Sensory evaluation
Trang 1Sensory and Nutritive Qualities of Food
Application of Quality Index Method (QIM)
Scheme in Shelf-life Study of Farmed
Atlantic Salmon (Salmo salar)
ABSTRACT: Salmon (Salmo salar) was stored in ice up to 24 d, and changes during storage were observed with
sensory evaluation using the Quality Index Method (QIM), and Quantitative Descriptive Analysis (QDA), total viable counts (TVC), hydrogen sulfide (H 2 S)-producing bacteria, and instrumental texture measurements (com-pression test) Maximum storage time in ice was determined with QDA and fat content by Soxhlet extraction A high correlation between QIM and storage time in ice was found Storage time could be predicted with ± 2 d TVC increased exponentially with storage and was dominated by H 2 S-producing bacteria after 20 d in ice, which was the maximum storage time Texture measurements indicated softening of salmon flesh with storage.
Keywords: sensory evaluation, quality of salmon, fish freshness, shelf life
Introduction
FRESHNESS IS ONE OF THE MOST IMPORTANT ASPECTS OF FISH, AND
because of consumer preferences, there is a strong tendency
to select very fresh fish (Luten and Martinsdottir 1997) Sensory
evaluation is the most important method for freshness and
qual-ity assessment in the fish sector (Hootman 1992) The world’s
production of farmed salmon increased between 1990 and 1997,
from 540,000 tons to almost 1,400,000 tons per year (FAO 2000)
In 1997, 38% of the salmon produced in the world was Atlantic
salmon (Salmo salar) Because of the increased trade between
countries, purchases are often performed on unseen lots, and
there is a need for a good freshness grading system for salmon,
such as the Quality Index Method (QIM) This method is a
sea-food freshness quality grading system, which is used to assess
fish freshness in a rapid and reliable way QIM is based upon a
scheme originally developed by the Tasmanian Food Research
Unit (Bremner 1985) The method has to be adapted to each fish
species To date, the system incorporates fresh herring (Clupea
harengus) and cod (Gadus morhua) (Jonsdottir 1992; Larsen and
others 1992), red fish (Sebastes mentella/marinus) (Martinsdottir
and Arnason 1992), Atlantic mackerel (Scomber scombrus), horse
mackerel (Trachurus trachurus) and European sardine (Sardina
pilchardus) (Andrade and others 1997), brill (Rhombus laevis),
dab (Limanda limanda), haddock (Melanogrammus aeglefinus),
pollock (Pollachius virens), sole (Solea vulgaris), turbot
(Scophtal-mus maxi(Scophtal-mus) and shrimp (Pandalus borealis) (Luten 2000),
gilt-head seabream (Sparus aurata) (Huidobro and others 2001), and
farmed salmon (Salmo salar) (Sveinsdottir and others 2001) QIM
has several unique advantages, including estimation of past and
remaining storage time in ice (Botta 1995; Hyldig and Nielsen
1997; Luten and Martinsdottir 1997)
The maximum storage time of fish can be determined by
sen-sory evaluation of cooked samples The Quantitative Descriptive
Analysis (QDA) (Stone and Sidel 1985) is a sensory method,
which may be used for the determination of maximum shelf life
in addition to a detailed description of the sensory profile for a
product With the QDA, all detectable aspects of a product are
described and listed by a trained panel The list is then used to
evaluate the product, and the panelists quantify the sensory as-pects of the product using an unstructured scale The end of shelf life is the result of unpleasant sensory characteristics
most-ly due to bacterial growth The amount of bacteria on newmost-ly caught fish can vary greatly, normally ranging from 102 to 107 cfu/
cm2 (Liston 1980) The most important seafood spoilage bacteria are characterized by their ability to produce H2S and reduce trim-ethylamine oxide (TMAO), which has been used for their specific determination Capell and others (1997) found counts of H2 S-producing bacteria closely associated with the rejection of
sever-al fish species, irrespective of the temperature and atmosphere Microbial metabolites have low odor thresholds, and during fish spoilage, the concentrations of sulfur compounds, short-chain acids, alcohols, sulfur compounds, and amines increase (Olafs-dottir and Fleurence 1997)
In raw fish, the texture softens during chilled storage
(Anders-en and others 1995; Ein(Anders-en and Thomass(Anders-en 1998) because pro-teolytic enzymes break down the muscle structure (Andersen 1995) The fat content of fish flesh appears to influence the tex-ture When the fat content is high, the flesh is softer (Andersen and others 1994), and juiciness increases (Einen and Thomassen 1998) The total lipid content of farmed salmon is often up to double the content found in wild salmon (Moe 1990) and has been reported varying from 12% to 19% (Hafsteinsson and oth-ers 1998; Refsgaard and othoth-ers 1998)
The aim of this work was to perform a shelf-life study with
farmed Atlantic salmon (Salmo salar) and characterize the
chang-es in frchang-eshnchang-ess with the Quality Index Method (QIM) scheme for raw salmon and the Quantitative Descriptive Analysis (QDA) for cooked salmon Furthermore, the goal was to compare the
senso-ry analysis to microbial counts (total viable counts and H2 S-pro-ducing bacteria) and instrumental texture measurements (com-pression test)
Materials and Methods Salmon
The salmon was obtained from the fish farm Silungur ehf
Trang 2(Vogar, Iceland) The salmon had been fed various types of the
feed blend “Gull” (Gull 3, 4, 6, 8, 10, 12, depending on the age of
the salmon) from Fodurblandan hf (Reykjavik, Iceland) The
blend contained 40% protein, 16% carbohydrates, and 25% to
30% fat The salmon were starved for 2 wk and then slaughtered
with carbonic acid After slaughtering, the salmon were gutted,
bled, gills cut through, and the salmon were then rinsed in
run-ning water for 30 min, followed by chilling to 0 °C in slush ice (0 to
–1 °C) before icing in boxes The salmon weighed 3 to 4 kg The
fish were slaughtered before sexual maturity in 8 batches
(Octo-ber/November 1999) and stored up to 24 d at 0 to 2 °C in iced
boxes until analyzed A total of 50 salmon were used in the
exper-iment Eleven were used for training the sensory panel Salmon
stored 1, 2, 4, 8, 11, 13, 15, 17, 19, 20, 21, 22, and 24 d in ice were
analyzed during the shelf-life study Three salmon from each
storage day were analyzed with QIM; thereof 2 were used for
QDA, microbial counts, texture measurement, and fat analysis
(Figure 1)
Sensory evaluation
Quality Index Method
The QIM scheme for salmon lists quality attributes for
ap-pearance/texture, eyes, gills, and abdomen and descriptions of
how they change with storage time Scores were given for each
quality attribute according to the descriptions, ranging from 0 to
3 Very fresh fish normally received the score 0, with scores
in-creasing with storage time The scores given for all the quality
at-tributes are summarized by the Quality Index, which increases
linearly with storage time in ice The sensory evaluation of each
attribute was conducted according to Martinsdottir and others
(2001)
Prior to the shelf-life study, the QIM scheme for farmed
salm-on (Sveinsdottir and others 2001) was revised, as it did not
in-clude a parameter for the textural state of rigor mortis
Addition-ally, 1 score was added for color/appearance of the skin Changes
were made in the setup of the scheme and selection of words to
describe the parameters in the scheme, mainly to make each
de-scription more precise and to facilitate the QIM assessment
Twelve trained panelists of the Icelandic Fisheries
Laborato-ries sensory panel participated in the sensory evaluation with
QIM Members had several years of experience in evaluating fish
freshness Prior to the shelf-life study, the panel was trained in
applying the QIM scheme in 2 sessions The scheme was
ex-plained to the panel while observing salmon of different
fresh-ness categories The panel used the scheme to assess 6 to 9
salm-on from 2 to 3 different storage days per sessisalm-on during the
shelf-life study The salmon was placed on a clean table 30 min
before the evaluation The side where the gills had been cut
through was facing down Each salmon was coded with 3 random
digit numbers All observations of the salmon were conducted
under standardized conditions, with as little interruption as
pos-sible, at room temperature, and under white fluorescent light
Quantitative Descriptive Analysis
The QDA, introduced by Stone and Sidel (1985), was used to
assess cooked samples of salmon An unstructured scale (0 to
100%) was used on a list of words describing odor, flavor,
appear-ance, and texture
Twelve panelists of the Icelandic Fisheries Laboratories´
sen-sory panel participated in the QDA of the cooked salmon They
were all trained according to international standards (ISO 1993),
including detection and recognition of tastes and odors, training
in the use of scales, and in the development and use of descrip-tors The members of the panel were familiar with the QDA method and experienced in sensory analysis of salmon Two ses-sions were used for training of the panel using salmon of differ-ent freshness categories Sensory evaluation of the cooked
salm-on was performed parallel to the QIM assessment Each panelist evaluated duplicates of samples from 2 to 3 different storage days The fish was served in a random order during 2 sessions for each day of the sensory evaluation
All sample observations were conducted according to interna-tional standards (ISO 1988) Twelve samples collected from each salmon with skin came from the loin part, ranging from the spine
to 2 cm below the lateral line The samples were coded with 3 ran-dom digit numbers and cooked at 95 to 100 °C for 7 min in a pre-warmed oven (Convotherm Elektrogeräte GmbH, Eglfing, Ger-many) with air circulation and steam and then served to the panel
Microbial counts
Skin samples were collected before all other analysis by cut-ting 2 x 7.5 cm2 skin strips from 1 side of the fish and placed in a Stomacher containing 50 mL Butterfield´s Buffer solution (APHA 1992) Blending was done in a Stomacher 400 for 1 min Flesh samples were collected after QIM evaluation from the other side
of the salmon The skin was washed with alcohol and removed with a sterilized scalpel The flesh under the skin was collected, and after mincing, 25 g were weighed into a stomacher bag con-taining 225 g Butterfield´s Buffer solution to obtain a 10-fold di-lution Blending was done in a Stomacher for 1 min Further 10-fold dilutions were made as needed Total viable counts (TVC) and selective counts of H2S-producing bacteria were done on iron agar (IA) by the pour plate technique with an overlay as de-scribed by Gram and others (1987) The plates were incubated at
22 °C for 3 d Bacteria forming black colonies on this agar produce
H2S from sodium thiosulfate and/or cysteine
Instrumental texture measurements
One sample from each fish was measured in a Texture
Analyz-er (TA.XT2; Stable Micro System, Surrey, England) using a com-pression test The salmon was filleted, skin removed, and sam-ples collected transversely behind the dorsal fin Samsam-ples were cut right above the lateral line, 2.5 cm in length and width, and 2.2 ± 1.4 cm in height The samples were then covered with plas-tic and stored in a refrigerator at 4 to 5 °C until measured (within
3 to 5 h) using an aluminum compression plate (SMSP/100) Samples were compressed to 80% of the sample height at a con-stant speed (0,8 mm/s) with a 100 g concon-stant force The trigger force was set at 5 g and the registration rate to 200 PPS (registra-tions per s)
Fat content
Samples were collected according to a method recommended
by the Norwegian General Standardizing Body or (1994), the Norwegian Quality Cut (NQC) The samples were vacuum packed and stored at –20 °C until analyzed (within 10 d) The fat content was determined with the Soxhlet method (AOAC 1990) with modification described in the IFL´s method manual for chemical analysis (IFL 1999) using the solvent petroleum ether
Data analysis
The QI was treated with analysis of variance (ANOVA, 2-factor without replication) to analyze if a difference existed within a group (QI given for each salmon within a storage day and QI
Trang 3giv-Sensory and Nutritive Qualities of Food
en by each judge assessing salmon within a storage day) The
equation of best fit and the correlation coefficients (r) of QI, total
viable count, H2S-producing microbes on salmon skin and flesh,
and instrumental texture parameters against storage time in ice
were calculated using Microsoft® Excel 2000 (Microsoft
Corpora-tion, Redmond, Wash., U.S.A.)
Data from QDA was treated in HyperSense© (Version 1.6;
1996 Icelandic Fisheries Laboratories, Reykjavik, Iceland)
Inter-action of panelists and samples was assumed, and statistical
analysis was performed using 2-factor design with interaction in
the analysis of variance (ANOVA) The program calculates
multi-ple comparison using Tukey´s test Multivariate comparison of
different attributes in QIM and QDA was conducted in the
statis-tical program Unscrambler® (Version 6.1; CAMO, Trondheim,
Norway) with principal component analysis (PCA) Predictability
of QI was analyzed using partial least square regression (PLS)
with full cross validation The average QI for each storage day,
in-cluding assessment of 3 salmon, was used for this analysis The
root mean square error of prediction (RMSEP) was calculated for
the model (the prediction error in original units) Bias is the
aver-aged difference between predicted and measured Y-values for all
samples in the validation set The standard error of performance
(SEP) is the precision of results corrected for the bias From a
PLS2 model, the initial variance (signal) at zero PCs and the
re-siduals variance (noise) after optimal PCs were plotted as a
sig-nal to noise (S/N) ratio for each panelist and for each word (Mar-tens and Mar(Mar-tens 2000) The significance level was set at 5%, if
Table 1—The QIM scheme for farmed salmon Revised from Sveinsdottir and others (2001)
The fish is yellowish, mainly near the abdomen 2
* Examine the side where the gills have not been cut through
Figure 1—Sampling plan for measurements in the shelf-life study of salmon at the Icelandic Fisheries Laborato-ries in November 1999
Trang 4not stated otherwise.
Results and Discussion Sensory analysis
Quality Index Method
The sum of scores evaluated according to the QIM scheme
(Table 1) was presented as the Quality Index (QI) The QI was
cal-culated for each storage day of sampling and formed a linear
re-lationship with time (Figure 2)
High correlation between the average QI and days in ice was
obtained with a slope of 0.692 The slope was different from the
slope observed by Sveinsdottir and others (2001) using the QIM
scheme for salmon, presumably because of the revision of the
scheme prior to this shelf-life study, including the addition of 2
score attributes The aim when developing QIM scheme for fish
is to have the regression line begin at the origin (0,0), which was
not reached here, since the intercept was at 1.568 If the line was
forced through the origin, the correlation between the average QI and days in ice became lower (R2 = 0.933) The QIM scheme gave the assessors the opportunity to choose between scores ranging from 0 to 3 but never a negative number, therefore, allowing for residuals above zero but not below
The difference between salmon of the same storage time in ice was in some cases significant The results were analyzed with partial least square regression (PLS) to examine how well the QI could predict the storage time in ice (Figure 3)
The standard error of performance (SEP) value for the QI was 2.0 (Figure 3) The SEP may be used to evaluate the precision of the predictability of the QI Since the QI was the sum of 11 at-tributes evaluated in the QIM scheme, a normal distribution could be assumed (O´Mahony 1986) Esbensen and others (1998) stated that 2*SEP could be regarded as a 95% confidence interval assuming normal distribution Therefore, it can be as-sumed that the QI of a batch (if 3 salmon were assessed) could be used to predict the storage time with ± 2.0 d It could be assumed that including more salmon in the assessment of each batch might reduce this interval, as observed by Sveinsdottir and oth-ers (2001), where including 5 salmon per storage day gave a SEP value of 1.4
There was a variation in the QI obtained by different panel-ists (Figure 4) The variation increased with storage time, indicat-ing that the panelists were in better agreement when analyzindicat-ing very fresh salmon with the QIM scheme at the beginning of stor-age compared to the not-so-fresh salmon at later ststor-ages There was a tendency for some of the panelists to score either higher or lower than the average score obtained throughout the storage time The variation between the panelists in this study, which were trained during 2 1-h sessions, was comparable to the varia-tion between panelists trained during 6 1-h sessions (Sveinsdot-tir and others 2001) in a similar study This indicated that the 2
Figure 2—Quality Index of salmon Averages over each day
of storage analyzed against days in ice.
Figure 3—PLS1 modeling of QIM data from salmon stored
in ice using full cross validation: Measured against
pre-dicted Y values Average QI for each storage day based
on assessment of 3 salmon used to predict storage time
in d.
Figure 4—Average QI of salmon with storage in ice, as given by each panelist (1 through 12)
Trang 5Sensory and Nutritive Qualities of Food
sessions were sufficient training for the panel
QIM assumes the scores for all quality attributes increase with
storage time in ice (Figure 5)
The average texture score was determined by pressing a
fin-ger on the spine muscle and observing how the flesh recovered
according to Martinsdottir and others (2001) The scores were
around 0 at storage day 1, as the salmon was in rigor Propagation
of rigor caused the muscle to relax again, and through storage in ice, the flesh became soft due to autolysis influenced by both fish muscle enzymes and microbial enzymes (Gill 1995; Nielsen 1995) The skin became softer or less springy after 17 to 20 d, where the average score increased from 1 to 1.5 The average score of skin odor reached only 2 at the end of the storage time The score 3 (rotten) was used rarely by panelists At the
begin-Figure 5—Average scores of each quality attribute assessed with QIM scheme for salmon stored in ice against days
in ice
Trang 6ning of the storage time when the salmon was very fresh, the
odor was described as fresh seaweed or neutral, then a
cucum-ber-like odor dominated the salmon skin odor During later
stag-es, the odor was described as sour and finally as rotten Freshly
caught fish generally contains low levels of volatile compounds
like 2,6-nonadienal, which has a very characteristic cucumber
aroma and a low odor threshold (0,001 ppb), which contribute to
fresh-like odors Short-chain acids, alcohols, amines, and sulfur
compounds from microbial activity probably caused the sour
and rotten odor (Olafsdottir and Fleurence 1997) The average
scores for other quality attributes like skin and all the attributes
for eyes and gills increased throughout the storage time The
av-erage scores for quality attributes of abdomen were very low
un-til after 8 d of storage when they began to increase with the
stor-age time
Quantitative Descriptive Analysis
The positive attributes for flavor of salmon were described as
characteristic salmon, metallic, sweet, and oily flavor on a scale
ranging from 0 to 100% Average scores for most positive flavor at-tributes did not change for the 1st 17 to 19 d of storage but de-creased thereafter (Figure 6) The average scores of sweet and metallic were between 20 and 50 through the storage time, but for salmon stored 21 d, it went below 20 For characteristic salmon flavor and oily flavor, the difference was clearer The scores were between 50 and 70 until 22 d, when they dropped below 40 Scores for the negative attributes, sour, rancid, and musty/ earthy (Figure 6) were low, approximately 0 to 20 for the 1st 17 to
20 d of storage Thereafter, the scores increased, especially sour flavor scores, which were around 50 after 22 d Rancid and musty/earthy flavor reached only 30 after 22 d The feed of farmed salmon often contains carotenoids (Moe 1990), which have been considered to play an important role in protecting
lip-id tissues from oxlip-idation (Burton and Ingold 1984) This may have been the reason for the low rancidity scores The increasing rancid flavor observed during the last storage day might corre-spond to a train-oily flavor reported by Milo and Grosch (1996), who found their nonfresh cooked salmon samples to be fatty and train-oily smelling According to their findings, the rancid flavor
in salmon was caused by formation of volatile oxidation products such as aldehydes and ketones They analyzed various odorants
in salmon of different freshness (stored 26 wk at -60 °C (fresh) and -13 °C (not fresh)) They found propionaldehyde and (Z)-1,5-octadien-3-one as the most potent high-volatile odorants in cooked fresh salmon samples The odor of those compounds was described as sweet and metallic, respectively Odor is a part of the overall flavor, and those compounds may therefore have been responsible for the sweet and metallic flavor of the cooked salmon in this study A mixture of odorants in the cooked salmon might be responsible for the characteristic salmon flavor Milo and Grosch (1996) detected various odorants from cooked
salm-on (fresh), and the characteristic salmsalm-on odor was caused by compounds like propionaldehyde and acetaldehyde (sweet), hexanal and (Z)-3-hexenal (green), methional (boiled potato-like), dimethyl trisulfide (cabbage-potato-like), and 1-octen-3-one (mushroom-like) The oily flavor and odor might have been due
to (Z,Z)-3,6-nonadienal as it was described as fatty and green
Difference for most QDA attributes was generally only ob-served after 20 d in ice (Table 2) Data from day 24 was kept out
Figure 6—Changes in flavor attributes of cooked salmon (average scores) against storage of the raw salmon in ice observed by a trained QDA panel
Figure 7—Loadings in PCA of salmon data including all
quality parameters assessed in QDA of cooked salmon and
storage time in ice f = flavor, o = odor
Trang 7Sensory and Nutritive Qualities of Food
of the analysis because the salmon had utterly exceeded the
lim-its of acceptance When the salmon had been stored for 21 d in
ice, a part of the panel refused to taste the samples after
smell-ing the salmon This strongly indicated that after 20 d of storage
in ice, salmon was—according to sensory evaluation—no longer
fit for human consumption This was in agreement with previous
studies Sveinsdottir and others (2001) concluded that 20 to 21 d
was the maximum storage time in ice for salmon Magnussen and
others (1996) observed sensory changes in cooked salmon stored
7, 14, and 21 d and found minor differences between 7 and 14 d,
but the overall quality was greatly reduced after 21 d of storage
Lande and Rørå (1999) analyzed the flavor, odor, and overall
ef-fects in cooked salmon Minor changes were observed with
stor-age time up to 18 d in ice, however, they did not continue the
sensory evaluation of cooked salmon after the 18 d
Differences were observed among panelists for each QDA at-tribute This is a well-known phenomenon in sensory evaluation The main types of differences among assessors may be caused
by confusion about attributes, individual differences in sensitiv-ity to certain sensations, individual differences in the use of the scale, or individual differences in precision (Næs and others 1994) Various ways have been discussed to detect and handle such differences among assessors (Næs 1990; Næs and Solheim 1991; Næs and others 1994) The noise to signal ratio may be ob-served to decide how to treat the difference among assessors (Sveinsdottir and others 2001)
When the results were analyzed with PCA, the variable d in ice contributed to PC1, and a clear grouping was found between positive and negative sensory attributes on each side of the PC1-axis (Figure 7) Negative parameters became more evident in
Figure 8—Total viable count and H 2 S-producing microbes on skin and in flesh of salmon stored in ice
Figure 9—Correlation between bacterial counts on skin and Quality Index of salmon stored in ice
Trang 8salmon stored longer in ice as they were grouped with the
param-eter d in ice, while the positive paramparam-eters became less evident
The negative attributes described salmon at the end of the
stor-age time similarly Discoloration appeared to become slightly
more evident with storage, but the texture parameters evaluated
in cooked salmon contribute very little to PC1 and therefore do
not appear to change with storage time, contrary to the texture of
raw salmon
Microbial counts
The total viable counts (TVC) on skin and in flesh increased
exponentially with storage time (Figure 8) A similar pattern was
noted for bacterial counts on skin and QI with storage time, as
salmon of low bacterial counts also received low scores in QIM A
high correlation was established between QI and TVC on skin,
and the same was seen for H2S-producing bacteria, which
in-creased proportionally to the TVC at the later stages of storage
(Figure 9)
At the beginning of storage, the TVC on skin was
approxi-mately 103 cfu/cm2, which is not unusual for newly caught fish
(Liston 1980) Very few H2S-producing microbes were a part of the
initial microflora (< 10 cfu/cm2), but their proportion of the TVC
increased with storage time The TVC (mainly H2S-producing
mi-crobes) on salmon skin was 108 cfu/cm2 after about 20 d of
stor-age The bacterial counts in salmon flesh were lower than those
on the skin Newly slaughtered salmon contained TVC around 10
cfu/g in flesh The flesh of healthy live or newly caught fish is
sterile because the immune system of the fish prevents the
bac-teria from growing in the flesh, but when the fish dies, the
im-mune system collapses, and during storage, bacteria invade the
flesh (Gram 1995) After 20 d of storage in ice, the TVC was 105
cfu/g As for the microbial growth on salmon skin, the H2
S-pro-ducing bacteria dominated the bacterial flora at the end of stor-age Counts of H2S-producing bacteria were very low (below 10
Figure 10—Typical compression curve of the instrumental texture measurements of a salmon sample in the shelf-life study, measured in the TA.XT2 Texture Analyzer (SMS) Hardness = H1, Resilience = A2/A1, where H1 equals the maximum force, A1 equals the area under the curve from
b e g i n n i n g o f m e a s u r e m e n t u n t i l m a x i m u m f o r c e i s reached, and A2 equals the area under the curve from maximum force until the force has reached zero again.
Table 2—Sensory scores of attributes of cooked salmon assessed by QDA
in ice flavor (+) flavor (+) flavor (+) flavor (-) flavor (+) flavor (-) Juicy Te n d e r
21
22
Statistical analysis of QDA scores of cooked salmon using 2-factor design with interaction ANOVA and Tukey’s test for multiple comparison showing the
storage day when difference is significant ((+) indicate positive attributes and (-) negative attributes.)
Trang 9Sensory and Nutritive Qualities of Food
cfu/g) until after 8 d in ice Similar results were observed and
re-ported by Lande and Rørå (1999) The TVC in salmon flesh at the
rejection limits observed in this study were considerably lower
than what has been found previously (Olafsdottir and others
1997) This could be caused by the high counts of H2S-producing
bacteria at the end of the storage time, since those organisms
were probably primarily responsible for spoilage (Capell and
others 1997) This supports the rejection of the cooked salmon
samples after 20 d of storage, when the TVC were dominated by
H2S-producing bacteria
Instrumental texture measurements
The instrumental hardness (Figure 10) of salmon samples
de-creased with storage time (Figure 11), indicating softening of the
salmon flesh Similar results were observed by Andersen and
others (1995) and Einen and Thomassen (1998)
There was no correlation between texture parameters
evalu-ated in cooked salmon and instrumental texture parameters for
raw salmon (Table 3) This was not unexpected for juiciness, as
none of the measured texture parameter simulates juiciness
However, tough/tender might have been related to instrumental
factors such as the attributes hardness and resilience, expressing
how stretchable the samples were The texture evaluated in QIM
(stiffness), on the other hand, was correlated to instrumental
tex-ture parameters Salmon with firm textex-ture according to
instru-mental texture measurements was assessed firm in QIM
Fat content
The average fat content of the salmon was 15.1 ± 2.1% (95%
confidence interval) and ranged from approximately 10% to 19% This was comparable to previously reported fat content of farmed salmon (Hafsteinsson and others 1998; Refsgaard and others 1998)
Tenderness, rancid odor, and flavor increased with increased fat content of the salmon (Table 4) Tenderness has previously been reported to increase with increased fat content in salmon (Andersen and others 1994) Juiciness did not correlate with fat content
Conclusions
THE SCORES FOR QUALITY ATTRIBUTES INCLUDED IN THE QIM scheme increased differently with storage time in ice, but a linear relationship with high correlation was found between QI and storage time in ice Individual salmon spoil at different rates A minimum of 3 salmon should be included in the assess-ment of each batch of salmon The storage time of the salmon may be predicted within ± 2.0 d at the 95% significance level, but examining a greater number of salmon per batch might increase the precision Based upon the sensory evaluation of cooked salmon, the maximum storage life of salmon has been deter-mined as 20 d in ice The quality of the cooked salmon did not change much through ice storage until 17 to 20 d Then the scores for positive attributes decreased, while the scores for neg-ative attributes increased Differences among panelists were evi-dent for all evaluated attributes in QDA and the QI The high cor-relation between QI and storage time in ice made it possible to predict the past storage time in ice As the maximum storage time
of salmon in ice was determined as 20 d, this information may be utilized directly for assessment with the QIM for farmed salmon
to predict remaining storage time in ice assuming optimum stor-age conditions and used in production and quality manstor-age- manage-ment
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Table 4—Correlation (r) between measured fat and some sensory attributes (QDA method) of salmon stored in ice
* Comparisons of significance according to O´Mahony (1986) Significance (p< 0.05)
Table 3—Correlation (r) between evaluated and measured
texture parameters of salmon stored in ice
Texture parameters QIM Dry/Juicy Tough/Tender
* Comparisons of significance according to O´Mahony (1986) Significance
(p < 0.05)
Figure 11—Correlation between hardness of salmon
mea-sured in TA.XT2 Texture Analyzer and storage time in ice.
Correlation is significance according to O´Mahony (1986).
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MS 20010283 Submitted 6/1/01, Accepted 9/7/01, Received 12/20/01 This work was carried out at the Icelandic Fisheries Laboratories (IFL) as a part of an ongoing project called Quality Index Method and Information Technology (QimIT) (CRAFT, CT97 9063) The authors would like to thank Asa Thorkelsdottir and the sensory panels at IFL, staff at the service department for microbial and chemical analysis, and Gudrun Olafsdottir and Rosa Jonsdottir for advice.
Authors Sveinsdottir, Martinsdottir, and Kristbergsson are with University
of Iceland, Dept of Food Science at Icelandic Fisheries Laboratories, Icelan-dic Fisheries Laboratories, P.O Box 1405, Skulagata 4, IS-121 Reykjavik, Iceland Authors Hyldig and Jørgensen are with Danish Institute for Fisher-ies Research, Technical Univ of Denmark, Build 221, DK-2800 Lyngby, Denmark Direct inquiries to author Kristbergsson (E-mail: kk@hi.is).