Abstract
Principal component analysis (PCA) is currently one of the most used multivariate data analysis techniques for evaluating information from food analysis. In this review, a brief introduction to the theoretical principles that underlie PCA will be given, in addition to presenting the most commonly used computer programs. An example from the literature was discussed to illustrate the use of this chemometric tool and interpretation of graphs and parameters obtained. A list of recently published articles will also be presented, in order to show the applicability and potential of the technique in the food analysis field.
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References
Abdi H, Williams LJ. Principal component analysis. WIRES Comput Stat. 2:433-459 (2010) https://doi.org/10.1002/wics.101
Aredes RS, Peixoto FC, Sphaier LA, Marques FFC. Evaluation of craft beers through the direct determination of amino acids by capillary electrophoresis and principal component analysis. Food Chemistry. 344: 128572 (2021) https://doi.org/10.1016/j.foodchem.2020.128572
Avian C, Leu JS, Prakosa SW, Faisal M. An improved classification of pork adulteration in beef based on electronic nose using modified deep extreme learning with principal component analysis as feature learning. Food Analytical Methods. 15:3020–3031 (2022) https://doi.org/10.1007/s12161-022-02361-9
Azilawati MI, Hashim DM, Jamilah B, Amin I. RP-HPLC method using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate incorporated with normalization technique in principal component analysis to differentiate the bovine, porcine and fish gelatins. Food Chemistry. 172:368–376 (2015) https://doi.org/10.1016/j.foodchem.2014.09.093
Beattie JR, Esmonde-White FWL. Exploration of principal component analysis: deriving principal component analysis visually using spectra. Applied Spectroscopy. 75:361–375 (2021) https://doi.org/10.1177/0003702820987847
Bro R, Smilde AK. Principal component analysis. Analytical Methods. 6:2812-2831 (2014) https://doi.org/10.1039/C3AY41907J
Calvini R, Ulrici A, Amigo JM. Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging. Chemometrics and Intelligent Laboratory Systems. 146:503–511 (2015) https://doi.org/10.1016/j.chemolab.2015.07.010
Camacho J, Smilde AK, Saccenti E, Westerhuis JA. All sparse PCA models are wrong, but some are useful. Part I: Computation of scores, residuals and explained variance. Chemometrics and Intelligent Laboratory Systems. 196:103907 (2020) https://doi.org/10.1016/j.chemolab.2019.103907
Caridi F, Mottese AF, Messina M, D'Agostino M. Fatty acids evaluation by principal component analysis for the traceability of Sicilian and Calabrian olive oils. Current Nutrition & Food Science 17:844-849 (2021) https://doi.org/10.2174/1573401317666210127105215
Cozzolino D, Power A, Chapman J. Interpreting and reporting principal component analysis in food science analysis and beyond. Food Analytical Methods. 12:2469–2473 (2019) https://doi.org/10.1007/s12161-019-01605-5
Farrugia J, Griffin S, Valdramidis VP, Camilleri K, Falzon O. Principal component analysis of hyperspectral data for early detection of mould in cheeselets. Current Research in Food Science. 4:18–27 (2021) https://doi.org/10.1016/j.crfs.2020.12.003
Fernández-Torres R, Pérez-Bernal JL, Bello-López MA, Callejón-Mochón M, Jiménez-Sánchez JC, Guiraúm-Pérez A. Mineral content and botanical origin of Spanish honeys. Talanta. 65:686-691 (2015) https://doi.org/10.1016/j.talanta.2004.07.030
Ferreira MMCF. Quimiometria: Conceitos, métodos e aplicações. Ed. Unicamp, Campinas. pp. 110–146. (2015).
Field A. Descobrindo a estatística usando o SPSS. Ed. Artmed, São Paulo (2009).
Gezek G, Hashemi P, Kalaycıoğlu Z, Kaygusuz H, Sarıoğlu G, Döker S, Dirmenci T, Erim FB. Evaluation of some Turkish Salvia species by principal component analysis based on their vitamin B2, mineral composition, and antioxidant properties. Lebensm-Wiss Technology. 100:287-293 (2019) https://doi.org/10.1016/j.lwt.2018.10.066
Ghosh D, Chattopadhyay P. Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products. Journal of Food Science Technology. 49:328–334 (2012) https://doi.org/10.1007/s13197-011-0280-9
Gomes DAS, Alves JPS, Silva EGP, Novaes CG, Silva DS, Aguiar RM, Araújo SA, Santos ACL, Bezerra MA. Evaluation of metal content in tea samples commercialized in sachets using multivariate data analysis techniques. Microchemical Journal. 151:104248 (2019) https://doi.org/10.1016/j.microc.2019.104248
Gosetti F, Chiuminatto U, Mazzucco E, Mastroianni R, Marengo E. Ultra-high-performance liquid chromatography/tandem high-resolution mass spectrometry analysis of sixteen red beverages containing carminic acid: Identification of degradation products by using principal component analysis/discriminant analysis. Food Chemistry. 167:454–462 (2015) https://doi.org/10.1016/j.foodchem.2014.07.026
Granato D, Santos JS, Escher GB, Ferreira BL, Maggio RM. Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology. 72:83-90 (2018) https://doi.org/10.1016/j.tifs.2017.12.006
Guellis C, Valério DC, Bessegato GG, Boroski M, Dragunski JC, Lindino CA. Non-targeted method to detect honey adulteration: Combination of electrochemical and spectrophotometric responses with principal component analysis. Journal of Food Composition and Analysis. 89:103466 (2020) https://doi.org/10.1016/j.jfca.2020.103466
Gumus ZP, Ertas H, Yasar E, Gumus O. Classification of olive oils using chromatography, principal component analysis and artificial neural network modelling. Food Measurements. 12:1325–1333 (2018) https://doi.org/10.1007/s11694-018-9746-z.
Hair JF, Black WC, Babin B, Anderson RE, Tatham RL. Análise multivariada de dados. 6th ed. Ed. Bookman, Porto Alegre (2009)
Hou D, O’Connor D, Igalavithana AD. Alessi DS, Luo J, Tsang DCW, Sparks DL, Yamauchi Y, Rinklebe J, Ok YS. Metal contamination and bioremediation of agricultural soils for food safety and sustainability. Nature Reviews Earth & Environmental. 1:366–381 (2020) https://doi.org/10.1038/s43017-020-0061-y
Ismail AM, Sani MSA, Azid A, Zaki NNM, Arshad S, Tukiran NA, Abidin SASZ, Samsudin MS, Ismail A. Food forensics on gelatine source via ultra-high-performance liquid chromatography diode-array detector and principal component analysis. SN Applied Science. 3:79 (2021) https://doi.org/10.1007/s42452-020-04061-7
Iwaniak A, Hrynkiewicz M, Bucholska J, Darewicz M, Minkiewicz P. Structural characteristics of food protein-originating di- and tripeptides using principal component analysis. Europe Food Research Technology. 244:1751–1758 (2018)
Jackson JE, Oblimin Rotation. Encyclopedia of Biostatistics (2005) : https://doi.org/10.1002/0470011815.b2a13060
Jollife IT, Cadima J. Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A. 374:20150202 (2016) https://doi.org/10.1098/rsta.2015.0202
Kalaycıoğlu Z, Kaygusuz H, Döker S, Kolaylı S, Erim, FB. Characterization of Turkish honeybee pollens by principal component analysis based on their individual organic acids, sugars, minerals, and antioxidant activities. Lebensm-Wiss Technology. 84:402-408 (2017) https://doi.org/10.1016/j.lwt.2017.06.003
Kozak M, Scaman, CH. Unsupervised classification methods in food sciences: discussion and outlook. Journal of Science Food Agriculture. 88:1115–1127 (2008) https://doi.org/10.1002/jsfa.3215
Lee B, Lin P, Cha HS, Luo J, Chen F. Characterization of volatile compounds in Cowart muscadine grape (Vitis rotundifolia) during ripening stages using GC-MS combined with principal component analysis. Food Science and Biotechnology. 25:1319–1326 (2016) https://doi.org/10.1007/s10068-016-0207-3
Li L, Li B, Zhang Q, Gong L, Meng X. Use of principal component and hierarchical cluster analysis to characterise strawberries. Oxid Community. 39:118-131 (2016)
Lima MAS, dos Santos LO, David JM, Ferreira SLC. Mineral content in mustard leaves according to the cooking method. Food Chemistry. 273:172–177 (2019) https://doi.org/10.1016/j.foodchem.2017.12.042
Matos D, Abud S, Castilho ER. Análise fatorial. Ed. Enap, Brasília (2019).
Matuk J, Herring AH, Dunson DB. Bayesian functional principal components analysis using relaxed mutually orthogonal processes. (2022). https://doi.org/10.48550/arXiv.2205.12361
McLeod LD, Swygert KA, Thissen D. in Factor analysis for items scored in two categories, Thissen D, Wainer H. (Eds.), Test scoring. Taylor and Francis. pp. 189–216 (2001).
Mingoti SA. Análise de dados através de métodos de estatística multivariada: uma abordagem prática. Ed. UFMG, Belo Horizonte. (2005)
Morawski RZ, Miękina A. Application of principal components analysis and signal-to-noise ratio for calibration of spectrophotometric analyzers of food. Measurement. 79:302–310 (2016) https://doi.org/10.1016/j.measurement.2015.10.026
Moussawi SN, Ouaini R, Matta J, Chébib H, Cladière M, Camel V. Simultaneous migration of bisphenol compounds and trace metals in canned vegetable food. Food Chemisry. 288:228-238 (2019) https://doi.org/10.1016/j.foodchem.2019.02.116
Otto M. Chemometrics: Statistics and computer application in analytical chemistry. Wiley-VCH, Weinheim, pp. 121-134 (2007)
Patras A, Brunton NP, Downey G, Rawson A, Warriner K, Gernigon G. Application of principal component and hierarchical cluster analysis to classify fruits and vegetables commonly consumed in Ireland based on in vitro antioxidant activity. Journal of Food Composition and Analysis. 24:250–256 (2011) https://doi.org/10.1016/j.jfca.2010.09.012
Peng X, Li X, Shi X, Guo S. Evaluation of the aroma quality of Chinese traditional soy paste during storage based on principal component analysis. Food Chemistry. 151:532–538 (2014) https://doi.org/10.1016/j.foodchem.2013.11.095
Porízka P, Klus J, Képeš E, Prochazka D, Hahn D, Kaiser J. On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review. Spectrochimica Acta B. 148:65-82 (2018) https://doi.org/10.1016/j.sab.2018.05.030
Ramos GR, Álvares-Coque MCG. Quimometria 1. Ed Sintesis, Madrid, pp. 133–152 (2001)
Ranamukhaarachchi SA, Peiris RH, Moresoli C. Fluorescence spectroscopy and principal component analysis of soy protein hydrolysate fractions and the potential to assess their antioxidant capacity characteristics. Food Chemistry. 217:469–475 (2017) https://doi.org/10.1016/j.foodchem.2016.08.029
Rodrigues HC, Leme LM, Paulino HFS, Pilau EJ, Valderrama P, Março PH. Non-targeted metabolite profiling to evaluate the drying process effect in the Peruvian maca actives through principal component analysis. Food Analytical Methods. 15:3225–3231 (2022) https://doi.org/10.1007/s12161-022-02378-0
Shang HL. A survey of functional principal component analysis. AStA Adv Stat Anal. 98:121–142 (2014) https://doi.org/10.1007/s10182-013-0213-1
Shendy AH, Eltanany BM, Al-Ghobashy MA, Gadalla SA, Mamdouh W, Lotfy HM. Coupling of GC-MS/MS to principal component analysis for assessment of matrix effect: efficient determination of ultra-low levels of pesticide residues in some functional foods. Food Analytical Methods. 12:2870–2885 (2019) https://doi.org/10.1007/s12161-019-01643-z.
Shi G, Shen X, Ren H, Rao Y, Weng S, Tang X. Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy. Frontier in Plant Science. 13:956778 (2022) https://doi.org/10.3389/fpls.2022.956778
Shima J, Cho Y, Lee K, An H, Lee C. Multivariate analysis of metals contents in spices commonly consumed in republic of Korea. Food Additives & Contaminants B. 14:184-192 (2021) https://doi.org/10.1080/19393210.2021.1914196
Silva ES, Silva EGP, Silva DS, Novaes CG, Amorim FAC, Santos MJS, Bezerra MA. Evaluation of macro and micronutrient elements content from soft drinks using principal component analysis and Kohonen self-organizing maps. Food Chemistry. 273:9–14 (2019) https://doi.org/10.1016/j.foodchem.2018.06.021
Souza AM, Poppi RJ. Experimento didático de quimiometria para análise exploratória de óleos vegetais comestíveis por espectroscopia no infravermelho médio e análise de componentes principais: um tutorial, parte I. Quimica Nova. 35:223-229 (2012) https://doi.org/10.1590/S0100-40422012000100039
Valderrama L, Paiva VB, Março PH, Valderrama P. Proposta experimental didática para o ensino de análise de componentes principais. Química Nova. 39:245-249 (2016) https://doi.org/10.5935/0100-4042.20150166
Wang Q, Jin G, Jin Y, Ma M, Wang N, Liu C, He L. Discriminating eggs from different poultry species by fatty acids and volatiles profiling: Comparison of SPME-GC/MS, electronic nose, and principal component analysis method. European Journal Lipid Science Technology. 116:1044-1053 (2014) https://doi.org/10.1002/ejlt.201400016
Werteker M, Huber S, Kuchling S, Rossmann B, Schreiner M. Differentiation of milk by fatty acid spectra and principal component analysis. Measurement. 98:311–320 (2017) https://doi.org/10.1016/j.measurement.2016.10.059
Wong C, Chan GK, Zhang M, Yao P, Lin H, Dong TT, Li G, Lai X, Tsim KW. Characterization of edible bird’s nest by peptide fingerprinting with principal component analysis. Food Quality and Safety. 1:83–92 (2017) https://doi.org/10.1093/fqsafe/fyx002
Wu Y, Lv S, Lian M, Wang C, Gao X, Meng Q. Study of characteristic aroma components of baked Wujiatai green tea by HS-SPME/GC-MS combined with principal component analysis. CyTA Journal of Food. 14:423-432 (2016) https://doi.org/10.1080/19476337.2015.1123298
Yang W, Hu M, Chen S, Wang Q, Zhu S, Dai J, Li X. Identification of adulterated cocoa powder using chromatographic fingerprints of polysaccharides coupled with principal component analysis. Food Analytical Methods. 8:2360–2367 (2015) https://doi.org/10.1007/s12161-015-0126-6
Yi T, Zhu L, Peng W, He X, Chen H, Li J, Yu T, Liang Z, Zhao Z, Chen H. Comparison of ten major constituents in seven types of processed tea using HPLC-DAD-MS followed by principal component and hierarchical cluster analysis. Lebensm-Wiss Technology. 62:194-201 (2015) https://doi.org/10.1016/j.lwt.2015.01.003
Zarpelon J, Molognoni L, Valese AC, Ribeiro DHB, Daguer H. Validation of an automated method for the analysis of fat content of dulce de leche. Journal of Food Composition and Analysis. 47:1-7 (2016) https://doi.org/10.1016/j.jfca.2015.12.011
Zheng X, Nie Y, Gao Y, Huang B, Ye J, Lu J, Liang Y. Screening the cultivar and processing factors based on the flavonoid profiles of dry teas using principal component analysis. Journal of Food Composition and Analysis. 67:29-37 (2018) https://doi.org/10.1016/j.jfca.2017.12.016
Acknowledgements
The authors would like to acknowledge the financial support of the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq. Grant Number 310949/2021-1) and Financiadora de Estudos e Projetos (FINEP).
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Souza, A.S., Bezerra, M.A., Cerqueira, U.M.F.M. et al. An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples. Food Sci Biotechnol 33, 1323–1336 (2024). https://doi.org/10.1007/s10068-023-01509-5
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DOI: https://doi.org/10.1007/s10068-023-01509-5