2O

(IV)

BSA-NH2

S N

N

N O C (CH2)2COO O

(VI)

N O

O

S N

Scheme 1

Scheme 2

Figure 2 The synthetic procedure for immunogen of cBSA–chlorpromazine (Scheme 1) and the coating antigen of cOVA–chlorpromazine (Scheme 2).

selected antiserum at 1/8000 dilution was utilised as primary

antibody and co-incubated with chlorpromazine The standard

calibration curve with final chlorpromazine concentrations of 0.05,

0.25, 0.5, 1.0, 2.0 and 10.0 ng mL−1was run in PBST.

The pretreatment of the samples (swine liver and chicken)

The sample was first milled for 10–15 min for homogenising One

gram of the chicken was weighed into a polythene tube, and

then 4 mL of the extraction solution was added The mixture was

shaken vigorously for 5 min and centrifuged at room temperature

(20–25◦C) for 10 min at 3000×g Two millilitres of the supernatant

was transferred and mixed with 4 mL of 1 mol L−1NaOH, and then

10 mL of n-hexane was added After being shaken vigorously

for 5 min, the mixture was centrifuged at room temperature

(20–25◦C) for 5 min at 3000× g Five millilitres of the supernatant

was extracted and placed into a 50 mL of round-bottom glass flask

Then the solution was evaporated to dryness at low pressure at

55◦C Then, 1 mL of PBST was added to the flask and the solutionwas treated as the blank sample which was stored at 0–4◦C forfuture use

Optimisation of the blank sample

Three different chicken and swine liver samples were purchasedfrom different supermarkets for preparing blank samples Theabsorbance of the blank samples was tested by the indirectELISA method, as described below The microplates were coatedwith coating antigen cOVA–pefloxacin at 1/1000 by overnightincubation at 4◦C Plates were washed with wash buffer threetimes and blocked with 250µL well−1of blocking buffer, followed

by incubation for 1 h at room temperature Plates were washedthree times again, then the blank samples (50µLwell−1), incubatedwith the antibody (1/8000, 50µL well−1), were added The plates

Detection of chlorpromazine in swine liver and chicken by ELISA www.soci.org

were incubated for 2 h at room temperature, then washed three

times, and goat anti-mouse IgG-HRP (1 : 1000, 100µL well−1) was

added, followed by incubation for 2 h at room temperature Plates

were washed three times and TMB substrate solutions A and B

were added (50µL well−1) in turn, and the plates were incubated

for another 15 min at room temperature The colour development

was halted by adding stopping solution (100µL well−1), and

absorbance was measured at 450 nm Absorbance was compared

with the blank reading of wells with PBST added instead of samples

The selected blank sample was defined according to the standard

curve

The blank sample selected was spiked with chlorpromazine

standard solution in PBST Competitive curves with final

chlorpro-mazine concentrations of 0.05, 0.25, 0.5, 1.0, 2.0 and 10 ng mL−1

were run in PBST and in blank sample to determine the matrix

effect of swine liver and chicken IC50and B0values from the blank

sample curve were obtained by comparing IC50 and B0 values

generated from the PBST buffer solution

RESULTS AND DISCUSSION

Preparation of antigen and characterisation of the antibody

As a small molecule, chlorpromazine has to be connected with

carrier protein in order to be immunogenic The structure of

chlorpromazine (Fig 1) showed that the side chain composed

of N–(CH2)3–N(CH3)2 is a very important structural feature for

this molecule In order to obtain a specific antibody towards

chlorpromazine, it will be important to expose this chain outside

instead of connecting this chain directly to avoid covering the most

important structural feature by carrier protein There is no suitable

functional group in the chlorpromazine molecule to use for linking

with carrier protein To synthesise an immunogen for preparation

of anti-chlorpromazine antibody, acepromazine was chosen as the

starting material because there is acetyl group in the acepromazine

molecule (Fig 2, Scheme 1), which can be used as a linking site

with carrier protein Considering that the acetyl group is located

in a position that is close to the side chain N–(CH2)3–N(CH3)2, it

requires a chain to separate hapten from carrier protein to ensure

that the hapten is uncovered by carrier protein First, acepromazine

reacted with hydroxylamine hydrochloride to form an immediate

(II) (Fig 2, Scheme 1), which was then treated by succinic anhydride

to form activated immediate (III) This derivatisation introduced a

carboxylic acid moiety into the hapten, which was a convenient

functional group for conjugation with a carrier protein The space

arm consisting of seven atoms will be helpful to increase the

reorganisation of antibody to chlorpramazine hapten Starting

from compound (III), both immunogen and coating antigen could

be prepared with different carrier proteins The immunogen was

prepared through a mixed anhydride method to link hapten with

carrier protein BSA (cBSA) (Fig 2) The coating antigen using OVA

as carrier protein was prepared by the DCC method in DMF as

shown in Fig 2 (Scheme 2) The yields of immunogen and coating

antigen obtained from Scheme 1 and Scheme 2 were both more

than 30%

In order to characterise the antibody produced in this

research, the titre, sensitivity and cross-reactivity were determined

according to the indirect competitive ELISA procedure described

above The titre of the antibody determined for the ELISA method

was defined as the reciprocal of the dilution which resulted in

an absorbance value that was twice of the background value

The titre of Mabs developed by the immunisation process

was more than 100 000 for all mice The prepared antibody

0.0 0.2 0.4 0.6 0.8 1.0

Chlorpromazine concentration (log[ppb])

Figure 3 Inhibition curve of anti-chlorpromazine monoclonal antibody

with chlorpromazine as a competitor in PBST.

b Percentage of cross-reactivity is defined as the ratio of the chlorpromazine concentration (ng mL−1) at IC 50 to that of the tested compound ×100.

was evaluated for its binding affinity with chlorpromazine Therepresentative inhibition curves for chlorpromazine tested bythe ELISA method is described in Fig 3, which showed that the

IC50 value of chlorpromazine was 0.73 ppb, indicating excellentsensitivity The MAbs raised in our study showed more than 20times higher sensitivity for chlorpromazine than that raised inpublished research,7 in which chlorpromazine was determined

by an ELISA procedure using the polyclonal antibody obtained

by immunisation of a propionylpromazine–protein conjugate inrabbit, with the detection capability of 20 ppb

The monoclonal antibody demonstrates remarkable specificitytoward chlorpromazine The cross-reactivity of the antibodywith another five structurally relevant compounds (Fig 1) wasmeasured by comparison of the IC50 of structurally relatedcompounds with that of chlorpromazine to determine thespecificity of the antibody As shown in Table 1, the antibody

demonstrates excellent specificity (<1.0%) showing only slight

reactivity with acepromazine among the chemicals listed (1.0%).The result supported our speculation that the N–(CH2)3–N(CH3)2side chain is a key structure for obtaining a specific antibody towardchlorpromazine This may be the reason for the lower binding ofMAbs for promethazine with N–(CH2)2–CHCH3–N(CH3)2 in theside chain, compared with that for acepromazine

J Sci Food Agric 2010; 90: 1789–1795
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa

10.2

Figure 4 Standard calibration curve of indirect competitive ELISA in PBST.

liver and chicken samples

According to the Council Regulation (EEC) No 2377/90,

chlorpro-mazine must not to be detectable in ‘veterinary medicinal products

in respect of all the various foodstuffs of animal origin, including

meat, fish, milk, eggs and honey’, so we detected chlorpromazine

in foodstuffs of animal origin We selected two classic animal

tis-sues from two classic animals commonly fed by chlorpromazine

for validation One was chicken, another was swine liver In

addi-tion, it is reported that pigs are particularly sensitive to the change

of surrounding conditions, so the stress could cause high morality

rates or make pigs produce low-quality meat, pale and soft These

serious adverse events make farmers utilise more sedatives as

feed additives Hence, swine liver samples were also selected to

evaluate the ELISA established in our study in biological system

besides chicken samples Other matrices, such as urine, milk, eggs

and honey, have not been analysed yet

To determine the limit of detection (LOD), 20 batches of blank

swine liver samples and 20 batches of blank chicken samples were

purchased from the local supermarket All the samples have been

tested by LC-MS to make sure there is no chlorpromazine residue

in them

The linear range of the ELISA determined as the

concentra-tion of 20–80% inhibiconcentra-tion of maximal absorbance value was

0.60–2.0 ppb, and the B/B0value in this range was plotted versus

chlorpromazine concentration to obtain standard curve (Fig 4)

samples spiked with chlorpromazine Level ( µg

Average recovery (%)

Inter-assay variation a (%)

Intra-assay variation b (%) Swine liver

The curve obtained showed good linearity (R2 = 0.9983) in this

range Using this curve, the 20 blank samples were analysed ing the ELISA to demonstrate the range of blank matrix effects

us-in the assay As shown us-in Table 2, results of these 20 known

chlorpromazine-free samples gave a mean of 0.10µg kg−1 for

swine liver samples and 0.08µg kg−1for chicken samples The

highest observed blank was 0.30µg kg−1for swine liver samples

and 0.20µg kg−1 for chicken samples As a general rule,27 theLOD is defined as the mean observed chlorpromazine concen-tration plus three times the standard deviations, or the highestobserved chlorpromazine concentration, whichever the greater

As was shown in Table 2, in both cases, the LOD was determined

by the mean plus three standard deviations due to their highervalues compared to the highest observed blank value

The same swine liver and chicken samples spiked by 0.5, 1.0 and

2.0µg kg−1, respectively, were measured by the immunoassay todetermine the variations of the ELISA method As was shown inTable 3, the variation of coefficients were determined to be in therange 9.7–13.0% for inter-assay and 11.0–15.3% for intra-assay,whereas the average recovery rates were in the range 86.2–95.6%,indicating satisfactory accuracy and precision

In our study, the animal tissues analysed were spiked with

chlorpromazine in vitro, in order to evaluate the matrix effect on recovery and variation efficiency of the method However, in vivo,

chlorpromazine is rapidly metabolised Based on the evaluation

of chlorpromazine by JECFA,8the major metabolic pathways arehydroxylation, oxidation, demethylation and glucuronidation Themetabolites varied differently in different species Further work ofour study is to apply the method in the analysis of tissues fromanimals fed by chlorpromazine and specifically demonstrate theantibody with chlorpromazine metabolites in different animals

CONCLUSION

In summary, an immunogen of chlorpromazine was designedand synthesised The monoclonal antibody for chlorpromazinebased on the immunogen was prepared for the first time Theantibody showed high sensitivity with an IC50value of 0.73 ppb,limit of detection of 0.05 ppb and specificity with almost nocross-reactivity towards commonly used sedatives When applied

Detection of chlorpromazine in swine liver and chicken by ELISA www.soci.org

to detect chlorpromazine spiked in swine liver and chicken,

satisfactory accuracy and precision were obtained Hopefully,

the ELISA test method is an alternative to chromatography for

regulatory analysis of chlorpromazine residues in foodstuffs of

animal orgin

ACKNOWLEDGEMENT

This research was supported by the National Natural Science

Foundation of China (No.20675048), the Shandong Natural Science

Foundation (Y2008B31), the National High-Tech Research and the

Development Program of China (863 Program, No 07AA10Z435,

No 2007AA06A407)

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equivalents versus defined daily doses: How to compare

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test for veterinary sedatives and the beta-blocker carazolol in

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8 Evaluation of certain veterinary drug residues in food, in Thirty-eighth

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Chlorpro-mazine – summary report, in EMEA/MRL/111/96-FINAL, The

Euro-pean Agency for the Evaluation of Medicinal Products, Veterinary

Medicines Evaluation Unit (1996).

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11 Cruz-Vera M, Lucena R and Cardenas S, Determination of

phenothiazine derivatives in human urine by using ionic

liquid-based dynamic liquid-phase microextraction coupled with liquid

chromatography J Chromatogr B 877:37–42 (2009).

12 Sobhi HR, Yamini Y and Abadi RHHB, Extraction and determination of

trace amounts of chlorpromazine in biological fluids using hollow

fiber liquid phase microextraction followed by high-performance

liquid chromatography J Pharm Biomed Anal 45:769–774 (2007).

13 Saracino MA, Amore M and Baioni E, Determination of selected phenothiazines in human plasma by solid-phase extraction and

liquid chromatography with coulometric detection Anal Chim Acta

624:308–316 (2008).

14 Mitrowska K, Posyniak A and Zmudzki J, Rapid method for the determination of tranquillizers and a beta-blocker in porcine and bovine kidney by liquid chromatography with tandem mass

spectrometry Anal Chim Acta 637:185–192 (2009).

15 Pujadas M, Pichini S and Civit E, A simple and reliable procedure for the determination of psychoactive drugs in oral fluid by

gas chromatography –mass spectrometry J Pharm Biomed Anal

44:594–601 (2007).

16 Wu HQ, Jin YC and Cai MZ, Simultaneous determination of 10 mental

drugs by gas chromatography –mass spectrometry Chin J Anal

Chem 35:500–504 (2007).

17 Zhang YL, Lu SX and Liu W, Preparation of anti-tetracycline antibodies and development of an indirect heterologous competitive enzyme- linked immunosorbent assay to detect residues of tetracycline in

milk J Agric Food Chem 55:211–218 (2007).

18 Lu SX, Zhang YL and Liu JT, Preparation of anti-pefloxacin antibody and development of an indirect competitive enzyme-linked immunosorbent assay for detection of pefloxacin residue in chicken

liver J Agric Food Chem 54:6995–7000 (2006).

19 Liu W, Zhao CB and Zhang YL, Preparation of polyclonal antibodies

to a derivative of 1-aminohydantoin (AHD) and development of

an indirect competitive ELISA for the detection of nitrofurantoin

residue in water J Agric Food Chem 55:6829–6834 (2007).

20 Zhao CB, Liu W and Ling HL, Preparation of anti-gatifloxacin antibody and development of an indirect competitive enzyme-linked immunosorbent assay for the detection of gatifloxacin residue

in milk J Agric Food Chem 55:6879–6884 (2007).

21 Liu ZQ, Lu SX and Zhao CH, Preparation of anti-danofloxacin antibody and development of an indirect competitive enzyme-linked immunosorbent assay for detection of danofloxacin residue in

chicken liver J Sci Food Agric 89:1115–1121 (2009).

22 Ding K, Zhao CH and Cao ZZ, Chemiluminescent detection of

gatifloxacin residue in milk Anal Lett 42:505–518 (2009).

23 Gajewski KG and Hsieh YHP, Monoclonal antibody specific to a major

fish allergen: parvalbumin J Food Prot 72:818–825 (2009).

24 Stanker LH, Merrill P and Scotcher MC, Development and partial characterization of high-affinity monoclonal antibodies for botulinum toxin type A and their use in analysis of milk by sandwich

ELISA J Immunol Methods 336:1–8 (2008).

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for immunohistochemistry, western blotting, and ELISA J Immunol

Received: 25 January 2010 Revised: 18 March 2010 Accepted: 16 April 2010 Published online in Wiley Interscience: 22 June 2010(www.interscience.wiley.com) DOI 10.1002/jsfa.4015

Comparison of PCR-DGGE and PCR-SSCP

analysis for bacterial flora of Japanese

traditional fermented fish products,

aji-narezushi and iwashi-nukazuke

RESULTS: Viable plate counts in aji-narezushi and iwashi-nukazuke were about 6.3–6.6 and 5.7–6.9 log colony-forming units

g −1respectively In the PCR-DGGE analysis, Lactobacillus acidipiscis was detected as the predominant bacterium in two of three

aji-narezushi samples, while Lactobacillus versmoldensis was predominant in the third sample By the PCR-SSCP method, Lb acidipiscis and Lactobacillus plantarum were isolated as the predominant bacteria, while Lb versmoldensis was not detected.

The predominant bacterium in two of three iwashi-nukazuke samples was Tetragenococcus muriaticus, while Tetragenococcus

halophilus was predominant in the third sample.

CONCLUSION: The results suggest that the detection of some predominant lactic acid bacteria species in fermented fish by cultivation methods is difficult.

c

2010 Society of Chemical Industry

Keywords: fermented fish; denaturing gradient gel electrophoresis (DGGE); single-strand conformation polymorphism (SSCP);

Lactobacillus; Tetragenococcus

INTRODUCTION

In modern Japanese cuisine, sushi is made from vinegar-flavoured

rice combined with seafood It is thought that sushi originates

from the salted and long-fermented fish called narezushi in Japan.

The earliest reference to sushi appears in a code named the

Yoro-Ritsuryo issued in AD 718, and this earliest sushi is postulated to

have been narezushi.1Since that time, narezushi products have

been made from various freshwater fish in several areas located

inland rather than in coastal regions Funazushi, a fermented

crucian carp with cooked rice made near Lake Biwa in central Japan,

is the most famous narezushi, characterised by its strong flavours

and odours Currently, narezushi is also made from marine fish.

For example, aji-narezushi, made from horse mackerel (Trachurus

japonicas) and rice, has been manufactured in the Noto peninsula

in Ishikawa, Japan since the middle of the last century.2

Fish nukazuke, salted and long-fermented fish with rice bran

(nuka, by-product of polished rice), is also one of the traditional

and popular fermented fish products in Japan Puffer fish ovary

nukazuke is a famous nukazuke product, because its deadly poison

is eliminated during long-term salting (2 years) and fermentation

with rice bran (1–2 years).3 However, iwashi-nukazuke, made

from sardine (Sardinops melanostica), is the most reasonable and

popular fish nukazuke Recently, some bioactive substances such

as antioxidants have been reported in iwashi-nukazuke.4

It is well known that lactic acid bacteria (LAB) in fermentedfoods affect not only product quality and preservation5but alsofood functionality, such as improving the intestinal environmentand antihypertensive effects.6,7 Recently, a high content of

γ -aminobutyric acid (GABA) was detected in aji-narezushi.2Production of GABA by LAB, including isolates from traditionalfermented foods, has been reported.8

Determination of the microflora of aji-narezushi and nukazuke using culture-based methods has been reported.2,9During fermentation, LAB and lactic acid increase and the pH valuedecreases (to 4.2 or less) However, the microflora has not yet beenclearly identified at the levels of genus and species Owing tothe known limitations of cultivation methods, many recent studieshave used culture-independent 16S rDNA-based polymerase chainreaction (PCR) techniques, including PCR denaturing gradient gelelectrophoresis (DGGE), to determine the bacterial flora of varioustraditional fermented foods such as fermented milk and soybean

iwashi-∗ Correspondence to: Takashi Kuda, Department of Food Science and

Technol-ogy, Tokyo University of Marine Science and TechnolTechnol-ogy, Minato-ku, Tokyo 108-8477, Japan E-mail: kuda@kaiyodai.ac.jp

Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato-ku, Tokyo 108-8477, Japan

Analysis of bacterial flora of traditional Japanese fermented fish products www.soci.org

products.10 – 12In this study, to clarify the quality and functional

properties of Japanese traditional products of lactic-fermented

fish with rice, we investigated the bacterial flora using a molecular

approach, combining PCR amplification of the V3 region of the

16S rDNA gene and DGGE (PCR-DGGE) Furthermore, we identified

the isolated strains using the culture-based PCR single-strand

conformation polymorphism (PCR-SSCP) method.13

MATERIALS AND METHODS

Samples

In order to carry out the study, aji-narezushi samples were obtained

from three fish shops in Noto, Ishikawa, Japan in August 2008

During aji-narezushi processing, fresh horse mackerel (20–60 g

body weight) were gutted and, after removal of the eyes or whole

head, placed in a barrel with plenty of salt (180–330 g salt kg−1

fish) The salted fish were then desalted in a vat filled with thin

(diluted two to five times) rice vinegar (komesu, approximately

45 g acetic acid L−1) The salting (from 3 days to several weeks)

and desalting (from several seconds to 5 h) periods varied with

each manufacturer The treated fish were stuffed and covered

with cooked rice, scattered with a small amount of Japanese

pepper leaves and red pepper and pickled at ambient temperature

(15–30◦C) The fermentation time was about 2–3 months During

fermentation the lid of the fermenting barrel was kept closed by

stone weights

Three iwashi-nukazuke samples were purchased from retail

shops in Hakusan, Ishikawa, Japan in September 2008 During

iwashi-nukazuke processing, fresh sardines (∼100 g body weight)

were gutted and placed in a barrel with plenty of salt for several

days The salted fish were then washed and pickled in a barrel with

plenty of nuka and moulted rice (koji) for about 1 year at ambient

temperature

For microbiological analysis, some of the whole samples were

separated and collected aseptically Then the remaining samples

were stored at−30◦C for chemical analysis

Chemical analysis

Moisture, salinity, pH and organic acids of the samples were

determined using methods cited in our previous reports.2,14Water

activity was measured with a water activity meter (Pawkit, Decagon

Devices, Pullman, WA, USA)

Viable plate count

Samples (25 g) were emulsified in 225 mL of sterile

phosphate-buffered saline (PBS; 20 mmol L−1KH2PO4, 10 mmol L−1K2HPO4,

pH 7.2) and blended for 60 s (Stomacher 400, Seward, London,

UK) The sample suspensions were diluted in PBS and appropriate

dilutions were spread in duplicate on tryptone soy (TS) agar (Oxoid,

Basingstoke, UK), Gifu anaerobic medium (GAM) agar (Nissui,

Tokyo, Japan), de Man, Rogosa and Sharpe (MRS) agar (Oxoid) and

potato dextrose (PD) agar (Nissui) plates containing 100 mg L−1

chloramphenicol To detect halophilic and halotolerant bacteria,

agar plates containing 100 g L−1NaCl (TS-HS, GAM-HS, MRS-HS

and PD-HS) were also used TS and PD agar plates were incubated

aerobically at 30◦C for 3 days GAM and MRS agar plates were

incubated anaerobically at 30◦C for 5 days using an AnaeroPack

system (Mitsubishi Gas Chemical, Tokyo, Japan) All agar plates

containing high salt (HS) were incubated for 7 days In the case

of aji-narezushi samples, ten colonies each from GAM and MRS

agar plates were selected and restreaked for purification prior to

PCR-SSCP analysis In the case of iwashi-nukazuke the colonies

were selected from GAM-HS and MRS-HS agar plates

Direct extraction of DNA and PCR amplification

DNA from 1 mL of homogenate per sample and the isolates wasextracted using a FastPure DNA kit (Takara, Otsu, Japan) PurifiedDNA was dissolved in ethylendiamine tetraacetic acid (EDTA)buffer (TE buffer) and used as the DNA template in PCR

The following primer pair was chosen for amplification of the V3region of the 16S rRNA gene: forward primer with GC clamp GC-339f (5-CGC CCG CCG CGC CCC GCG CCC GTC CCG CCG CCC CCGCCC GCT CCT ACG GGA GGC AGC AG-3) and reverse primer V3-53r(5-GTA TTA CCG CGG CTG CTG G-3).15This primer set has beenwidely used in DGGE analysis.16PCR amplification was performed

in 100µL reaction mixtures composed of 10 mmol L−1 Tris/HCl(pH 8.3), 50 mmol L−1KCl, 1.5 mmol L−1MgCl2, 50 pmol of eachprimer, 0.2 mmol L−1each of four dNTPs, 2.5 U of Takara Taq DNApolymerase (Takara Bio, Shiga, Japan) and 50 ng of template DNA

To minimise amplification of non-specific products and to obtainlarge amounts of PCR products, ‘touchdown’ PCR was performed,17where the initial annealing temperature was set at 8◦C above theexpected annealing temperature and decreased by 0.8◦C everysecond cycle until the expected annealing temperature (62◦C)was reached (total 20 cycles) and then five additional cycles werecarried out Amplification was carried out using the followingcycle: denaturation at 94◦C for 30 s, annealing for 30 s and primerextension at 72◦C for 10 s in a GeneAmp 9700 thermal cycler(Applied Biosystems, Foster City, CA, USA) Aliquots (5µL) ofPCR products were analysed first by electrophoresis on 20 g L−1agarose gels

DGGE analysis of PCR products

DGGE analysis of PCR amplification products was performed asdescribed previously16using a DCode System apparatus (Bio-RadLaboratories, Hercules, CA, USA) Polyacrylamide gels (80 g L−1acrylamide/bisacrylamide (37.5 : 1 w/w)) in 1× Tris/acetate/EDTAbuffer with a denaturing gradient ranging from 30 to 60%denaturant (100% denaturation corresponds to 7 mol L−1 ureaand 400 mL L−1formamide) were prepared with a Bio-Rad 475gradient delivery system (Bio-Rad Laboratories) Polymerisationwas achieved by adding 9 mL L−1 ammonium persulfate and0.9 mL L−1N,N,N,N-tetramethyl ethylene diamine The gels wereelectrophoresed at a constant voltage of 200 V at 60◦C for 3 h TheDNA fragments were stained with ethidium bromide and washedwith distilled water prior to UV transillumination

The main DGGE fragments were selected for nucleotidesequence determination Each band was excised with a sterilerazor The DNA of each fragment was eluted in 50µL of TEbuffer at 100◦C for 10 min The extracts were reamplified byPCR using the same primers and purified with SUPREC 138-PCR(Takara) according to the manufacturer’s instructions PurifiedDNA fragments were ligated in pT7 blue vectors (Novagen,

Darmstadt, Germany) and transformed into Escherichia coli JM109.

The transformants were grown on Luria-Bertani broth (LB) agar

containing ampicillin and screened by β-galactosidase assay.

Plasmid DNA of selected transformants was isolated using aPlasmid Miniprep kit (Bio-Rad Laboratories) The inserted DNA

sequence, approximately 200 bp of 16S rDNA (E coli position

389–530),18was determined using an Applied Biosystems 3130genetic analyser with a Big Dye Terminator V3.1 Cycle Sequencingkit (Applied Biosystems) To identify the inserted sequences, the

J Sci Food Agric 2010; 90: 1796–1801
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa

BLAST 2.0 algorithm was used to compare the derived sequence

with 16S rDNA sequences in the DNA Data Bank of Japan (DDBJ)

database The DGGE analysis was carried out duplicate

PCR-SSCP analysis of 16S rDNA V3 region

In the PCR-SSCP analysis we used precast polyacrylamide gels

followed by silver staining because of the high sensitivity of silver

staining This method visualises even a small amount of

non-specific amplification product; therefore several PCR primers and

thermal profiles were tested for specificity and differences in PCR

efficiency The primer set, SRV3-1 (5-CGG YCC AGA CTC CTA CGG

G-3) as the forward primer and V3R53 (5-GTA TTA CCG CGG

CTG CTG GC-3), which was designed based on 536R with minor

modifications, as the reverse primer, gave acceptable results.19,20

PCR amplification was performed in 100µL reaction mixtures

composed of 10 mmol L−1 Tris/HCl (pH 8.3), 50 mmol L−1 KCl,

1.5 mmol L−1MgCl2, 50 pmol of each primer, 0.2 mmol L−1each

of four dNTPs, 2.5 U of Takara Taq DNA polymerase (Takara Bio) and

50 ng of template DNA In this analysis, ‘touchdown’ PCR was also

performed, where the initial annealing temperature was set at 6◦C

above the target annealing temperature and decreased by 0.6◦C

every second cycle until the target annealing temperature (61◦C)

was reached and then five additional cycles were carried out at

the target annealing temperature Amplifications were carried out

in a GeneAmp 9700 thermal cycler (Applied Biosystems) using the

following cycle: denaturation at 94◦C for 30 s, annealing in the

temperature regime described above for 30 s and primer extension

at 72◦C for 10 s for ‘touchdown’ cycles and 72◦C for 30 s for the

last five additional cycles

SSCP analysis of PCR products was performed as described

previously.21 Briefly, PCR products were mixed 1 : 2 with

load-ing buffer (980 mL L−1 formamide/10 mmol L−1 EDTA/5 mL L−1bromophenol blue), denatured by heating for 10 min at 100◦C,cooled on ice, loaded on a precast, ready-to-use gel (GeneGelExcel 12.5/24 kit, GE Healthcare, UK) and electrophoresed in aGenePhor electrophoresis unit (GE Healthcare) at 650 V, 25 mAand 5◦C until the bromophenol blue front reached the anodebuffer strip (∼90 min) The gel was stained with a PlusOne DNAsilver staining kit (GE Healthcare) Scanned photographs of SSCPgels were stored as TIFF images

RESULTS AND DISCUSSIONChemical compounds

Table 1 shows the chemical constituents of the fermented fish

products The salinities of aji-narezushi and iwashi-nukazuke were

moderately high, about 60 and 100–160 g kg−1respectively, whiletheir respective water activities were about 0.89 and 0.77 Thepredominant organic acid in all samples was lactic acid, withother organic acids present only at very low levels The lactic acid

content was particularly high (>60 g kg−1) in one aji-narezushi

sample (As-1) The pH of aji-narezushi was lower than 4.3 These

results agree with our previous reports.2,9,14,22

Viable plate count

The viable plate counts of the fermented fish products are

summarised in Table 2 In the aji-narezushi samples, maximum

viable plate counts were 6.5–7.4 log colony-forming units (CFU)

g−1 The viable plate count was lowered by HS In one aji-narezushisample (As-1), viable cells were not detected on TS and TS-HS agarplates It is considered that a richer nutrient condition is required

Organic acids (g kg−1)

Plate count (log CFU g−1) Fermented

TS, tryptone soy agar; GAM, Gifu anaerobic medium agar; MRS, de Man, Rogosa and Sharpe agar; PD, potato dextrose agar; HS, high salt (containing

100 g L−1NaCl); ND, not detected (<2.00 log CFU g−1).

8 9 10

13 14

As-1 As-2 As-3 In-1 In-2 In-3

Figure 1 DGGE analysis of PCR-amplified 16S rDNA fragments from

aji-narezushi (As-1–As-3) and iwashi-nukazuke (In-1–In-3).

for the predominant bacteria The viable counts for yeasts on PD

and PD-HS agar plates were 3.1–4.2 log CFU g−1.

Maximum viable counts of the iwashi-nukazuke samples were

5–6 log CFU g−1 The microbial count of fish nukazuke varies

depending on the manufacturer.22In samples In-1 and In-3 the

counts on the agar plates containing HS were similar to or higher

than those on the agar plates with no added salt Previous reports

indicate that the predominant bacteria in fish nukazuke products

are Tetragenoccocus spp.9 However, no micro-organisms were

detected in sample In-2 on TS-HS and GAM-HS agar plates, though

the viable counts were high on MRS-HS and PD-HS agar plates

Bacterial flora analysed by PCR-DGGE method

For DGGE analysis we selected the V3 region of 16S rDNA as the

target region This region has been widely used in the analysis of

bacterial communities or the identification of isolated bacteria.11,23

PCR products originating from sample preparations were divided

into one to four main fragments by DGGE analysis (Fig 1), with the

banding patterns differing by sample Subsequently, to identify

the main bands, each band was recovered from the DGGE gel

and sequenced The results obtained from clone sequencing are

shown in Table 3

In the case of aji-narezushi, there was only one main band for

sample As-1, which differed from the main bands of the other two

samples Sequencing of the recovered DGGE gels indicated that

the predominant bacteria in sample As-1 and samples As-2 and

As-3 were Lactobacillus versmoldensis and Lactobacillus acidipiscis

respectively Lactobacillus versmoldensis frequently dominates the

LAB populations of raw fermented sausage products,24while Lb.

acidipiscis is isolated from fermented fish products (pla-ra and

pla-chom) made in Thailand.25The difference in predominant LAB

species may be correlated with the difference in TS agar plate

counts of the samples (Table 2)

The DGGE patterns of the iwashi-nukazuke samples indicated

that the predominant bacterium in sample In-1 was

Tetragenococ-cus muriatiTetragenococ-cus, while the main band of sample In-2 was identified

as the chloroplast of rice (Oryza sativa) Interestingly, sample In-3

showed two main bands corresponding to those of both samples

In-1 and In-2

of aji-narezushi and iwashi-nukazuke

Identity (%)

Aji-narezushi

a See Fig 1.

b The main bands are shown in bold type.

Tetragenococcus muriaticus, a moderately halophilic LAB, is

isolated from salted and fermented fish products along with

Tetragenococcus halophilus.26 Although T muriaticus is reported

to be a histamine-forming bacterium,27Satomi et al.28found that

the histidine decarboxylase gene (hdc) is encoded in the plasmid

of T halophilus and suggested that the hdc could be encoded on

transformable elements among LAB

In samples In-2 and In-3 a clear band of rice chloroplast wasdetected In a previous DGGE analysis of fermented plant foodsthe chloroplast was detected as the main band in the earlyfermentation stage.29Ward et al.30also successfully differentiated

Lactococcus lactis subsp lactis and Lc lactis subsp cremoris based

on 16S rRNA sequencing, but Ercolini et al.31 could not use theV3 region from 16S rDNA to identify these two subspecies of

Lc lactis Furthermore, Walter et al.32 also failed to distinguish

Lactobacillus casei and Lactobacillus rhamnosus using DGGE or

BLAST comparisons of V2–V3 sequences The authors suggestedthat differentiation of these species might be possible by usingprimers targeting other regions of 16S rRNA We also thinkthat further DGGE experiments using other regions or LAB-specific regions to clarify the LAB flora, particularly halophilic

or halotolerant LAB, of fermented fish are necessary

Bacterial flora analysed by PCR-SSCP method

PCR-SSCP analysis enables DNA fragments of similar sizes to beseparated according to their configuration (secondary structure).33Targeting the 16S rRNA V3 region, which permits phylogeneticdiscrimination of microbial species, allows for LAB monitoring inthe fermented food microbial community by one profile of bands,where each band corresponds to a different sequence of the 16SrRNA V3 region, i.e one bacterium.13

As shown in the PCR-DGGE analysis, the predominant bacteria

in the fermented fish products were LAB Therefore we isolated

bacterial strains from MRS and GAM agars for aji-narezushi and from MRS-HS and GAM-HS agars for iwashi-nukazuke for the PCR-

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sequencing of aji-narezushi and iwashi-nukazuke

No of isolates

Identification of 16S rDNA sequencing

Aji-narezushi

MRS 2 Lactobacillus acidipiscis

MRS 1 Lactobacillus plantarum

GAM 2 Lactobacillus paralimentarius

GAM 7 Lactobacillus acidipiscis

GAM 1 Lactobacillus plantarum

As-2 MRS 8 Lactobacillus plantarum

GAM 3 Lactobacillus plantarum

GAM 7 Lactobacillus acidipiscis

GAM 2 Lactobacillus casei

GAM 7 Lactobacillus acidipiscis

GAM 1 Tetragenococcus halophilus Iwashi-nukazuke

GAM-HS 10 Tetragenococcus muriaticus

GAM-HS 10 Not amplified (yeasts)

MRS, de Man, Rogosa and Sharpe agar; GAM, Gifu anaerobic medium

agar; HS, high salt (containing 100 g L−1NaCl).

SSCP analysis These agar plates were incubated under anaerobic

conditions

As summarised in Table 4, the V3 region of some isolates from

MRS agar plates was not amplified Although MRS agar is regarded

as a medium for LAB, the cells of the isolates were observed as

oval yeast shapes under a microscope Particularly in the

iwashi-nukazuke samples, all isolates from MRS-HS agar were yeasts.

The diversity of bacterial flora was not very high in the

aji-narezushi samples The predominant LAB isolated from all three

samples using GAM agar plates was Lb acidipiscis In the case

of samples As-2 and As-3, Lb acidipiscis was also detected

as predominant by the non-culture-based PCR-DGGE analysis

(Table 3) On the other hand, Lb versmoldensis, which was shown

to be predominant in sample As-1 by the PCR-DGGE analysis, was

not detected by the PCR-SSCP method Kr ¨ockel et al.24reported

that Lb versmoldensis grows better in MRS broth than on MRS agar

and that its colonies on MRS agar are small Furthermore, a lag

phase of up to 4 days could be observed when it was transferred

from MRS agar to MRS broth.24It is considered that the growth

rate of Lb versmoldensis is slower than that of other LAB such as Lb.

acidipiscis These results suggest that isolation of Lb versmoldensis

under cultivation method conditions is difficult and the population

of Lb versmoldensis was not reflected in the viable cell counts in

Table 2

Lactobacillus plantarum was detected in samples As-1 and As-2,

though this bacterium was not detected by the PCR-DGGE analysis

The composition of GAM agar, which contains liver extract, may

be suitable for growth of Lb plantarum Lactobacillus plantarum

is isolated not only from fermented vegetables but also from

fermented meat and fish.14 Furthermore, it is well known that

Lb plantarum has beneficial activities in fermented foods, such

as high lactic acid production, acid tolerance and bacteriocinproduction.14Other LAB species, Lb casei and T halophilus, were

isolated from sample As-3

In iwashi-nukazuke sample In-1 the predominant bacterium was identified as T muriaticus, while no bacterial colony was detected

in sample In-2 These results are in agreement with those of thePCR-DGGE analysis (Table 3) However, in the case of sample In-3

the predominant isolate was identified as T halophillus, which was

not detected by the PCR-DGGE analysis

As reported above, different results were obtained from thenon-culture-based PCR-DGGE method and the culture-based PCR-SSCP method It is considered that the PCR-DGGE method is moreuseful than the PCR-SSCP method to determine the predominantbacteria and check the growth of starter strains However, a greatervariety of microflora was expressed in the PCR-SSCP method than

in the PCR-DGGE method The non-bacterial (rice chloroplast)band in the PCR-DGGE analysis may hide the bacterial band.Therefore further study of the PCR-DGGE analysis using the V3 andother LAB-specific regions is necessary Furthermore, biochemical

investigation of the LAB strains isolated from aji-narezushi and iwashi-nukazuke is now in progress.

CONCLUSIONS

We studied the bacterial flora of traditional fermented fish

products, aji-narezushi and iwashi-nukazuke, using

non-culture-based PCR-DGGE and culture-non-culture-based PCR-SSCP methods In the

PCR-DGGE analysis, Lb acidipiscis and Lb versmoldenis were detected as the predominant bacteria in aji-narezushi However,

Lb versmoldensis could not be isolated using GAM and MRS agars.

The PCR-DGGE analysis showed that the predominant bacterium

in iwashi-nukazuke was T muriaticus rather than T halophilus.

Some of our results differed from those of previous studies usingcultivation methods Further studies on the detection, isolationand biochemical and fermentation properties of LAB, particularly

Lb versmoldensis, in aji-narezushi are necessary.

ACKNOWLEDGEMENT

This study was supported by a fund from the Ministry of Agriculture,Forestry and Fisheries for research and development projectspromoting the new policies of Agriculture, Forestry and Fisheries(No 2041)

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RESULTS: With the exception of the 4-O-5-dehydrodiferulic acid all known ferulate dehydrodimers, including the recently

described 8-8(tetrahydrofuran) dimer, were identified in the alkaline hydrolyzate of corn stover after chromatographic

fractionation Next to dehydrodimers, 18 cyclobutane dimers made up of ferulic acid and/or p-coumaric acid were identified

by GC-MS of the dimeric size exclusion chromatography fraction Ferulate dehydrotrimers were isolated by using multiple chromatographic procedures and identified by UV spectroscopy, MS and NMR Four trimers were unambiguously identified

as 5-5/8-O-4-, 8-O-4/8-O-4-, 8-8(aryltetralin)/8-O-4-, and 8-O-4/8-5-dehydrotriferulic acids, a fifth tentatively as

8-5/5-5-dehydrotriferulic acid.

CONCLUSION: The formation of ferulate dehydrotrimers is not limited to reproductive organs of grasses but also contribute to network formation in the cell walls of vegetative organs Although radically coupled hydroxycinnamate dimers and oligomers were in the focus of researchers over the last decade, the earlier described cyclobutane dimers significantly contribute to cell wall cross-linking.

c

2010 Society of Chemical Industry

Keywords: ferulic acid; p-coumaric acid; dehydrotrimer; dehydrotriferulic acid; cyclobutane dimers; forage digestibility

INTRODUCTION

Forage digestibility and hence quality is widely controlled by

the cell walls in the plant organs Structural and matrix

polysac-charides, lignin, and proteins are the major constituents of the

walls Cell wall composition and interactions between cell wall

polymers mediated by, for example, polysaccharide or

polysac-charide–lignin cross-links are important factors limiting their

digestibility.1 – 6In grasses, hydroxycinnamates, particularly

feru-late and p-coumarate, are minor constituents of the cell wall While

p-coumarates are mostly bound to lignin7and only to a lesser

de-gree to arabinoxylans,8,9ferulates are primarily acylating the O-5

position of arabinose side-chains in arabinoxylans.10Radical- and

light-induced coupling reactions of esterified ferulates lead to the

formation of dimeric ferulate cross-links between cell wall

arabi-noxylans These dimers are known as dehydrodiferulates (radical

coupling) or cyclobutane ferulate dimers (light-induced coupling)

More recently, dehydrotriferulates and dehydrotetraferulates were

isolated from corn bran.11 – 15Theoretically, these compounds can

cross-link up to four polysaccharide chains forming a strong

network in the cell wall Ferulates can also cross-couple with

monolignols.16,17Thus, they are co-polymerized into lignins18,19

and cross-link arabinoxylans with lignins Although radicals can

easily be generated from p-coumarate, radically formed dimers

of p-coumarates have not been identified from plant materials.

Radical transfer reactions with other phenolics in the cell wall

are discussed to explain these findings.20 Cyclobutane dimers

of p-coumarates and mixed cyclobutane dimers of ferulates and p-coumarates, however, were identified in different grasses.21 – 23

As these compounds are formed by a photochemical mechanismthe formation of the cyclobutane dimers requires sunlight duringplant growth The formation of these compounds is thereforesupposed to vary strongly depending on the localization of theconsidered organ or tissue in the plant

While ferulate oligomers such as trimers were isolated formcorn bran they were not yet identified in vegetative organs, e.g.stems or leaves, widely used as forages The aim of this studywas to demonstrate that ferulate oligomers, especially ferulatetrimers, are not exclusively involved in cell wall cross-linking ofreproductive organs but also occur in vegetative organs, thushaving a potential influence on forage digestibility

∗ Correspondence to: Mirko Bunzel, Department of Food Science and Nutrition,

University of Minnesota, 1334 Eckles Avenue, St Paul, MN 55108, USA E-mail: mbunzel@umn.edu

a Department of Biochemistry and Food Chemistry, University of Hamburg,

Grindelallee 117, 20146 Hamburg, Germany

b Department of Food Science and Nutrition, University of Minnesota, 1334 Eckles

Avenue, St Paul, MN 55108, USA

05001000150020002500mV

Alcalase 2.4 L (EC 3.4.21.62, from Bacillus licheniformis, 2.4 AU

g−1) was kindly donated by Novo Nordisk (Bagsvaerd,

Den-mark) Bio-Beads S-X3 was from Bio-Rad Laboratories (Munich,

Germany), the solvent resistant BioBeads size exclusion

chro-matography (SEC) glass column ECOPLUS from Kronlab (Sinsheim,

Germany) Sephadex LH-20 was from Amersham Pharmacia

Biotech (Freiburg, Germany) SEC and Sephadex LH-20

chro-matography instrumentation (L-6000 pump, L-7400 UV

detec-tor) was from Merck/Hitachi (Darmstadt, Germany) Phenyl-hexyl

HPLC-columns were purchased from phenomenex

(Aschaffen-burg, Germany) Phenyl-hexyl-RP-HPLC was carried out using

either of the following instrumentations: L-6200 intelligent pump,

T-6300 column thermostat, L-7400 UV detector or L-7150

in-telligent pump, L-7300 column oven, L-7455 photodiode array

detector (Merck/Hitachi, Darmstadt, Germany) HPLC-MS

instru-mentation was from Hewlett-Packard (Waldbronn, Germany): HP

Series 1100: autosampler G1313, pump G 1312A, mass

spec-trometer G 1946A, photodiode array detector 1314A Nuclear

magnetic resonance (NMR) experiments were performed on a

Bruker DRX-500 (Rheinstetten, Germany) instrument Gas

chro-matography–mass spectroscopy (GC-MS) was carried out on a

Trace 2000 GC coupled to a PolarisQ ion trap mass

spectrome-ter (ThermoQuest, Thermo Scientific, Dreieich, Germany) using a

HP-5-MS fused-silica capillary column (Hewlett-Packard)

Plant material and sample preparation

Seeds (DK 233) were made available by Monsanto Agrar

Deutschland GmbH (D ¨usseldorf, Germany) The plant material

was provided by Professor F Schwarz and Dr F Zeller from the

Technical University of Munich, Department of Animal Nutrition

The plant material was seeded on 28 April 2004 in a field trial

in Freising, Germany, and harvested on 27 September 2004 The

dry matter content of the grains at the time of harvest was

640 g kg−1, representing a typical maturity stage used for the

production of silage The duration of sunshine from sowing to

harvest was calculated as 1226 h Immediately after harvesting,

the cob was removed, and the corn stover was coarsely chopped,

freeze-dried, and stepwise ground to pass a 0.5 mm screen Dried

material (280 g) was washed five times with water (3 L each,

stirring for 10 min) followed by centrifugation The residue was

consecutively extracted in a Soxhlet apparatus for 8 h with ethanol

and 6 h with acetone followed by a drying step Residual material

(200 g) was suspended in a phosphate buffer pH 7.5, and proteins

were partially degraded by using the protease alcalase (30µLenzyme g−1dry material, 30 min at 60◦C, continuous agitation).The residue was centrifuged, washed with hot water, 95% (v/v)ethanol, and acetone, and dried

Alkaline hydrolysis and extraction

Alkaline hydrolysis and extraction was performed according to

a previously described procedure.12In brief, extracted and driedstover (165 g) was saponified (NaOH (2 mol L−1); 20 mL NaOH g−1stover) under nitrogen, protected from light and under continuous

stirring for 18 h Following acidification of the mixture (pH <

2), liberated phenolic acids were extracted into diethyl ether.Ether extracts were extracted with NaHCO3 solution (50 g L−1).

After acidification (pH < 2) of the combined aqueous layers the

phenolic acids were re-extracted into diethyl ether Ether extractswere dried over Na2SO4, evaporated to dryness and re-dissolved

in tetrahydrofuran (10 mL)

Fractionation of phenolic acids

SEC was carried out by using Bio-Beads S-X3 (gel bed: 1.5 cm×

95 cm) swollen in tetrahydrofuran which was also used as mobilephase Hydrolyzate dissolved in tetrahydrofuran (about 200 mg in

500µL) was applied to the column The flow rate was maintained

at 0.25 mL min−1for 360 min, increased to 0.5 mL min−1between

360 and 395 min and further increased to 0.75 mL min−1until allmaterial was eluted Fractions were collected according to thechromatogram monitored at 325 nm (Fig 1) Fractions B2 and B3from 20 runs were pooled, dried under a stream of nitrogen, re-dissolved in methanol (MeOH)/water 50/50 (v/v) (ultrasonic bath,addition of a few drops acetone to improve solubility), and usedfor Sephadex LH-20 chromatography

Sephadex LH-20 chromatography was performed as describedpreviously11with some minor modifications Fraction B2 (386 mg)was separated in two Sephadex runs whereas only a portion

of fraction B3 (875 mg) was separated in a single run In brief,the sample was applied to the column (gel bed: 2.5 cm ×

85 cm) pre-conditioned with aqueous trifluoroacetic acid (TFA)(0.5 mmol L−1)/MeOH 95/5 (v/v) Elution was carried out as follows:(1) elution with TFA (0.5 mmol L−1)/MeOH 95/5 (v/v) for 72 h, flowrate: 1.5 mL min−1; (2) elution with TFA (0.5 mmol L−1))/MeOH50/50 (v/v) for 72 h, flow rate: 1.0 mL min−1; (3) elution with TFA(0.5 mmol L−1)/MeOH 40/60 (v/v) for 65 h, flow rate: 1.0 mL min−1;(4) rinsing step with TFA (0.5 mmol L−1)/MeOH 10/90 (v/v).Detection was carried out at 280 and 325 nm Fractions werecollected over 12-min periods, combined according to the

J Sci Food Agric 2010; 90: 1802–1810
2010 Society of Chemical Industryc www.interscience.wiley.com/jsfa