Multivariate optimization and HS-SPME/GC-MS analysis of VOCs in red, yellow and purple varieties of Capsicum chinense sp. peppers

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1 Microchemical Journal 82 (2006) Multivariate optimization and HS-SPME/GC-MS analysis of VOCs in red, yellow and purple varieties of Capsicum chinense sp. peppers Eliane Teixeira Sousa a, Frederico de M. Rodrigues a,b, Cláudio C. Martins a, Fabio Santos de Oliveira a, Pedro Afonso de P. Pereira a, Jailson B. de Andrade a, a Instituto de Química, Universidade Federal da Bahia, Campus Universitário de Ondina, Salvador, BA, Brazil b Empresa Baiana de Desenvolvimento Agrícola S.A. (EBDA), Salvador, BA, Brazil Received 29 December 2005; accepted 4 January 2006 Abstract Peppers are used not only in cookery, but also in many other applications, like cosmetic, pharmaceutical and nourishing industry. The chemical composition of peppers is quite complex and several volatile and non-volatile substances contribute to their flavor, which is an important sensorial propriety. In this work a headspace/solid phase microextraction/gas chromatography coupled to mass spectrometry method was developed to evaluate the profiles of volatile compounds that contribute to the aroma of red, yellow and purple varieties of Capsicum chinense sp. peppers. The optimization of the extraction conditions was carried out using multivariate strategies such as factorial design and response surface methodology. The GC-MS analysis allowed the tentative identification of 34 compounds, with similarities higher than 85%, in accordance with the NIST mass spectral library. The data obtained by the analysis of volatile compounds, according to the proposed method, were treated with PCA chemometrics tool in order to group different varieties of C. chinense sp. peppers with similar VOC profiles. Amongst the most abundant VOCs, of pentanoic acid, dimethylcyclohexanols, humulene and esters of butanoic acid were found. Principal component analysis turned possible to visualize the grouping tendencies of the studied varieties of pepper, as well as the identification of the volatile compounds responsible for discriminating the three groups. Considering the fact that many species of peppers are used as human food, the significance of this work is further emphasized by its applicability to the study of food quality indicators, and as a tool for investigations on the composition of the pepper sources Elsevier B.V. All rights reserved. Keywords: Peppers; VOCs; HS-SPME/GC-MS; Chemometrics 1. Introduction Pepper is an annual herbaceous plant of the Solenaceae family [1] cultivated worldwide and extensively used as spice in the diet, especially due to its characteristic pungency, aroma and color appeal [2]. Archeological artifacts evidenced that Capsicum gender peppers have been used since B.C. by primitive communities in America [3]. Besides the large application of Capsicum peppers related with cookery, they have been employed in traditional medicine as antimicrobial, insecticide, anticonvulsive, sedative [4 6] and even in unusual applications such as in bravery rituals of young indigenous warrior of South America [3]. Corresponding author. Tel./fax: address: (J.B. de Andrade). In spite of the attention given to pungency of peppers, related to their capsaicin content [7 9], odor plays an important role to the flavor for this kind of spice and to its acceptance by consumers [10,11]. Depending on the species and level of ripeness of peppers, their aroma is different due to differences in volatile compound profiles [12]. The varieties of Capsicum chinense peppers, also known as scented peppers, are essential ingredients of several typical cookery recipes of Bahia (13 01 S and W), in Brazil and the consumption of this kind of spice represents a relevant characteristic of the local culture. In this way, the typical aroma of C. chinense sp. peppers is one of the most attractive properties, representing a quality parameter for the consumers. The analysis of volatile compounds potentially related to the aroma of pepper has been a challenge for many researchers, since a large number of volatile compounds have been identified in pepper varieties [12,13], despite only a small X/$ - see front matter 2006 Elsevier B.V. All rights reserved. doi: /j.microc

2 E.T. Sousa et al. / Microchemical Journal 82 (2006) Fig. 1. Photographs of yellow (A), red (B) purple (C) varieties of C. chinense sp. peppers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) fraction of them really contributes to this sensorial attribute [14]. Different analytical methods have been developed to determine compounds that contribute to flavor in fresh and processed chilli, such as solvent extraction distillation extraction [15 17] and dynamic and static headspace [18 20]. However, these methods are laborious, time-consuming and expensive and must lead to erroneous conclusions about the intact aroma of pepper, related to artifacts introduced due to interval time required between sample preparation and solvent interaction steps [12]. In this way, an ideal sample preparation technique for evaluation of volatile compounds in peppers should be simple, fast, inexpensive and compatible with a range of analytical instrumentation. Headspace/solid phase microextraction (HS-SPME) appears to be a technique that fits most of requisites for sample preparation steps, to analyze volatile compounds in peppers [21], due to its simplicity, no solvent use, little sample manipulation, multiple sampling, high sensitivity, lower analysis time interval and easiness of automation. Important parameters affecting the SPME method development are related to the type of fiber employed, the extraction time interval, the extraction temperature, the sample amount and the desorption time and temperature. The procedure employed for the optimization of the extraction conditions can be carried out by traditional univariate approach, in which each factor is studied separately, or using multivariate experimental design strategy [22 24], which allows for simultaneous variation of all evaluated factors, making possible to distinguish interactions among them that would not be detectable by classical experimental design. The multivariate optimization design also allows a reduction in the amount of required experiments, without loss of information. The evaluation of the profiles of volatile compounds that could contribute to the aroma of different varieties of peppers is a difficult task, since a large number of VOCs can be detected in Table 1 Experimental levels employed for screening design Variable Coded variable ( 1) (0) (+1) Extraction temperature/ C Extraction time/min Desorption time/min this kind of samples. In this sense, multivariate chemometrics pattern recognition techniques [25,26], such as principal component analysis (PCA), are powerful tools to identify similarities among complex samples in which several compounds are measured. The aim of this study was to develop an analytical methodology based on HS-SPME/GC-MS, in order to identify and evaluate the profiles of volatile compounds, that could contribute to the aroma of red, yellow and purple varieties of C. chinense sp. peppers, as well as those responsible for giving different attributes to each one. In this way, multivariate chemometrics techniques were employed in order to optimize the SPME extraction procedure and to identify grouping tendencies amongst the three varieties. The significance of this work is further emphasized by its applicability to the study of food quality indicators, and as a tool for investigations on the composition of the pepper sources. 2. Materials and methods 2.1. Samples Fresh samples of red, yellow and purple varieties of C. chinense sp. peppers (Fig. 1) were purchased from different vendors at a local market. Pulps and seeds of each type of pepper were sliced and crushed manually, and then 3.0 g were placed in a sealed 20mL headspace vial for extraction Solid phase microextraction The SPME extractions were carried out in a polydimethylsiloxane fibre (PDMS 100μm, Supelco Bellefonte), with a manual holder. Choice of this fibre was due to its nonpolarity and relatively high thickness. VOCs were extracted from the headspace volume of the samples, after optimization, according to the following conditions: extraction time 44 min; extraction temperature 64 C. The trapped volatile compounds were Table 2 Experimental levels employed for central composite design Variable Coded variable ( 1.41) ( 1) (0) (+1) (+1.41) Extraction temperature/ C Extraction time/min

3 144 E.T. Sousa et al. / Microchemical Journal 82 (2006) Fig. 2. Pareto chart of standardized effects of 2 3 factorial design for total chromatographic peak area. desorbed at 250 C in the GC injection port for 1 min and flushed into the GC column GC-MS analysis Analyses were performed on a GC-MS system (Shimadzu GC-2010/QP-2010 high performance quadrupole, Japan) under the following instrumental conditions: helium flow rate: 1.22ml min 1 temperature program: 50 C (0min); 20 C min C; 150 C for 10min; 10 C min 1 up to 250 C injector mode and temperature: split, 250 C split ratio: 1:50 source temperature: 250 C transfer line temperature: 250 C energy of impact: 70 ev up to column: HP-5 MS (30m 0.25mm i.d. 1.00μm, Agilent Palo Alto) Response 2.4. Optimization of the SPME conditions As an initial step, a screening 2 3 full factorial design [22 24] was performed to evaluate significant variables involved in SPME. Three replications were performed in the central point of factorial design in order to quantify experimental error. The variables evaluated by screening experimental design were the extraction temperature, the extraction time and the desorption time. The levels employed in these experiments are listed in Table 1. The response evaluated during all experiments was the total sum of peak areas, obtained in the GC-MS analysis. Once significant variables were retained, central composite design with 5 replicates in central point and response surface Extraction time Temperature Fig. 3. Response surface obtained by central composite design using coded variables where the response was total chromatographic peak area. Table 3 Experiments carried out to confirm SPME optimal conditions estimated by response surface methodology at extrapolation conditions Temperature/ C Extraction time/min Response Evaluated response was given by total peak areas.

4 E.T. Sousa et al. / Microchemical Journal 82 (2006) Fig. 4. HS-SPME/GC-MS chromatogram related to volatile organic compounds of yellow variety of C. chinense sp. peppers. methodology [22 24] were carried out in order to locate the optimum values of temperature and extraction time. The experimental levels involved in central composite design optimization are listed in Table 2. The statistical experimental design and optimization calculations were performed using the Statistica 7.0 software (Statsoft, USA). Fig. 5. HS-SPME/GC-MS chromatogram related to volatile organic compounds of red variety of C. chinense sp. peppers.

5 146 E.T. Sousa et al. / Microchemical Journal 82 (2006) Principal component analysis A set of 34 VOCs that were detected in the majority of the analyzed pepper samples or presented percentile peak area higher than 2% was evaluated. A data matrix containing the peak areas of 34 VOCs, in 6 samples of each variety of C. chinense sp. peppers (red, yellow and purple) was submitted to the principal component analysis (PCA) with autoscaling pretreatment [25,26], resulting in a data matrix. Since PCA is a well-known chemometrics technique of multivariate analysis, which turns easier to visualize grouping tendencies, using the whole information contained in a large number of variables, this tool was employed to intensify grouping tendencies of different varieties of C. chinense sp. peppers, based on their VOC profiles. The Unscrambler 8.0 (CAMO, Norway) chemometrics package was employed for PCA calculations. 3. Results and discussion 3.1. SPME optimization The results obtained by screening factorial design are summarized in the Pareto's chart of effects illustrated in Fig. 2. As can be seen there, only the extraction time and the interaction between temperature and extraction time effects were significant at 95% confidence level. In this way, only extraction temperature and time were evaluated for optimization purposes. Once relevant variables were detected by factorial design, further experiments were carried out keeping desorption time at 1min, in order to reduce the overall analysis time interval. Then a central composite design was built using the experimental levels that gave the best response in the factorial design as central point. Five replicates in the central point were performed to estimate experimental error and to detect lack of fit. Response surface obtained by central composite design is illustrated in Fig. 3. The estimated optimum values for extraction temperature and time, by response surface methodology, were 74 C and 59 min, respectively, which were outside the previously evaluated experimental region. The quadratic equation obtained using coded values for the variables is given by R= T T t t T t, where R means the response, T extraction temperature, t extraction time, and T t the interaction between desorption temperature and time. The estimated increase in overall sensitivity, in the optimal point by extrapolation (extraction temperature = 74 C, extraction time = 59min), in comparison with the experimental point that gave the best results by central composite design (extraction temperature = 64 C, extraction time = 30min) was only 3%. In order to evaluate the analytical response in the new conditions, further experiments were carried out, using the temperature value which corresponds to the optimum point, given by the response surface method (Table 3). The results showed that at 74 C, there was a decrease in the overall sensitivity, Fig. 6. HS-SPME/GC-MS chromatogram related to volatile organic compounds of purple variety of C. chinense sp. peppers.

6 E.T. Sousa et al. / Microchemical Journal 82 (2006) when compared with that one obtained at 64 C. This was presumably due to a competing mechanism of thermal desorption of VOCs from the surface of the fibre. In this way, practical optimal conditions were set with extraction time of 44 min, extraction temperature of 64 C and desorption time of 1 min, as a compromise between sensitivity and analytical throughput Volatile compounds for C. chinense sp. peppers Figs. 4 6 illustrate typical chromatograms, obtained at the optimized conditions for C. chinense sp. peppers, yellow, red and purple varieties. For each variety, the most significant VOCs were tentatively identified by comparing their mass spectra with the NIST electronic Mass Spectral Database, available in the equipment. It could be tentatively identified compounds such as alcohols, terpenes and esters of carboxylic acids. Amongst the most abundant, it was found the of pentanoic acid, dimethylcyclohexanols, humulene and esters of butanoic acid. Results are shown in Table 4, along with the occurrence or not of each VOC in the three varieties. It should be mentioned that in a previous study made by our group [27], it was observed that amongst the three peppers, the yellow variety presented the highest concentrations of capsaicinoids, namely 2.32 and 0.66 mg g 1 for capsaicine and dihydrocapsaicine, respectively, which turns it the more pungent Principal component analysis A multivariate analysis of the results was performed, in order to visualize grouping tendencies and possible latent variables which could distinguish potentially aroma constituents of red, yellow and purple varieties of C. chinense sp. peppers. The first 8 principal components explain 93% of the total variance, indicating that a reduced number of volatile compounds could explain the overall characteristics of the samples. Fig. 7 illustrates PC 1 versus PC 3 score and loading graphs, which are related to sample and variable profiles, respectively. This pair of principal components was chosen due to its clear discrimination amongst the groups of samples and the easiest relationship of these groups with VOCs. As can be seen in these figures, the score graph remarks clearly the grouping tendencies of the samples of each of the three kinds of pepper in this study. On the other hand, variables described by the loading graph and which are located at the same quadrant of a sample group in the scores graph are the most important ones in describing this particular sample group. In this way, PCA loading graph puts into evidence that compounds of numbers 6, 22 and 31 (pentanoic acid, ; tetradecane and humulene, as can be seen in Table 4) were related to purple peppers while compound of number 32 (4,11,11-Trimethyl-8-methylene bicyclo[7.2.0]undec-3-ene) was related to red peppers. By PCA, the most relevant VOCs to discriminate red and purple peppers agreed with the expected ones, since tetradecane Table 4 Retention times for identified volatile compounds of selected varieties of C. chinense sp. peppers No. Compound name Retention time/min Detected compound Red pepper Yellow pepper 1 Butanoic acid, 2-methyl, 6, methyl butyl ester 2 Butanoic acid, 3-methyl, methyl propyl ester 3 Butanoic acid, 3-methyl, pentyl ester 4 Propanoic acid, 2-methyl, Butanoic acid, 2-methyl, Pentanoic acid, β-citronellene Butanoic acid, 3-methyl, hexenyl ester 9 Butanoic acid, 3-methyl, Propanoic acid, 2-methyl, heptyl ester 11 2,9-dimethyl-5-decyne cyclobutanecarboxylic acid, Pentanoic acid, heptyl ester Hexane, 1-(hexyloxyl)-4 methyl Hexanoic acid, Butanoic acid, 2-methyl, heptyl ester 17 Butanoic acid, 3-methyl, octyl ester 18 Isomer of hexanoic acid, Tridecane, 2-methyl Cyclohexanol, 3,3 dimethyl Isomer of cyclohexanol, ,3 dimethyl 22 Tetradecane Pentanoic acid, decyl ester Germacrene D Valeric acid, decyl ester Pentanoic acid, decyl ester ,1-dimethyl-2-nonyl cyclopropane, 28 Tetradecane,2-methyl H-benzocycloheptene, ,4a,5,6,7,8,9a-octahydro- 3,5,5-trimethyl-9-methylene 30 Pentadecane Humulene ,11,11-Trimethyl-8-methylene bicyclo[7.2.0]undec-3-ene 33 Himachala-2,4-diene (C 15 H 24 ) Citronellyl n-propionate found compound and not found compound. Purple pepper and 4,11,11-Trimethyl-8-methylene bicyclo[7.2.0]undec-3-ene were compounds found only in purple and red pepper varieties (Table 4), respectively, allowing discrimination between these groups. The appointment of pentanoic acid, (variable 6), as a relevant VOC by PCA to describe purple pepper samples, may be related to its higher average percentile area in this variety

7 148 E.T. Sousa et al. / Microchemical Journal 82 (2006) PC3 Scores Y2 R5 R3 2 1 R6 R4 R1 R2 Red (A) Y5 0 Y4 Y1-1 P3 P4 P6 Y6-2 P2 P5 Y P RESULT1, X-expl: 44%,9% 0.3 PC3 X-loadings Purple Red Purple 6 Yellow (B) PC Yellow 28 4 PC RESULT1, X-expl: 44%,9% Fig. 7. Principal component analysis (A) scores and (B) loading graphs (see text for details). when compared to the red peppers, besides this compound have not been detected in yellow peppers. Results obtained by PCA also evidenced that yellow pepper variety presented a higher sample dispersion, which can be explained by its higher number of VOCs detected, in comparison to red and purple varieties, leading to a larger number of variables related to this group in loading graph. Principal component analysis had confirmed that VOCs which were detected in only one or two varieties (Table 4) were the most relevant to discriminate the groups under study. 4. Conclusions The strategy of multivariate experimental design made possible, with a small number of experiments (11 and 13 experiments for screening and optimization designs, respectively), the identification of the significant parameters related to the overall sensitivity and the optimization of experimental conditions for the proposed headspace SPME sampling method. The SPME method, coupled with GC-MS analysis, was able to tentatively identify 34 volatile organic compounds from three different varieties of C. chinense sp. peppers, while the principal component analysis turned possible the visualization of grouping tendencies of the studied varieties of pepper, as well as the identification of the volatile compounds responsible for discriminating the three groups. The significance of this work is further emphasized by its applicability to the study of food quality indicators, and as a tool for investigations on the composition of the pepper sources. Acknowledgments The present work was supported by the National Research Council of Brazil (CNPq), Fundação de Apoio a Pesquisa do

8 E.T. Sousa et al. / Microchemical Journal 82 (2006) Estado da Bahia (FAPESB) PRONEX/FAPESB/CNPq, Financiadora de Estudos e Projetos (FINEP) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authors wish also to thank for the valuable support of Marcelino C. Lobo, Nelson A. S. Santos and Zenira C. V. Viana. References [1] M.I.M. Mosquera, D.H. Mendez, J. Agric. Food Chem. 42 (1994) 38. [2] H.C. Heath, Source Book of Flavors, Van Nostrand Reinhold, New York, [3] R.I. Barbosa, F.J.F. Luz, H.R. Nascimento Filho, C.B. Maduro, Acta Amazon., Supl. 32 (2002) 177. [4] R.H. Cichewicz, P.A. Thorpe, J. Ethnopharmacol. 52 (1996) 61. [5] J. Molina-Torres, A. Garcia-Chávez, E. Ramirez-Chávez, J. Ethnopharmacol. 64 (1999) 241. [6] R. Otero, V. Nunez, J. Barona, R. Fonnegra, S.L. Jiménez, R.G. Osorio, M. Saldarriaga, A. Diaz, J. Ethnopharmacol. 73 (2000) 233. [7] L.M. Bartoshuk, Food Qual. Prefer. 4 (1993) 21. [8] O. Ceyan, A. Mavi, Ecol. Food Nutr. 38 (1999) 137. [9] J. Prescott, R.J. Stevenson, Food Rev. Int. 11 (1995) 665. [10] B.G. Green, Trends Food Sci. Technol. 7 (1996) 415. [11] K.H. Ney, Alimenta 29 (1990) 117. [12] M.M. Mazida, M.M. Salleh, H. Osman, J. Food Compos. Anal. 18 (2005) 427. [13] S. van Ruth, E. Boscaini, D. Mayr, J. Pugh, M. Posthumus, Int. J. Mass Spectrom (2003) 55. [14] H. Guth, W. Grosch, in: R. Teranishi, E.L. Wick, I. Hornstein (Eds.), Flavor Chemistry, Kluwer Academic Publishers/Plenum Press, New York, [15] R.L. Chitwood, R.M. Pangborn, W. Jenning, Food Chem. 11 (1983) [16] C. Wu, S. Liou, J. Agr. Food Chem. 34 (1986) 770. [17] K. Korany, N. Kocsis, M. Amtmann, Z. Mednyanszky, J. Food Compos. Anal. 15 (2002) 195. [18] K.E. Murray, F.B. Whitfield, J. Sci. Food. Agric. 26 (1975) 973. [19] L.W. Haymon, L.W. Aurant, J. Agric. Food. Chem. 19 (1971) [20] T. Jagella, W. Grosh, Eur. Food Res. Technol. 209 (1999) 16. [21] F.V. Parreira, Z.L. Cardeal, Quim. Nova 28 (2005) 646. [22] D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyer-Verbeke, Handbook of Chemometrics and Qualimetrics Part A, Elsevier, Amsterdam, [23] P.W. Araujo, R.G. Brereton, Trends Anal. Chem. 15 (1996) 26. [24] T.L. Lindstedt, E. Seifert, L. Abramo, B. Thelin, A. Nystro, J. Pettersen, R. Bergman, Chemom. Intell. Lab. Syst., Lab. Inf. Manag. 42 (1998) 3. [25] K.R. Beebe, R.J. Pell, M.B. Seasholtz, Chemometrics: A Practical Guide, Wiley, New York, [26] R.G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, Wiley, Chichester, [27] J.M.S. Barbosa, R.C.C. Lovatti, L.C. Azevedo, P.A.P. Pereira, J.B. de Andrade, Determinação de Capsaicinóides em Pimenta de Cheiro por CLAE-UV, Anais XIX Congresso Brasileiro de Ciência e Tecnologia de Alimentos, Recife-PE, 2004.

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