classification of chili powders by high performance liquid chromatography‐diode array detection
TRANSCRIPT
This article was downloaded by: [University of Sydney]On: 27 August 2014, At: 06:59Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office:Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Classification of Chili Powders by HighPerformance Liquid Chromatography‐DiodeArray DetectionEsther Forgács a & Tibor Cserháti aa Institute of Materials and Environmental Chemistry, Chemical ResearchCentre , Hungarian Academy of Sciences , Budapest, HungaryPublished online: 04 Jan 2007.
To cite this article: Esther Forgács & Tibor Cserháti (2006) Classification of Chili Powders by HighPerformance Liquid Chromatography‐Diode Array Detection, Analytical Letters, 39:15, 2775-2785, DOI:10.1080/00032710600824771
To link to this article: http://dx.doi.org/10.1080/00032710600824771
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HPLC
Classification of Chili Powdersby High Performance Liquid
Chromatography-Diode Array Detection
Esther Forgacs and Tibor Cserhati
Institute of Materials and Environmental Chemistry, Chemical Research
Centre, Hungarian Academy of Sciences, Budapest, Hungary
Abstract: The color pigments of six chili powders of different origins (China, Bali,
Pakistan, Malaysia, India, and Thailand) were separated and quantified by reversed-
phase high-performance liquid chromatography (RP-HPLC) using a narrow-bore octa-
decylsilica column (Purospher, 125 � 3 mm I.D., Merck, Darmstadt, Germany),
gradient elution, and diode array detector (DAD). The similarities and dissimilarities
among the pigment composition of chili powders have been elucidated by principal
component analysis (PCA). The RP-HPLC separated 71–111 pigment fractions
depending on the detection wavelength and on the origin of chili powder. The
pigment composition of chili powders from Malayia and China showed marked simi-
larities while the composition of pigments of other chili powders was different. It was
concluded that RP-HPLC–DAD can be successfully employed for the separation and
quantitative determination of pigments of chili powders of various origins and may
help the classification of chili powders and facilitate the authenticity test of such
food products.
Keywords: Chili, HPLC, authenticity
INTRODUCTION
The quantity and composition of pigments in foods and food products exert a
marked influence on the consumer acceptance and, consequently, on the
Received 19 January 2005; accepted 28 April 2006
Address correspondence to Esther Forgacs, Institute of Materials and Environ-
mental Chemistry, Chemical Research Centre, Hungarian Academy of Sciences,
Budapest, Hungary. E-mail: [email protected]
Analytical Letters, 39: 2775–2785, 2006
Copyright # Taylor & Francis Group, LLC
ISSN 0003-2719 print/1532-236X online
DOI: 10.1080/00032710600824771
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commercial value of the products. It has been established many times that one
of the main properties employed for the evaluation of the product quality is the
color, that is, an adequate pigment composition considerably enhances the
marketability of the products. A desirable color is due to the complex
mixture of various constituents mixed in proper proportions. The exact
knowledge of the stability of pigments against hydrolysis, oxidation, and
other environmental and technological conditions is also of paramount import-
ance, because it may help the prediction of the product shelf-life and the
assessment of the influence of technological steps on the pigments resulting
in more consumer friendly processing methods (Morais et al. 2002; Cserhati
et al. 2002). Furthermore, the qualitative determination and identification of
pigments may contribute to the establishment of the provenance of the
product and to its exact qualification (Revilla et al. 2001). Unfortunately,
traditional analytical methods are generally unsuitable for the separation
and accurate determination of the particular fractions of complex pigment
mixtures. Moreover, they do not contain any useful information on the
concentration of the individual pigments and they are not suitable for their
identification. The high separation capacity of various chromatographic
techniques makes them a method of preference for the analysis of pigments
(Airs et al. 2001). The successful separation of anthocyanins in red wines
(Berente et al. 2001), color pigments in paprika (Hayashi et al. 2001), theafla-
vins of black tea (Du et al. 2001), and color pigments of chili powder
(Capsicum frutescens) (Cserhati et al. 2000) has been recently reported.
Principal component analysis (PCA), a versatile and easy-to-use multi-
variate mathemathical-statistical method was developed to contribute to the
extraction of maximal information from large data matrices containing
numerous columns and rows (Mardia et al. 1979). Use of PCA makes
possible the elucidation of the relationship between the columns and rows
of any data matrix without being the dependent variable. It is a so-called
projection method representing the original data in smaller dimensions. It
calculates the correlations (similarities and dissimilarities) between the
columns of the data matrix and classifies the variables according to the coeffi-
cients of correlations taking into considerations simultaneously the magnitude
and sign of the coefficients of correlation. PCA frequently is used in many
fields of up-to date research. Thus, it has been employed in quantitative
structure-activity relationship (QSAR) studies (Drew et al. 1998), for the
exploration of molecular structure-property relationships (Seybold 1999),
for the evaluation of molecular lipophilicity (Sarbu and Todor 1998;
Mannhold et al. 1998), for theoretical organic chemistry (Heberger and
Lopata 1998), for quantitative structure-retention studies in chromatography
(Heberger and Gorgenyi 1999), for the elucidation of structure-biodegradation
relationships (Damborsky et al. 1998), for the clustering of amino acids
(Zaliani, and Gancia 1999), for the assessment of solvent properties
(Katritzky et al 1999), and polarity indicators in gas chromatography
(Heberger 1999), etc. As the resulting matrices of PC loadings and variables
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are generally multidimensional they cannot be evaluated by visual methods.
Nonlinear mapping technique (NLMAP) has been developed for the
reduction of the dimensionality of such matrices (Sammon 1969).
The aims of this work were the separation and quantitation of the color
pigments of assorted chili (Capsicum frutescenc) powders by RP-HPLC/DAD
Table 1. Steps of gradient elution employed
for the RP-HPLC separation of color pigments
of chili (Capsicum frutescenc) powders.
Solvent A ¼ methanol-acetonitrile (80 : 20,
v/v); solvent B ¼ bidistilled water
Time (min) Solvent A Solvent B
0 15 85
25 80 20
35 80 20
45 90 10
55 90 10
58 97 3
90 97 3
Figure 1. Separation of color pigments of chili powder (origin:Pakistan) at 340 nm.
For conditions see Materials and Methods.
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and the employment of PCA and nonlinear mapping technique for the classifi-
cation of the chili powders according to the chromatographic profile of pigments.
EXPERIMENTAL
Chili powders of different origin (sample I: China; sample II: Bali; sample III:
Pakistan; sample IV: Malaysia; sample V: India; sample VI: Thailand) were
purchased in a local market in Budapest (Hungary). Pigments were
extracted from samples of 2 g weight with 15 mL of acetone. The mixtures
were homogenized, then centrifuged. The procedure was repeated until the
extracts were colorless (normally 3 extraction steps were enough). The
combined extracts were dried over anhydrous sodium sulfate and then dried
in a rotary evaporator at 358C. The residue was dissolved in 10 mL of
acetone, filtered through a 0.45 mm membrane filter, and used for RP-HPLC
analysis. Solvents of HPLC quality used for extraction and HPLC develop-
ment were purchased from Merck (Darmstadt, Germany). The HPLC separ-
ation of pigments was performed with a Waters LC Module I HPLC
instrument with an injection device of variable volume and a Waters 746
Data Module integrator (Waters-Millipore Inc., Milford, Massaschuttes,
Figure 2. Separation of color pigments of chili powder (origin: Pakistan) at 440 nm.
For conditions see Materials and Methods.
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USA). Separation and quantitative determination of pigments were carried out
on a narrow-bore octadecylsilica column (Purospher RP-18, 125 � 3 mm I.D.;
particle size, 5 mm; carbon loading, 4.9%; Merck, Darmstadt, Germany). The
column was not thermostated. Analyses were carried out at ambient tempera-
ture (21 + 18C). The ratio of yellow and red pigments was assessed by eval-
uating the chromatograms at 340 (yellow) and 440 nm (red). The flow rate was
0.50 mL/min. The gradient steps employed for the RP-HPLC separation of
pigments are compiled in Table 1.
Gradient steps were linear. Three parallel measurements were carried out
for each sample, and the relative standard deviation (RSD%) of the retention
times and peak areas were computed (intraday reproducibility). The interday
reproduccibility was calculated from the three parallel determinations carried
out for 4 consecutive working days. The intraday and interday reproducibil-
ities were compared with the “F” probe.
Principal component analysis was applied to assess the similarities
and differences among the pigment compositions of chili powders. The six
chili powders were the variables. As the retention time of pigment fractions
showed high variations among the samples the retention times and,
Figure 3. Separation of color pigments of chili powder (origin: Bali) at 340 nm. For
conditions see Materisls and Methods.
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consequently, the relative peak areas of the individual peaks cannot be
employed as observations for PCA. Therefore, the peak areas were summar-
ized between the retention time intervals of 0–15, 15–30, 30–45, 45–60,
60–75, and 75–90 min at both 340 and 440 nm and were included in PCA
as observations (altogether 12 variables). The limit of the total variance
explained by the principal components was set to 99%. In order to facilitate
the evaluation of the multidimensional matrices of PC loadings and
Figure 4. Separation of color pigments of chili powder (origin: Bali) at 440 nm. For
conditions see Materials and Methods.
Table 2. Amount of pigments (%) in various sections of the chromatograms of chili
powders of various origin determined at 440 and 340 nm
Retention
time
interval
(min)
Wavelength
(nm) China Bali Pakistan Malaysia India Thailand
0–15 440 2.97 1.06 5.75 2.92 2.93 3.23
340 2.88 1.26 7.15 3.98 1.18 1.59
15–30 440 8.25 4.70 12.17 6.68 12.37 7.90
340 13.75 14.37 34.67 12.08 12.07 5.07
30–45 440 20.86 17.72 27.23 18.84 36.80 21.21
340 11.55 20.31 13.56 17.01 62.22 12.56
45–60 440 18.35 15.85 17.74 22.67 13.69 19.64
340 8.26 11.10 7.10 12.38 4.53 14.22
60–75 440 23.23 14.87 18.79 23.79 16.19 24.70
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variables their dimensionality has been reduced to two by the nonlinear
mapping technique. The iteration of the nonlinear maps was carried out
to the point when the difference between the two last iterations was lower
than 1028.
Table 3. Similarities and dissimilarities between the
pigment composition of chili powders. Results of principal
component analysis
No of PC
explained % Eigenvalue
Variance
explained %
Total
variance
1 3.91 65.17 65.17
2 1.02 16.93 82.21
3 0.70 11.70 93.81
4 0.29 4.79 98.60
Figure 5. Similarities and dissimilarities of chili powders according to the compo-
sition of color pigments. Two-dimensional nonlinear map of principal component
loadings. No. of iterations: 97; maximal error: 1.05 . 1022.
Classifying Chili Powders by HPLC-DAD 2781
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Software for PCA and nonlinear mapping was developed by Dr. Barna
Bordas (Plant Protection Institute, Hungarian Academy of Sciences,
Budapest, Hungary).
RESULTS AND DISCUSSION
Color pigments of each chili powder were effectively separated into many
fractions under the RP-HPLC conditions applied as demonstrated in Figs. 1–4.
The chromatographic profiles of pigments are different at 340 and 440
nms suggesting that both red and yellow pigments occur equally in each
chili powder (compare Figs. 1 and 2 and 3 and 4). This finding indicates
that the determination of the pigment composition at minimally two
different wavelengths is a prerequisite of the accurate evaluation of the
pigment composition. The chromatographic profiles of pigments also show
considerable deviations according to the type of the sample (compare Figs.
1 through 3 and 2 through 4) demonstrating that the RP-HPLC analysis of
chili pigments may help the identification of the origin of chili powders.
Similar results were reported previously (Cserhati et al. 2000, 2001; Kosa
et al. 2001).
The relative peak areas of pigment fractions quantified at 340 and 440 nm
are collected in Table 2.
The data in Table 2 prove again that the amount of pigment in a time
interval considerably depends on both the origin of the sample and the
detection wavelength, consequently, the chromatographic profiles character-
ize the individual chili powder well.
The results of PCA using are compiled in Table 3.
Three principal components explain the overwhelming majority of total
variance suggesting that the six original variables can be replaced by three
background (abstract) variables with only 6.19% loss of information.
However, PCA does not confirm the reality of such theoretical variables
only indicates their mathematical possibility. The fact that the majority of
Table 4. Principal component loadings
Chili powder
No. of principal components
1 2 3
China 0.97 20.17 20.09
Bali 0.86 0.01 0.23
Pakistan 0.64 0.34 2 0.69
Malaysia 0.97 20.06 0.02
India 0.32 0.88 0.33
Thailand 0.88 20.32 0.23
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chili powder has high loadings in the first PC suggests the fundamental simi-
larity of pigment composition of chili powders. Chili originating from China
and Malaysia has the highest, those of Pakistan and India have the lowest
loadings in the first principal component.
The two-dimensional nonlinear map of PC loadings is shown in Fig. 5.
The scattering of chili powders on the map wholly supports the results
compiled in Table 4: the points representing chili powders originating from
Malaysia and China are the nearest to each other while points of chili
powders from India and Pakistan are far away from the other points, indicating
that the composition of pigment is markedly different.
The two-dimensional nonlinear map of PC variables (relative peak areas
determined at different time intervals and detection wavelengths) is shown
in Fig. 6.
The points do not form clear-cut clusters. Only the retention time intervals
0–15 min are very near to each other, indicating that the first part of chroma-
togram is not characteristic for the pigment composition of chili powders,
therefore, it cannot be employed for the identification of the samples. The
relative peak areas measured at 340 and 440 nms are well separated indicating
that both of them can be employed for the classification of chili powders.
No significant differences were found between the intraday and interday
repoducibilities of retention times and peak areas proving the good stability
Figure 6. Similarities and dissimilarities between the pigment fractions. Two-dimen-
sional nonlinear map of principal component variables. No. of iterations: 70; maximal
error: 1.51 . 1022. Numbers refer to pigment fractions in Table 2.
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and reproducibility of the RP-HPLC system. The RSD values for both intraday
and interday reproducibilities were 0.9%–1.8% for retention times and 3.7%–
5.3% for peak areas. The relatively high RSD values for peak areas may be
due to the fact that some pigment fractions were not entirely separated
during the chromatographic process.
It can be concluded from the results that the color pigments of chili
powders can be readily separated and quantitated by RP-LC. The profile
and ratio of yellow and red pigments measured at 440 and 340 nm is charac-
teristic for the chili powders of different origin. Principal component analysis
is a valuable tool for the assessment of the similarities and differences among
the chili powders according to the composition of the pigments. The method
may facilitate the authenticity test of chili powders. By comparing the present
method with the other one previously published (Kosa et al. 2001) it can be
established that the analysis time of this method is shorter and the capacity
to differentiate among chili powders of different origin is the same.
REFERENCES
Airs, R.L., Atkinson, J.E., and Keely, B.J. 2001. J. Chromatogr. A, 917: 167.Berente, B., Reichenbacher, M., and Danzer, K. 2001. Fresenius J. Anal. Chem.,
371: 68.Cserhati, T., Forgacs, E., Darwish, Y., Morais, H., Mota, T., and Ramos, A.C. 2002.
J. Chromatogr. A, 949: 269.Cserhati, T., Forgacs, E., Morais, M.H., Mota, T., and Ramos, A. 2001. J. Liq.
Chromat. Relat. Technol., 24: 425.Cserhati, T., Forgacs, E., Morais, M.H., Mota, T., and Ramos, A. 2000. J. Chromatogr.
A, 896: 69.Damborsky, J., Berglund, A., Kuty, A., Ansorgova, A., Nagata, A., and Sjostrom, M.
1998. QSAR, 17: 450.Drew, M.G.B., Wilden, G.R.H., Spillane, W.J., Walsh, R.M., Ryder, C.A., and
Simmie, J.M. 1998. J. Agr. Food Chem., 46: 3016.Du, Q., Jiang, H., and Itu, Y. 2001. J. Liq. Chromat. Relat. Technol., 24: 2363.Hayashi, T., Hayashi, K., Fujita, J., Ono, M., Oka, H., Ito, Y., Matsumoto, H.,
Ozeki, N., Itakura, Y., and Nakazawa, H. 2001. J. Liq. Chromat. Relat. Technol.,24: 2347.
Heberger, K. 1999. Chemometer Intell. Lab., 47: 41.Heberger, K. and Gorgenyi, M. 1999. J. Chromatogr. A, 845: 21.Heberger, K. and Lopata, A. 1998. J. Org. Chem., 63: 8646.Katritzky, A.R., Tamm, T., Wang, Y., and Karelson, M. 1999. J. Chem. Inf. Comput.
Sci., 39: 692.Kosa, A., Cserhati, T., Forgacs, E., Morais, H., Mota, T., and Ramos, A.C. 2001.
J. Chromatogr. A, 915: 149.Mannhold, R., Cruciani, G., Dross, K., and Rekker, R. 1998. J. Comp.-Aid. Mol. Des.,
12: 573.Mardia, K.V., Kent, J.T., and Bibby, J.M. 1979. Multivariate Analysis; Acad. Press:
London, 213.Morais, H., Rodrigues, P., Ramos, C., Almeida, V., Forgacs, E., Cserhati, T., and
Oliveira, J.S. 2002. Food Sci. Tech. Int., 8: 55.
E. Forgacs and T. Cserhati2784
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nloa
ded
by [
Uni
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ity o
f Sy
dney
] at
06:
59 2
7 A
ugus
t 201
4
Revilla, E., Garcia-Beneytez, E., Cabello, F., Martin-Ortega, G., and Ryan, J.-M. 2001.J. Chromatogr. A, 915: 53.
Sarbu, C. and Todor, S. 1998. JPC–J. Planar Chromatogr., 11: 123.Seybold, P.G. 1999. SAR QSAR Env. Res., 10: 101.Zaliani, A. and Gancia, E. 1999. J. Chem. Inf. Comput. Sci., 39: 525.Sammon, J.W., Jr. 1969. IEEE Trans. Comput., C18: 401.
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