Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing.

التفاصيل البيبلوغرافية
العنوان:	Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing.
المؤلفون:	Granato D; Dept. of Food Engineering, State Univ. of Ponta Grossa, Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, Brazil., Putnik P; Faculty of Food Technology and Biotechnology, Univ. of Zagreb, Pierottijeva 6, 10000, Zagreb, Croatia., Kovačević DB; Faculty of Food Technology and Biotechnology, Univ. of Zagreb, Pierottijeva 6, 10000, Zagreb, Croatia., Santos JS; Dept. of Food Engineering, State Univ. of Ponta Grossa, Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, Brazil., Calado V; School of Chemistry, Federal Univ. of Rio de Janeiro, Rio de Janeiro, Brazil., Rocha RS; Dept. de Alimentos, Inst. Federal de Educação, Ciência e Tecnologia (IFRJ), 20270-021, Rio de Janeiro, Brazil., Cruz AGD; Dept. de Alimentos, Inst. Federal de Educação, Ciência e Tecnologia (IFRJ), 20270-021, Rio de Janeiro, Brazil., Jarvis B; Dept. of Food and Nutrition Sciences, School of Chemistry, Food and Pharmacy, The Univ. of Reading, Whiteknights, Reading, Berkshire RG6 6AP, U.K., Rodionova OY; Semenov Inst. of Chemical Physics RAS, Kosygin str. 4, 119991, Moscow, Russia., Pomerantsev A; Semenov Inst. of Chemical Physics RAS, Kosygin str. 4, 119991, Moscow, Russia.
المصدر:	Comprehensive reviews in food science and food safety [Compr Rev Food Sci Food Saf] 2018 May; Vol. 17 (3), pp. 663-677. Date of Electronic Publication: 2018 Mar 30.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Institute of Food Technologists Country of Publication: United States NLM ID: 101305205 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-4337 (Electronic) Linking ISSN: 15414337 NLM ISO Abbreviation: Compr Rev Food Sci Food Saf Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة:	Original Publication: Chicago, Ill. : Institute of Food Technologists
مستخلص:	In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data. (© 2018 Institute of Food Technologists®.)
References:	Alañón, M. E., Pérez-Coello, M. S., & Marina, M. L. (2015). Wine science in the metabolomics era. TrAC Trends in Analytical Chemistry, 74, 1-20. https://doi.org/10.1016/j.trac.2015.05.006. Alewijn, M., van der Voet, H., & van Ruth, S. (2016). Validation of multivariate classification methods using analytical fingerprints - concept and case study on organic feed for laying hens. Journal of Food Composition and Analysis, 51, 15-23. https://doi.org/10.1016/j.jfca.2016.06.003. Alonso-Salces, R. M., Serra, F., Reniero, F., & Heberger, K. (2009). Botanical and geographical characterization of green coffee (Coffea arabica and Coffea canephora): Chemometric evaluation of phenolic and methylxanthine contents. Journal of Agricultural and Food Chemistry, 57(10), 4224-4235. https://doi.org/10.1021/jf8037117. Azcarate, S. M., Gil, R., Smichowski, P., Savio, M., & Camiña, J. M. (2017). Chemometric application in foodomics: Nutritional quality parameters evaluation in milk-based infant formula. Microchemical Journal, 130, 1-6. https://doi.org/10.1016/j.microc.2016.07.016. Bajoub, A., Medina-Rodríguez, S., Gómez-Romero, M., Ajal, E. A., Bagur-González, M. G., Fernández-Gutiérrez, A., & Carrasco-Pancorbo, A. (2017). Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics. Food Chemistry, 215, 245-255. https://doi.org/10.1016/j.foodchem.2016.07.140. Beebe, K. R., Pell, R. J., & Seasholtz, M. B. (1998). Chemometrics: A practical guide (1st ed). New York: Wiley & Sons. Berrueta, L. A., Alonso-Salces, R. M., & Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158(1-2), 196-214. https://doi.org/10.1016/j.chroma.2007.05.024. Bevilacqua, M., Bro, R., Marini, F., Rinnan, Å., Rasmussen, M. A., & Skov, T. (2017). Recent chemometrics advances for foodomics. TrAC Trends in Analytical Chemistry, 96, 42-51. https://doi.org/10.1016/j.trac.2017.08.011. Boareto, Á. J. M., De Souza, M. B., Valero, F., & Valdman, B. (2007). A hybrid neural model (HNM) for the on-line monitoring of lipase production by Candida rugosa. Journal of Chemical Technology & Biotechnology, 82(3), 319-327. https://doi.org/10.1002/jctb.1678. Borràs, E., Ferré, J., Boqué, R., Mestres, M., Aceña, L., & Busto, O. (2015). Data fusion methodologies for food and beverage authentication and quality assessment - A review. Analytica Chimica Acta, 891, 1-14. https://doi.org/10.1016/j.aca.2015.04.042. Boussard, A., Cordella, C. B. Y., Rakotozafy, L., Moulin, G., Buche, F., Potus, J., & Nicolas, J. (2012). Use of chemometric tools to estimate the effects of the addition of yeast, glucose-oxidase, soybean or horse bean flours to wheat flour on biochemical bread dough characteristics. Chemometrics and Intelligent Laboratory Systems, 113, 68-77. https://doi.org/10.1016/j.chemolab.2012.01.006. Brandily, M. L., Monbet, V., Bureau, B., Boussard-Plédel, C., Loréal, O., Adam, J. L., & Sire, O. (2011). Identification of foodborne pathogens within food matrices by IR spectroscopy. Sensors and Actuators B: Chemical, 160(1), 202-206. https://doi.org/10.1016/j.snb.2011.07.034. Bruckner, S., Albrecht, A., Petersen, B., & Kreyenschmidt, J. (2012). Influence of cold chain interruptions on the shelf life of fresh pork and poultry. International Journal of Food Science & Technology, 47(8), 1639-1646. https://doi.org/10.1111/j.1365-2621.2012.03014.x. Bursać Kovačević, D., Gajdoš Kljusurić, J., Putnik, P., Vukušić, T., Herceg, Z., & Dragović-Uzelac, V. (2016). Stability of polyphenols in chokeberry juice treated with gas phase plasma. Food Chemistry, 212, 323-331. https://doi.org/10.1016/j.foodchem.2016.05.192. Bursać Kovačević, D., Putnik, P., Dragović-Uzelac, V., Vahčić, N., Babojelić, M. S., & Levaj, B. (2015). Influences of organically and conventionally grown strawberry cultivars on anthocyanins content and color in purees and low-sugar jams. Food Chemistry, 181, 94-100. https://doi.org/10.1016/j.foodchem.2015.02.063. Camin, F., Boner, M., Bontempo, L., Fauhl-Hassek, C., Kelly, S. D., Riedl, J., & Rossmann, A. (2017). Stable isotope techniques for verifying the declared geographical origin of food in legal cases. Trends in Food Science & Technology, 61, 176-187. https://doi.org/10.1016/j.tifs.2016.12.007. Cebi, N., Durak, M. Z., Toker, O. S., Sagdic, O., & Arici, M. (2016). An evaluation of Fourier transforms infrared spectroscopy method for the classification and discrimination of bovine, porcine and fish gelatins. Food Chemistry, 190, 1109-1115. https://doi.org/10.1016/j.foodchem.2015.06.065. Charlebois, S., Schwab, A., Henn, R., & Huck, C. W. (2016). Food fraud: An exploratory study for measuring consumer perception towards mislabeled food products and influence on self-authentication intentions. Trends in Food Science & Technology, 50, 211-218. https://doi.org/10.1016/j.tifs.2016.02.003. Chiesa, L., Panseri, S., Bonacci, S., Procopio, A., Zecconi, A., Arioli, F., … Moreno-Rojas, J. M. (2016). Authentication of Italian PDO lard using NIR spectroscopy, volatile profile and fatty acid composition combined with chemometrics. Food Chemistry, 212, 296-304. https://doi.org/10.1016/j.foodchem.2016.05.180. Como, F., Carnesecchi, E., Volani, S., Dorne, J. L., Richardson, J., Bassan, A., … Benfenati, E. (2017). Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model. Chemosphere, 166, 438-444. https://doi.org/10.1016/j.chemosphere.2016.09.092. Cubero-Leon, E., De Rudder, O., & Maquet, A. (2018). Metabolomics for organic food authentication: Results from a long-term field study in carrots. Food Chemistry, 239, 760-770. https://doi.org/10.1016/j.foodchem.2017.06.161. D'Archivio, A. A., Giannitto, A., Maggi, M. A., & Ruggieri, F. (2016). Geographical classification of Italian saffron (Crocus sativus L.) based on chemical constituents determined by high-performance liquid-chromatography and by using linear discriminant analysis. Food Chemistry, 212, 110-116. https://doi.org/10.1016/j.foodchem.2016.05.149. Danezis, G. P., Tsagkaris, A. S., Camin, F., Brusic, V., & Georgiou, C. A. (2016). Food authentication: Techniques, trends & emerging approaches. TrAC Trends in Analytical Chemistry, 85, 123-132. https://doi.org/10.1016/j.trac.2016.02.026. Davis, R., Paoli, G., & Mauer, L. J. (2012). Evaluation of Fourier transform infrared (FT-IR) spectroscopy and chemometrics as a rapid approach for sub-typing Escherichia coli O157:H7 isolates. Food Microbiology, 31(2), 181-190. https://doi.org/10.1016/j.fm.2012.02.010. De Luca, M., Restuccia, D., Clodoveo, M. L., Puoci, F., & Ragno, G. (2016). Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes. Food Chemistry, 202, 432-437. https://doi.org/10.1016/j.foodchem.2016.02.018. Derde, M. P., & Massart, D. L. (1988). Comparison of the performance of the class modelling techniques UNEQ, SIMCA, and PRIMA. Chemometrics and Intelligent Laboratory Systems, 4(1), 65-93. https://doi.org/10.1016/0169-7439(88)80013-3. Do, T. K. T., Hadji-Minaglou, F., Antoniotti, S., & Fernandez, X. (2015). Authenticity of essential oils. TrAC Trends in Analytical Chemistry, 66, 146-157. https://doi.org/10.1016/j.trac.2014.10.007. Dong, W., Zhao, J., Hu, R., Dong, Y., & Tan, L. (2017). Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chemistry, 229, 743-751. https://doi.org/10.1016/j.foodchem.2017.02.149. dos Santos, C. A. T., Páscoa, R. N. M. J., & Lopes, J. A. (2017). A review on the application of vibrational spectroscopy in the wine industry: From soil to bottle. TrAC Trends in Analytical Chemistry, 88, 100-118. https://doi.org/10.1016/j.trac.2016.12.012. Drivelos, S. A., Danezis, G. P., Haroutounian, S. A., & Georgiou, C. A. (2016). Rare earth elements minimal harvest year variation facilitates robust geographical origin discrimination: The case of PDO “Fava Santorinis”. Food Chemistry, 213, 238-245. https://doi.org/10.1016/j.foodchem.2016.06.088. Duflos, G., Coin, V. M., Cornu, M., Antinelli, J.-F., & Malle, P. (2006). Determination of volatile compounds to characterize fish spoilage using headspace/mass spectrometry and solid-phase microextraction/gas chromatography/mass spectrometry. Journal of the Science of Food and Agriculture, 86(4), 600-611. https://doi.org/10.1002/jsfa.2386. Duflos, G., Leduc, F., N'Guessan, A., Krzewinski, F., Kol, O., & Malle, P. (2010). Freshness characterisation of whiting (Merlangius merlangus) using an SPME/GC/MS method and a statistical multivariate approach. Journal of the Science of Food and Agriculture, 90(15), 2568-2575. https://doi.org/10.1002/jsfa.4122. Dumancas, G. G., Ramasahayam, S., Bello, G., Hughes, J., & Kramer, R. (2015). Chemometric regression techniques as emerging, powerful tools in genetic association studies. TrAC Trends in Analytical Chemistry, 74, 79-88. https://doi.org/10.1016/j.trac.2015.05.007. Dziurkowska, E., & Wesolowski, M. (2015). Multivariate statistical analysis as a supplementary tool for interpretation of variations in salivary cortisol level in women with major depressive disorder. The Scientific World Journal, 2015, 1-8. https://doi.org/10.1155/2015/987435. Fan, F. H., Ma, Q., Ge, J., Peng, Q. Y., Riley, W. W., & Tang, S. Z. (2013). Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. Journal of Food Engineering, 118(4), 426-433. https://doi.org/10.1016/j.jfoodeng.2013.04.015. Fidelis, M., Santos, J. S., Coelho, A. L. K., Rodionova, O. Y., Pomerantsev, A., & Granato, D. (2017). Authentication of juices from antioxidant and chemical perspectives: A feasibility quality control study using chemometrics. Food Control, 73, 796-805. https://doi.org/10.1016/j.foodcont.2016.09.043. Funes, E., Allouche, Y., Beltrán, G., & Jiménez, A. (2015). A review: Artificial neural networks as tool for control food industry process. Journal of Sensor Technology, 05(01), 28-43. https://doi.org/10.4236/jst.2015.51004. Gad, H. A., & Bouzabata, A. (2017). Application of chemometrics in quality control of Turmeric (Curcuma longa) based on ultra-violet, Fourier transform-infrared and 1H NMR spectroscopy. Food Chemistry, 237, 857-864. https://doi.org/10.1016/j.foodchem.2017.06.022. Gao, F., Zhou, S., Han, L., Yang, Z., & Liu, X. (2017). A novel FT-IR spectroscopic method based on lipid characteristics for qualitative and quantitative analysis of animal-derived feedstuff adulterated with ruminant ingredients. Food Chemistry, 237, 342-349. https://doi.org/10.1016/j.foodchem.2017.05.011. Gondim, C. d. S., Junqueira, R. G., Souza, S. V. C. d., Ruisánchez, I., & Callao, M. P. (2017). Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies. Food Chemistry, 230, 68-75. https://doi.org/10.1016/j.foodchem.2017.03.022. Granato, D., Koot, A., & van Ruth, S. M. (2015). Geographical provenancing of purple grape juices from different farming systems by proton transfer reaction mass spectrometry using supervised statistical techniques. Journal of the Science of Food and Agriculture, 95(13), 2668-2677. https://doi.org/10.1002/jsfa.7001. Granato, D., Margraf, T., Brotzakis, I., Capuano, E., & van Ruth, S. M. (2015). Characterization of conventional, biodynamic, and organic purple grape juices by chemical markers, antioxidant capacity, and instrumental taste profile. Journal of Food Science, 80(1), C55-C65. https://doi.org/10.1111/1750-3841.12722. Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). 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 and Technology, 72, 83-90. Grunert, T., Stephan, R., Ehling-Schulz, M., & Johler, S. (2016). Fourier transform infrared spectroscopy enables rapid differentiation of fresh and frozen/thawed chicken. Food Control, 60, 361-364. https://doi.org/10.1016/j.foodcont.2015.08.016. Guo, W.-L., Du, Y.-P., Zhou, Y.-C., Yang, S., Lu, J.-H., Zhao, H.-Y., … Teng, L.-R. (2011). At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement. World Journal of Microbiology and Biotechnology, 28(3), 993-1002. https://doi.org/10.1007/s11274-011-0897-x. Gurbanov, R., Gozen, A. G., & Severcan, F. (2018). Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 189, 282-290. https://doi.org/10.1016/j.saa.2017.08.038. Hamdouche, Y., Meile, J. C., Nganou, D. N., Durand, N., Teyssier, C., & Montet, D. (2016). Discrimination of post-harvest coffee processing methods by microbial ecology analyses. Food Control, 65, 112-120. https://doi.org/10.1016/j.foodcont.2016.01.022. Herceg, Z., Kovačević, D. B., Kljusurić, J. G., Jambrak, A. R., Zorić, Z., & Dragović-Uzelac, V. (2016). Gas phase plasma impact on phenolic compounds in pomegranate juice. Food Chemistry, 190, 665-672. https://doi.org/10.1016/j.foodchem.2015.05.135. Hidalgo, B., & Goodman, M. (2013). Multivariate or multivariable regression? American Journal of Public Health, 103(1), 39-40. https://doi.org/10.2105/ajph.2012.300897. Hong, E., Lee, S. Y., Jeong, J. Y., Park, J. M., Kim, B. H., Kwon, K., & Chun, H. S. (2017). Modern analytical methods for the detection of food fraud and adulteration by food category. Journal of the Science of Food and Agriculture, 97(12), 3877-3896. https://doi.org/10.1002/jsfa.8364. Kanik, E. A., Orekici Temel, G., Erdogan, S., & Ersoz Kaya, I. (2013). Affected states soft independent modeling by class analogy from the relation between independent variables, number of independent variables and sample size. Balkan Medical Journal, 30(1), 28-32. https://doi.org/10.5152/balkanmedj.2012.070. Karabagias, I. K., Louppis, A. P., Karabournioti, S., Kontakos, S., Papastephanou, C., & Kontominas, M. G. (2017). Characterization and geographical discrimination of commercial Citrus spp. honeys produced in different Mediterranean countries based on minerals, volatile compounds and physicochemical parameters, using chemometrics. Food Chemistry, 217, 445-455. https://doi.org/10.1016/j.foodchem.2016.08.124. Karoui, R., Downey, G., & Blecker, C. (2010). Mid-infrared spectroscopy coupled with chemometrics: A tool for the analysis of intact food systems and the exploration of their molecular structure−quality relationships − A review. Chemical Reviews, 110(10), 6144-6168. https://doi.org/10.1021/cr100090k. Kim, S., Kim, H., Reuhs, B. L., & Mauer, L. J. (2006). Differentiation of outer membrane proteins from Salmonellaenterica serotypes using Fourier transform infrared spectroscopy and chemometrics. Letters in Applied Microbiology, 42(3), 229-234. https://doi.org/10.1111/j.1472-765X.2005.01828.x. Kim, S., Reuhs, B. L., & Mauer, L. J. (2005). Use of Fourier transform infrared spectra of crude bacterial lipopolysaccharides and chemometrics for differentiation of Salmonella enterica serotypes. Journal of Applied Microbiology, 99(2), 411-417. https://doi.org/10.1111/j.1365-2672.2005.02621.x. Kuuliala, L., Abatih, E., Ioannidis, A. G., Vanderroost, M., De Meulenaer, B., Ragaert, P., & Devlieghere, F. (2018). Multivariate statistical analysis for the identification of potential seafood spoilage indicators. Food Control, 84, 49-60. https://doi.org/10.1016/j.foodcont.2017.07.018. Larson-Hall, J. (2010). 2.1.1 Levels of measurement of variables in a guide to doing statistics. In Second language research using SPSS (pp. 33-35). New York: Taylor & Francis. Lei, V., & Jakobsen, M. (2004). Microbiological characterization and probiotic potential of koko and koko sour water, African spontaneously fermented millet porridge and drink. Journal of Applied Microbiology, 96(2), 384-397. https://doi.org/10.1046/j.1365-2672.2004.02162.x. Lohani, U. C., & Muthukumarappan, K. (2017). Modeling of continuous ultrasonication to improve total phenolic content and antioxidant activity in sorghum flour: A comparison between response surface methodology and artificial neural network. International Journal of Food Engineering, 13(4). https://doi.org/10.1515/ijfe-2016-0086. Longobardi, F., Innamorato, V., Di Gioia, A., Ventrella, A., Lippolis, V., Logrieco, A. F., … Agostiano, A. (2017). Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses. Food Chemistry, 237, 743-748. https://doi.org/10.1016/j.foodchem.2017.05.159. Lucci, P., Saurina, J., & Núñez, O. (2017). Trends in LC-MS and LC-HRMS analysis and characterization of polyphenols in food. TrAC Trends in Analytical Chemistry, 88, 1-24. https://doi.org/10.1016/j.trac.2016.12.006. Manning, L. (2016). Food fraud: Policy and food chain. Current Opinion in Food Science, 10, 16-21. https://doi.org/10.1016/j.cofs.2016.07.001. Margraf, T., Santos, É. N. T., de Andrade, E. F., van Ruth, S. M., & Granato, D. (2016). Effects of geographical origin, variety and farming system on the chemical markers and in vitro antioxidant capacity of Brazilian purple grape juices. Food Research International, 82, 145-155. https://doi.org/10.1016/j.foodres.2016.02.003. Marseglia, A., Acquotti, D., Consonni, R., Cagliani, L. R., Palla, G., & Caligiani, A. (2016). HR MAS 1H NMR and chemometrics as useful tool to assess the geographical origin of cocoa beans - Comparison with HR 1H NMR. Food Research International, 85, 273-281. https://doi.org/10.1016/j.foodres.2016.05.001. Mikš-Krajnik, M., Yoon, Y.-J., Ukuku, D. O., & Yuk, H.-G. (2016). Volatile chemical spoilage indexes of raw Atlantic salmon (Salmo salar) stored under aerobic condition in relation to microbiological and sensory shelf lives. Food Microbiology, 53, 182-191. https://doi.org/10.1016/j.fm.2015.10.001. Munck, L., Nørgaard, L., Engelsen, S. B., Bro, R., & Andersson, C. A. (1998). Chemometrics in food science-a demonstration of the feasibility of a highly exploratory, inductive evaluation strategy of fundamental scientific significance. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 31-60. https://doi.org/10.1016/s0169-7439(98)00074-4. Muñiz-Valencia, R., Jurado, J. M., Ceballos-Magaña, S. G., Alcázar, Á., & Hernández-Díaz, J. (2014). Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks. Journal of Food Composition and Analysis, 34(1), 7-11. https://doi.org/10.1016/j.jfca.2014.02.003. Nagel, O. G., Molina, M. P., & Althaus, R. L. (2011). Optimization of bioassay for tetracycline detection in milk by means of chemometric techniques. Letters in Applied Microbiology, 52(3), 245-252. https://doi.org/10.1111/j.1472-765X.2010.02990.x. Nagel, O. G., Molina, M. P., & Althaus, R. L. (2012). Use chemometric techniques in the optimization of a specific bioassay for betalactams in milk. Letters in Applied Microbiology, 54(1), 32-38. https://doi.org/10.1111/j.1472-765X.2011.03169.x. Nagel, O. G., Zapata, M. D. L. L., Basílico, J., Bertero, J., Molina, M. P., & Althaus, R. L. (2009). Effect of chloramphenicol on a bioassay response for the detection of tetracycline residues in milk. Journal of Food and Drug Analysis 17(1), 36-42. Nascimento, K., Sattler, J. A., Macedo, L. F. L., Gonzalez, C., Melo, I., Araujo, E., … Muradian, L. B. A. (2018). Phenolic compounds, antioxidant capacity and physicochemical properties of Brazilian Apis mellifera honeys. LWT-Food Science and Technology, 91, 85-94. Nunes, C. A., Alvarenga, V. O., de Souza Sant'Ana, A., Santos, J. S., & Granato, D. (2015). The use of statistical software in food science and technology: Advantages, limitations and misuses. Food Research International, 75, 270-280. https://doi.org/10.1016/j.foodres.2015.06.011. O'Farrell, M., Lewis, E., Flanagan, C., Lyons, W., & Jackman, N. (2005a). Comparison of k-NN and neural network methods in the classification of spectral data from an optical fibre-based sensor system used for quality control in the food industry. Sensors and Actuators B: Chemical, 111-112, 354-362. https://doi.org/10.1016/j.snb.2005.02.003. O'Farrell, M., Lewis, E., Flanagan, C., Lyons, W. B., & Jackman, N. (2005b). Combining principal component analysis with an artificial neural network to perform online quality assessment of food as it cooks in a large-scale industrial oven. Sensors and Actuators B: Chemical, 107(1), 104-112. https://doi.org/10.1016/j.snb.2004.09.050. Obranović, M., Škevin, D., Kraljić, K., Pospišil, M., Neđeral, S., Blekić, M., & Putnik, P. (2015). Influence of climate, varieties and production process on tocopherols, plastochromanol-8 and pigments in flaxseed oil. Food Technology and Biotechnology, 53, 496-504. https://10.17113/ftb.53.04.15.4252. Oliveri, P., Casale, M., Casolino, M. C., Baldo, M. A., Nizzi Grifi, F., & Forina, M. (2010). Comparison between classical and innovative class-modelling techniques for the characterisation of a PDO olive oil. Analytical and Bioanalytical Chemistry, 399(6), 2105-2113. https://doi.org/10.1007/s00216-010-4377-1. Oliveri, P., & Downey, G. (2012). Multivariate class modeling for the verification of food-authenticity claims. TrAC Trends in Analytical Chemistry, 35, 74-86. https://doi.org/10.1016/j.trac.2012.02.005. Oliveri, P., & Simonetti, R. (2016). Chemometrics for food authenticity applications. In G. Downey (Ed.), Advances in food authenticity testing (1st ed, pp. 701-728). Amsterdam: Elsevier. Pacella, M., & Semeraro, Q. (2014). Application of neural-based algorithms as statistical tools for quality control of manufacturing processes. In D. Granato & G. Ares (Eds.), Mathematical and statistical methods in food science and technology (pp. 431-448). Los Angeles: Wiley & Sons. Pardo-Mates, N., Vera, A., Barbosa, S., Hidalgo-Serrano, M., Núñez, O., Saurina, J., & . . . Puignou, L. (2017). Characterization, classification and authentication of fruit-based extracts by means of HPLC-UV chromatographic fingerprints, polyphenolic profiles and chemometric methods. Food Chemistry, 221, 29-38. https://doi.org/10.1016/j.foodchem.2016.10.033. Peng, J., Liu, F., Zhou, F., Song, K., Zhang, C., Ye, L., & He, Y. (2016). Challenging applications for multi-element analysis by laser-induced breakdown spectroscopy in agriculture: A review. TrAC Trends in Analytical Chemistry, 85, 260-272. https://doi.org/10.1016/j.trac.2016.08.015. Pérez-Caballero, G., Andrade, J. M., Olmos, P., Molina, Y., Jiménez, I., Durán, J. J., . . . . Miguel-Cruz, F. (2017). Authentication of tequilas using pattern recognition and supervised classification. TrAC Trends in Analytical Chemistry, 94, 117-129. https://doi.org/10.1016/j.trac.2017.07.008. Petrakis, E. A., & Polissiou, M. G. (2017). Assessing saffron (Crocus sativus L.) adulteration with plant-derived adulterants by diffuse reflectance infrared Fourier transform spectroscopy coupled with chemometrics. Talanta, 162, 558-566. https://doi.org/10.1016/j.talanta.2016.10.072. Pomerantsev, A. L. (2014). Chemometrics in excel. Hoboken NJ: John Wiley & Sons. Pomerantsev, A. L., & Rodionova, O. Y. (2014). Concept and role of extreme objects in PCA/SIMCA. Journal of Chemometrics, 28, 429-438. Poojary, M. M., Putnik, P., Bursać Kovačević, D., Barba, F. J., Lorenzo, J. M., Dias, D. A., & Shpigelman, A. (2017). Stability and extraction of bioactive sulfur compounds from Allium genus processed by traditional and innovative technologies. Journal of Food Composition and Analysis, 61, 28-39. https://doi.org/10.1016/j.jfca.2017.04.007. Pustjens, A. M., Weesepoel, Y., & van Ruth, S. M. (2016). Food fraud and authenticity: Emerging issues and future trends. In C. E. Leadley (Ed.), Innovation and future trends in food manufacturing and supply chain technologies (1st ed, pp. 3-20). Woodhead Publishing. Putnik, P., Bursać Kovačević, D., Herceg, K., Roohinejad, S., Greiner, R., Bekhit, A. E.-D. A., & Levaj, B. (2017). Modelling the shelf-life of minimally-processed fresh-cut apples packaged in a modified atmosphere using food quality parameters. Food Control, 81, 55-64. https://doi.org/10.1016/j.foodcont.2017.05.026. Putnik, P., Bursać Kovačević, D., Režek Jambrak, A., Barba, F., Cravotto, G., Binello, A., & . . . Shpigelman, A. (2017). Innovative “Green” and novel strategies for the extraction of bioactive added value compounds from citrus wastes-A review. Molecules, 22(5), 680. https://doi.org/10.3390/molecules22050680. Putnik, P., Roohinejad, S., Greiner, R., Granato, D., Bekhit, A. E.-D. A., & Bursać Kovačević, D. (2017). Prediction and modeling of microbial growth in minimally processed fresh-cut apples packaged in a modified atmosphere: A review. Food Control, 80, 411-419. https://doi.org/10.1016/j.foodcont.2017.05.018. Rasouli, Z., & Ghavami, R. (2016). Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 165, 191-200. https://doi.org/10.1016/j.saa.2016.04.044. Reinholds, I., Bartkevics, V., Silvis, I. C. J., van Ruth, S. M., & Esslinger, S. (2015). Analytical techniques combined with chemometrics for authentication and determination of contaminants in condiments: A review. Journal of Food Composition and Analysis, 44, 56-72. https://doi.org/10.1016/j.jfca.2015.05.004. Rendall, R., Reis, M. S., Pereira, A. C., Pestana, C., Pereira, V., & Marques, J. C. (2015). Chemometric analysis of the volatile fraction evolution of Portuguese beer under shelf storage conditions. Chemometrics and Intelligent Laboratory Systems, 142, 131-142. https://doi.org/10.1016/j.chemolab.2015.01.015. Rodionova, O. Y., Oliveri, P., & Pomerantsev, A. L. (2016). Rigorous and compliant approaches to one-class classification. Chemometrics and Intelligent Laboratory Systems, 159, 89-96. https://doi.org/10.1016/j.chemolab.2016.10.002. Rodionova, O. Y., Titova, A. V., & Pomerantsev, A. L. (2016). Discriminant analysis is an inappropriate method of authentication. TrAC Trends in Analytical Chemistry, 78, 17-22. https://doi.org/10.1016/j.trac.2016.01.010. Rutherford, A. (2011). 3.6.3. Type 1 error rate control and analysis power. In ANOVA and ANCOVA: A GLM Approach (pp. 65-67). Hoboken, NJ: Wiley. Sabir, A., Rafi, M., & Darusman, L. K. (2017). Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics. Food Chemistry, 221, 1717-1722. https://doi.org/10.1016/j.foodchem.2016.10.114. Shikha Ojha, K., Granato, D., Rajuria, G., Barba, F. J., Kerry, J. P., & Tiwari, B. K. (2018). Application of chemometrics to assess the influence of ultrasound frequency, Lactobacillus sakei culture and drying on beef jerky manufacture: Impact on amino acid profile, organic acids, texture and color. Food Chemistry, 239, 544-550. https://doi.org/10.1016/j.foodchem.2017.06.124. Skov, T., Honoré, A. H., Jensen, H. M., Naes, T., & Engelsen, S. B. (2014). Chemometrics in foodomics: Handling data structures from multiple analytical platforms. TrAC Trends in Analytical Chemistry, 60, 71-79. https://doi.org/10.1016/j.trac.2014.05.004. Soares, S., Amaral, J. S., Oliveira, M. B. P. P., & Mafra, I. (2017). A comprehensive review on the main honey authentication issues: Production and origin. Comprehensive Reviews in Food Science and Food Safety. 16(5), 1072-1100. https://doi.org/10.1111/1541-4337.12278. Sørensen, S., Lund, K. H., Cederberg, T. L., & Ballin, N. Z. (2016). Identification of Baltic Sea salmon based on PCB and dioxin profiles. Food Control, 61, 165-171. https://doi.org/10.1016/j.foodcont.2015.09.044. Souza, Jr., M. B., & Trica, D. (2013). Introdução a Modelagem e Dinâmica para Controle de Processos (1st. ed). Rio de Janeiro: Publit. Spink, J., Ortega, D. L., Chen, C., & Wu, F. (2017). Food fraud prevention shifts the food risk focus to vulnerability. Trends in Food Science & Technology, 62, 215-220. https://doi.org/10.1016/j.tifs.2017.02.012. Spiteri, M., Rogers, K. M., Jamin, E., Thomas, F., Guyader, S., Lees, M., & Rutledge, D. N. (2017). Combination of 1H NMR and chemometrics to discriminate manuka honey from other floral honey types from Oceania. Food Chemistry, 217, 766-772. https://doi.org/10.1016/j.foodchem.2016.09.027. Ståhle, L., & Wold, S. (1987). Partial least squares analysis with cross-validation for the two-class problem: A Monte Carlo study. Journal of Chemometrics, 1(3), 185-196. https://doi.org/10.1002/cem.1180010306. Subramanian, J., & Simon, R. (2013). Overfitting in prediction models - Is it a problem only in high dimensions? Contemporary Clinical Trials, 36(2), 636-641. https://doi.org/10.1016/j.cct.2013.06.011. Suleman, T., van Vuuren, S., Sandasi, M., & Viljoen, A. M. (2015). Antimicrobial activity and chemometric modelling of South African propolis. Journal of Applied Microbiology, 119(4), 981-990. https://doi.org/10.1111/jam.12906. Szymańska, E., Gerretzen, J., Engel, J., Geurts, B., Blanchet, L., & Buydens, L. M. C. (2015). Chemometrics and qualitative analysis have a vibrant relationship. TrAC Trends in Analytical Chemistry, 69, 34-51. https://doi.org/10.1016/j.trac.2015.02.015. Tabachnick, B. G., & Fidell, L. S. (2007). Principal components and factor analysis. In Using multivariate statistics (5th ed, pp. 607-615). Needham Heights, MA: Allyn & Bacon. Tax, D., & Duin, R. (1998). Outlier detection using classifier instability. Lecture notes in Computer Science, 1451, 593-601. Tena, N., Boix, A., & von Holst, C. (2015). Identification of botanical and geographical origin of distillers dried grains with solubles by near infrared microscopy. Food Control, 54, 103-110. https://doi.org/10.1016/j.foodcont.2015.01.033. van Boekel, M., Fogliano, V., Pellegrini, N., Stanton, C., Scholz, G., Lalljie, S., & . . . Eisenbrand, G. (2010). A review on the beneficial aspects of food processing. Molecular Nutrition & Food Research, 54(9), 1215-1247. https://doi.org/10.1002/mnfr.200900608. van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. https://doi.org/10.1016/j.tifs.2017.06.017. Varmuza, K., & Filzmoser, P. (2009). Chemoinformatics-chemometrics-statistics. In Introduction to multivariate statistical analysis in chemometrics (pp. 1-26). Boca Raton, FL: CRC Press Taylor and Francis Group. Vasconcelos, H., Saraiva, C., & de Almeida, J. M. M. M. (2014). Evaluation of the spoilage of raw chicken breast fillets using fourier transform infrared spectroscopy in tandem with chemometrics. Food and Bioprocess Technology, 7(8), 2330-2341. https://doi.org/10.1007/s11947-014-1277-y. Walker, G. S. (2017). Food authentication and traceability: An Asian and Australian perspective. Food Control, 72, 168-172. https://doi.org/10.1016/j.foodcont.2016.01.028. Wold, M. S. (1977). SIMCA: A method for analyzing chemical data in terms of similarity and analogy. In B. R. Kowalski (Ed.), Chemometrics theory and application, American Chemical Society Symposium Series (Vol. 52, pp. 243-282). Wash., D.C.: American Chemical Society. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. https://doi.org/10.1016/0169-7439(87)80084-9. Wu, L., Du, B., Vander Heyden, Y., Chen, L., Zhao, L., Wang, M., & Xue, X. (2017). Recent advancements in detecting sugar-based adulterants in honey - A challenge. TrAC Trends in Analytical Chemistry, 86, 25-38. https://doi.org/10.1016/j.trac.2016.10.013. Xu, L., Cai, C.-B., & Deng, D.-H. (2011). Multivariate quality control solved by one-class partial least squares regression: Identification of adulterated peanut oils by mid-infrared spectroscopy. Journal of Chemometrics, 25(10), 568-574. https://doi.org/10.1002/cem.1402. Yang, X.-T., Qian, J.-P., Li, J., Ji, Z.-T., Fan, B.-l., Xing, B., & Li, W.-Y. (2016). A real-time agro-food authentication and supervision system on a novel code for improving traceability credibility. Food Control, 66, 17-26. https://doi.org/10.1016/j.foodcont.2016.01.032. Zhang, W., & Xue, J. (2016). Economically motivated food fraud and adulteration in China: An analysis based on 1553 media reports. Food Control, 67, 192-198. https://doi.org/10.1016/j.foodcont.2016.03.004. Zielinski, A. A. F., Haminiuk, C. W. I., Nunes, C. A., Schnitzler, E., van Ruth, S. M., & Granato, D. (2014). Chemical composition, sensory properties, provenance, and bioactivity of fruit juices as assessed by chemometrics: A critical review and guideline. Comprehensive Reviews in Food Science and Food Safety, 13(3), 300-316. https://doi.org/10.1111/1541-4337.12060.
معلومات مُعتمدة:	303188/2016-2 CNPq; Croatian Science Foundation
فهرسة مساهمة:	Keywords: classification; food authentication; multivariate statistical techniques; one-class classifiers; pattern recognition
تواريخ الأحداث:	Date Created: 20201222 Latest Revision: 20201222
رمز التحديث:	20221213
DOI:	10.1111/1541-4337.12341
PMID:	33350122
قاعدة البيانات:	MEDLINE

View record at Wiley

الوصف
تدمد:	1541-4337
DOI:	10.1111/1541-4337.12341