Quantitative Analysis of Cholesterol and Cholesterol Ester Mixtures Using Near-Infrared Fourier Transform Raman Spectroscopy

One of the fields of research o f our laboratory is the use of Raman spectroscopy to study the chemical com- position of biological samples in view of its application as an in situ and in vivo detection method o f human diseases. Atherosclerosis is the main cause o f death in industrial countries. Therefore, any contribution to the understand- ing of the pathogenesis o f atherosclerosis is potentially o f major interest. Most o f the studies on atherosclerosis show the importance o f lipids in the disease pathogene- s i s . 1-6 However, current techniques o f investigation re- quire the r e m o v a l o f tissue samples and their destruction. In these cases, a nondestructive technique that could rap- idly provide biochemical information using very small amounts of tissue sample with no previous preparation would be of great relevance. R a m a n spectroscopy is an analytical technique based on the interaction of an incident m o n o c h r o m a t i c radiation with vibrational energy levels o f molecules. This tech- nique, applied in our laboratory for m a n y years in c o m - bination with optical fibers, allows in situ analyses giving both qualitative and quantitative information for various chemical mixtures and, especially, biological samples. 7-1~ In these cases, a near-infrared excitation source allowing fluorescence-free measurements should be used in order to obtain high-quality R a m a n spectra.

Quantitative Analysis of Cholesterol and Cholesterol Ester Mixtures Using Near-Infrared Fourier Transform R a m a n Spectroscopy

P L e C A C H E U X , G M E N A R D , H N G U Y E N Q U A N G , P W E I N M A N N , M J O U A N ,

a n d N G U Y E N Q U Y D A O *

Laboratoire P.C.M., URA D1907 CNRS, Ecole Centrale Paris, 92295 Chatenay-Malabry Cedex, France

Near-infrared Fourier transform Raman spectroscopy is a rapid

and nondestructive technique that can provide reliable qualitative

in situ information about the chemistry of biological samples W e

have combined this technique with partial least-squares (PLS) re-

gression to perform a quantitative determination of free and ester-

ified cholesterol in two synthetic sample series In 66 ternary

mixtures containing various proportions of cholesterol, cholesterol

linoleate, and oleate, the standard errors of prediction were 1.27,

1.17, a n d 0 9 4 % , respectively For the second series of experiments

concerning the sensitive problem of quantitative analysis of choles-

terol palmitate and stearate mixtures, the standard error of predic-

tion for 49 samples was 3.02% It is also possible to extract quan-

titative information for a single component of the ternary mixtures

independently These results are of great importance w h e n - - a s in

the case of arterial s a m p l e s - - m a n y chemical species are present

Comparison between Raman spectra of ternary mixtures and ath-

erosclerotic rabbit aorta shows that many bands, assignable to free

and esterified cholesterol, are easily observed in the aorta spectrum

Index Headings: Lipids; Quantitative analysis; Raman spectroscopy;

Partial least-squares regression; Cholesterol; Cholesterol esters

I N T R O D U C T I O N

One of the fields of research of our laboratory is the

use of Raman spectroscopy to study the chemical com-

position of biological samples in view of its application as

an in situ and in vivo detection method of human diseases

Atherosclerosis is the main cause of death in industrial

countries Therefore, any contribution to the understand-

ing of the pathogenesis of atherosclerosis is potentially

of major interest Most of the studies on atherosclerosis

show the importance of lipids in the disease pathogene-

quire the removal of tissue samples and their destruction

In these cases, a nondestructive technique that could rap-

idly provide biochemical information using very small

amounts of tissue sample with no previous preparation

would be of great relevance

R a m a n spectroscopy is an analytical technique based

on the interaction of an incident monochromatic radiation

with vibrational energy levels of molecules This tech-

nique, applied in our laboratory for many years in com-

bination with optical fibers, allows in situ analyses giving

both qualitative and quantitative information for various

chemical mixtures and, especially, biological samples 7-1~

In these cases, a near-infrared excitation source allowing

fluorescence-free measurements should be used in order

to obtain high-quality R a m a n spectra

Received 19 July 1995; accepted 22 M a y 1996

* Author to w h o m correspondence should be sent

However, analyses of biological samples by R a m a n spectroscopy present us with two main problems On the one hand, the complexity of these samples leads to a great complexity in their Raman spectra Therefore, it is not easy to extract relevant information on specific compo- nents of interest On the other hand, the components to

be studied can be of very similar structures Therefore,

R a m a n spectra of the mixtures show overlapped bands (cases of long saturated hydrocarbon chains), making quantitative analysis very difficult In these cases, clas- sical quantitative spectral analysis methods using peak intensity information are inaccurate However, it has been shown that partial least-squares (PLS) regression, which

is a statistical multivariate method using factor analysis combined with R a m a n spectroscopic results, enables quantitative information on several components to be ex- tracted e v e n w h e n one is d e a l i n g with c o m p l e x mixtures ~2-~5

In this paper, this method is used to analyze two dif- ferent series of mixtures containing pure cholesterol and cholesterol esters (cholesterol oleate C18:1, linoleate C18:2, palmitate C16:0, and stearate C18:0) as a prelim- inary study These compounds were chosen because they are the predominant lipid species in the arterial intima The total amounts of C16:0, C18:0, C18:1, and C18:2 vary from 60 to 90% of the total cholesterol esters in the arterial wall Moreover, with increasing age and with ath- erosclerosis development, variations in the composition

of cholesterol fatty acid esters appear There is a signif- icant decrease in saturated fatty acids (palmitic and stea- ric) and an increase in linoleic acid and in the linole- ate/oleate ratio 2,3.5,6

In the first step of this study, a R a m a n analysis of ternary mixtures of cholesterol C18:l, and C18:2 was used in order to examine the possibility of quantitatively determining the concentration of one particular c o m p o - nent in the presence of the other two Next, quantitative analyses of binary mixtures of C16:0 and C18:0 were examined Both these cholesterol esters have a saturated fatty acid chain of 16 and 18 carbons, respectively Their

R a m a n spectra therefore show practically no differences This second series of samples represents a real challenge

as it is one of the most difficult analytical problems en- countered

MATERIAL AND M E T H O D S Experimental Procedure Cholesterol, C18:1, and C18:2 were purchased from Sigma Chemical Company, and their purities were 95, 98, and 98%, respectively

73 7

72 8

67 ~ / ~ M ~ ' / ~ / ~ / ~ X / / ~ 13

~%iX-~"~,~'X X , £ ~ , X X '~

34 33 32 31 30 29 28 27 26 25 24 23 22 21

% C H O L E S T E R O L L I N O L E A T E

Fro 1 Ternary diagram representing the 65 calibration mixtures con-

taining cholesterol, cholesterol linoleate, and cholesterol oleate

C 16:0 and C 18:0 were purchased from Aldrich Company,

and their purities were 97 and 96%, respectively

Two sample series were prepared by using cholesterol

and cholesterol ester solutions in chloroform mixed to

obtain homogeneous mixtures These solutions were kept

at r o o m temperature and in a vacuum until the total evap-

oration of chloroform occurred In the first series, 65

samples were prepared with molar fractions ranging from

63 to 72% for cholesterol, from 6 to 14% for C18:1, and

from 21 to 29% for C18:2, as shown in Fig 1 Because

concentrations of lipid components vary largely among

atherosclerotic plaques, this range of concentrations was

deliberately chosen as a first step The series of second

samples was comprised of 49 mixtures of C16:0 and C18:

0 with molar fractions ranging from 30 to 70%

FT-Raman spectra were measured from 100 to 4000

cm -~ with a Bruker IFS 66 Fourier transform spectrom-

eter equipped with a F R A 106 R a m a n scattering module

The excitation source was a Spectra Physics T F R laser

using 1047-nm radiation, and the detector used was a

liquid nitrogen-cooled germanium diode Two successive

spectra were obtained from each sample with 800-roW

laser power in a 2 0 0 - ~ m - d i a m e t e r spot on the sample

The spectra were collected with 200 scans at 2-cm-l res-

olution ( ~ 1 0 min total collection time) Data were re-

corded and processed with Bruker Opus software on a

C o m p a q 386DX microcomputer

The measured spectra were then transferred to a PC/AT

486 microcomputer equipped with a Turbo-Pascal PLS

program

Principle of Quantitative Raman Spectra Analysis

Using the Partial Least-Squares Regression Quanti-

tative R a m a n analysis with partial least-squares regres-

sion 15-21 was performed The R a m a n spectrum of a mix-

ture can be represented as a vector, s = (I~ I~

I,,), in which /~ is the R a m a n intensity at wavelength i

The sample composition is represented as another vector,

c = ( C i Cj Cm), in which the C: are the con-

centrations of the mixture components The aim of quan-

titative R a m a n spectral analysis is the deduction of the

composition from the sample Raman spectrum For this

purpose, a model should be established during the cali-

bration phase

In practice, the sample composition is not completely

known in the case of complex mixtures, and the spectrum vector contains irrelevant information such as noise and variations in intensity due to unknown components and

e x p e r i m e n t a l v a r i a t i o n s T h i s i r r e l e v a n t i n f o r m a t i o n should be discarded during model elaboration

The partial least-squares regression consists in calcu- lating new spectral and concentration data by projecting the observed S and C data matrices of calibration sam- ples onto the " f a c t o r s " bases, W and Q, respectively They are also the eigenvectors matrices of cov(tC.S) and cov(tS.C), respectively Under such conditions, the partial least-squares method takes the " a x e s " corresponding to the higher correlated variations between spectral infor- mation and known concentrations into account for the model elaboration The number of factors represents the amount of information involved and is therefore very sig- nificant for the estimation quality In practice, there are

an optimal number of factors to be used, which should

be determined by validating the model The relevant in- formation is extracted and the irrelevant is discarded Moreover, with this method, each c o m p o n e n t of a mix- ture can theoretically be studied without any reference to the others Two approaches are then conceivable: either

" P L S 2 " , which consists in the determination of several components at the same time, or " P L S 1", in which each component is studied separately

For each sample, the first R a m a n spectrum was used

in the calibration phase and the second in the validating prediction phase In the prediction phase, the quality of the results was determined by calculating the standard error of prediction (SEP), defined as

l Z I C,.,ro - C / uol 2 SEP = i= 1

/2 S

where ns is the number of prediction samples

RESULTS AND DISCUSSION Quantitative Determination of Cholesterol, C18:1, and C18:2 on Synthetic Mixtures Three spectra of ter- nary mixtures with their respective composition in cho- lesterol, C18:2, and C 1 8 : I - - A (71.6, 21.6, and 6.8%); B (63.5, 29.2, and 7.3%), and C (63.2, 22.6, and 1 4 2 % ) - are shown in Fig 2, together with the spectra of the pure esters for comparison (Fig 3) There are only a few dif- ferences between these spectra In fact, the major vibra- tional bands observed in these spectra can be assigned to cholesterol vibrations (1667 c m - ~ is due to the cholesterol

C = C stretching vibration; 1455 and 1437 cm 1 to the C -

H bending vibrations; 959, 880, 840, 805, 699, and 605

cm ~ to the vibrational modes of the sterol rings), and they are therefore shared by the three compounds Fur- thermore, R a m a n peaks due to fatty acid chains are often weak or overlapped with cholesterol peaks The main dif- ference appears on the bands at 1667 and 1655 cm ~, assigned, respectively, to the stretching vibration of C = C bonds in cholesterol and in unsaturated fatty acids The relative intensity of the C = C band at 1655 cm ~ increases with the number of unsaturated double bonds per fatty acid chain However, these variations do not allow a sat- isfactory quantitative analysis with the use of the stan-

1254 Volume 50, Number 10, 1996

CO

-i

W a v e n u m b e r (cm FIG 2 Near-infrared Raman spectra of three ternary mixtures with

respect to amounts of cholesterol, cholesterol linoleate, and cholesterol

oleate: A (71.6, 21.6, 6.8%); B (63.5, 29.2, 7.3%); and C (63.2, 22.6,

14.2%)

dard peak height method in the case of ternary mixtures

With a PLS program, the best results were obtained by

using the spectral region from 600 to 1800 cm -], which

contains most bands of the three compounds

The PLS method was carried out with the use of two

different approaches to the problem For the first approach

(PLS2), all concentration data were used at the same time

in the model elaboration Under these conditions, the PLS

method underscores the higher correlated variations be-

tween spectral information and all the concentrations

Here, PLS only eliminates irrelevant information corre-

sponding to experimental variations Free cholesterol and

C18:2 molar fractions were used to calculate the PLS2

model (C18:1 concentration is deduced from the other

two) For the second approach (PLS 1), only one concen-

tration was used in each model elaboration With this

method, PLS underscores the higher variations attributable

to a specific compound and eliminates irrelevant infor-

mation due to experimental variations and to the other

compounds Three models corresponding to each compo-

nent were run and compared to the PLS2 model Figures

4 and 5 show the variations of standard error of prediction

vs number of factors used in the model elaborations for

PLS2 and PLS1 methods, respectively

As shown in Fig 4, standard errors of prediction de-

crease with the number of factors (NF) used in the cali-

bration step until NF = 11 At that point, SEP values show

no significant variations It should be noted that the model

using only one factor provides a fairly good estimate of

sample composition (SEP < 2%), which means that a

large part of the relevant information is included in the

first "dominant" factor No factors higher than 11 corre-

1800 1600 1400 1200 ! 000 800 600 400

W a v e n u m b e r (cm -I ) FIG 3 Near-infrared Raman spectra of pure cholesterol esters: (A) cholesterol oleate; (B) cholesterol linoleate; (C) cholesterol palmitate; (D) cholesterol stearate

sponding to irrelevant information improve model quality Therefore, the best PLS2 results were obtained with 11 factors in the model elaboration, and the standard errors

of prediction calculated with 66 samples were 0.95, 1.19, and 1.30% for C 18:2, C 18:1, and free cholesterol, respec- tively

In the case of P L S I , three models were independently calculated with the use of each of the three component concentrations Curves showing variations of SEP vs the

r ~

1.8

1.6

1.4

1.2

0+8 I I I I I I I I I t I I I I I I I I

FIG 4 Plot of standard error of prediction (SEP) vs number of factors used in the calibration step for global quantitative analysis (PLS2) of the 65 ternary mixtures: ( 0 ) for cholesterol concentration, (A) for cho- lesterol linoleate, and ( 0 ) for cholesterol oleate

(3 - Cholesterol

1.8

1.6

1.4

1.2

Number of factors used

FIG 5 Plot of standard error of prediction (SEP) vs n u m b e r of factors

used in the calibration step for three independent quantitative analysis

(PLS1) of the 65 ternary mixtures: (O) for cholesterol concentration,

(A) for cholesterol linoleate, and ( • ) for cholesterol oleate

number of factors used (Fig 5) were very similar to those

obtained with PLS2 The best standard errors of predic-

tion were 0.94% with the use of 9 factors for cholesterol

linoleate, 1.17% with 10-11 factors for cholesterol ole-

ate, and 1.27% with 10 factors for free cholesterol The

optimal number of factors used was not the same as in

the PLS2 method nor the same for each compound This

observation is consistent with the fact that information

corresponding to the other component variations should

be eliminated The results provided by PLS 1 models were

as good as those for the PLS2 This consideration means

that NIR FT-Raman spectroscopy enables information to

be obtained for one or several compounds in a multicom-

ponent mixture This capability is of great interest in the

case of arterial samples where only several compound

compositions may be known or of interest

The Quantitative Determination of C16:0 and C18:

0 in Binary Synthetic Mixtures These two compounds

were chosen because of the difficult differentiation of

their Raman spectra Figure 6 shows the spectra of C18:

0 (A), C16:0 (C), and a mixture containing 50% of each

(B) There is no visible difference between these spectra,

because most bands observed on the spectra belong to

groups that both compounds have in common Moreover,

the bands assigned to the saturated fatty acid chain (1296

cm ~ due to C - H bending vibrations; 1129 and 1085

cm -~ due to C - C stretching vibrations) show very little

variation in their relative intensity

A quantitative analysis of C16:0 and C18:0 mixtures

was carried out with the use of 49 samples for calibration

and prediction The results are plotted in Fig 7 The best

standard error of prediction was 3.02% and was calcu-

lated with seven factors Moreover, the very important

W a v e n u m b e r (cm -I) FIG 6 Near-infrared R a m a n spectra of (A) cholesterol stearate, (B) 50:50 cholesterol palmitate/cholesterol stearate mixture, and (C) cho- lesterol palmitate

decrease of SEP in this case (from 10.95 to 3.02%) meant that the first seven factors had the same significance in the model elaboration There were no " d o m i n a n t " factors

in this case, and the first seven factors had practically the same relevance This result confirms the fact that there are very few differences between the compounds

I 1

8.75

~.' 6.5

4.25

2

I 3 $ 7 9 | 1 13 15 t 7 19

Number of factors used for calibration FIG '7 Plot of standard error of prediction (SEP) vs number of factors used in the calibration step for the quantitative analysis of 49 cholesterol palmitate/cholesterol stearate mixtures with concentrations ranging from

30 to 70% cholesterol palmitate The best standard error of prediction

is 3.02% calculated with seven factors

1256 Volume 50, Number 10, 1996

' ' ' I ' ' ' [ '

O3 ,-4

~q

£v

1 8 0 0 1 6 0 0

0

, I , , l , , l , , I , , ,

W a v e n u m b e r ( c m - I )

FIG 8 Near-infrared Raman spectra of (A) an atherosclerotic rabbit

aorta and (B) a ternary mixture containing cholesterol, cholesterol li-

noleate, and oleate

Comparison of the Synthetic Mixture Spectra and

Real Aorta S a m p l e s The results given in this paper

demonstrate that N I R F T - R a m a n interfaced with the PLS

methods can provide significant quantitative information

on the composition of lipid mixtures Moreover, NIR

F T - R a m a n can differentiate and analyze very similar

compounds such as C16:0 and C18:0 In addition, this

technique can determine biochemical composition of a

single c o m p o u n d in a complex mixture This capability

is of great importance in the case of biological samples,

especially those from the arterial wall, in which m a n y

species are present In practice, only several compounds

are worth studying, because of their involvement in the

pathogenesis Figure 8 shows a comparison between a

ternary mixture spectrum (cholesterol, C18:1, and C18:

2) and a thoracic aorta spectrum of a cholesterol-fed rab-

bit obtained in the following way: Prior to spectroscopic

study, samples were homogenized in order to obtain spec-

troscopic results that could be c o m p a r e d to the biochem-

ical ones Then, they were passively warmed to room

temperature while being kept moist with isotonic saline

solution The tissue samples were placed in an aluminium

cuvette with a small amount of isotonic saline solution,

to keep the tissue moist, and covered with a 0.1-ram glass

window During the spectral measurements, the samples

were cooled down with a cold nitrogen stream in order

to keep them at the room temperature The spectra of the

tissue samples were measured by using the same proce-

dure used for the synthetic samples These two spectra

have m a n y R a m a n bands in c o m m o n In particular, sev-

eral bands of the thoracic aorta spectrum are significant

(1667, 1655, 1455, 1437, 1301, and 699 cm-t) They are

due to cholesterol and cholesterol esters These bands are

easily discerned and can be used for an in situ quantita-

tive analysis of cholesterol and cholesterol esters in the arterial wall Moreover, previous studies demonstrated that NIR F T - R a m a n could provide in situ information concerning other species that accumulate in the athero- sclerotic arterial wall, such as elastin, collagen, carote- noids, and calcium apatite deposits 1~,22 Therefore, this technique should enable a quantitative study of disease progression and response to different modes of treatment

to be made

C O N C L U S I O N The results to be found in this paper demonstrate that

N I R F T - R a m a n spectroscopy interfaced with PLS re- gression can precisely determine the proportions of dif- ferent fatty acids present in the arterial wall The method

is a very powerful tool for histochemical analysis of ath- erosclerosis The next step of this study, in progress at the present time, is an in situ quantitative analysis of free and esterified cholesterol in cholesterol-fed rabbits arter- ies At the same time, the use of this technique will be extended to other lipidic and nonlipidic components that are involved in the pathogenesis of the human disease Once the method is validated, its extension to in situ hu- man plaque studies will be envisaged

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