Authentication and identification of food adulterants based on fingerprinting techniques and chemometric tools (Review Article)
Subject Areas : Food Science and Technology
1 - 1. Ph.D. Student of Mechanical Engineering of Biosystems, Department of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran
2 - Associate professor, Department of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran
Keywords: Authentication, Adulteration, Chemometrics, Food Fingerprints, Analytical Techniques,
Abstract :
Authentication is an important issue in quality control, hygiene, and safety of food products. Detection and identification of food adulterants require the development of novel and effective analytical methods for verification of composition, quality and authenticity to ensure food safety and consumer satisfaction. Fingerprinting techniques involve chromatographic fingerprinting, electrophoretic fingerprinting, spectroscopic fingerprinting, and electronic sensor fingerprinting. Liquid chromatography (LC), gas chromatography (GC), near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, Raman spectroscopy, hyperspectral imaging (HSI) and nuclear magnetic resonance spectroscopy (NMR) are already common techniques and they will utilize to food fraud prevention. NIR, MIR and Raman spectroscopic techniques, as well as sensor-based fingerprinting (E-Nose, E-Tongue and E-Eye), have the great advantage of providing fast, high throughput, and non-destructive analyses with limited costs. Food fingerprinting combined with chemometric techniques represents a valuable tool for fraud detection and control of food products. This review paper details the fingerprinting techniques applied in the detection and identification of adulteration to obtain food fingerprints, emphasizing the advantages and drawbacks of each technique, as well as review and discuss the reported studies in which these techniques have been applied in the area of food authentication.
References:
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· Aboonajmi, M. and Najafabadi, T.A. (2014). Prediction of poultry egg freshness using Vis-NIR spectroscopy with maximum likelihood method, International journal of food properties, 17(10): 2166-2176.
· Aboonajmi, M., Saberi, A., Najafabadi, T.A. and Kondo, N. (2016). Quality assessment of poultry egg based on visible–near infrared spectroscopy and radial basis function networks, International journal of food properties, 19(5): 1163-1172.
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· Buratti, S., Malegori, C., Benedetti, S., Oliveri, P. and Giovanelli, G. (2018). E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach. Talanta, 182: 131-141.
· Chen, J., Lu, Y.H., Wei, D.Z. and Zhou, X.L. (2009). Establishment of a fingerprint of raspberries by LC. Chromatographia, 70: 981–985.
· Chen, P., Harnly, J.M. and Lester, G.E. (2010). Flow injection mass spectral fingerprints demonstrate chemical differences in Rio red grapefruit with respect to year, harvest time, and conventional versus organic farming. Journal of Agricultural and Food Chemistry, 58(8): 4545–4553.
· Chen, W., Zhang, D. and Zhang, B. (2006). Determination of lecithin in functional food by UV spectrophotometry. Journal of the Chinese Cereals and Oils Association, 21(3): 189–191.
· Consonni, R., Cagliani, L.R., Stocchero, M. and Porretta, S. (2009). Triple concentrated tomato paste: Discrimination between Italian and Chinese products. Journal of Agricultural and Food Chemistry, 57(11): 4506–4513.
· Cubero-Leon, E., Penalver, R. and Maquet, A. (2014). Review on metabolomics for food authentication. Food Research International, 60: 95–107.
· Danezis, G.P., Tsagkaris, A. S., Camin, F., Brusic, V. and Georgiou, C.A. (2016). Food authentication: Techniques, trends & emerging approaches. TrAC Trends in Analytical Chemistry, 85: 123–132.
· Dobson, G., Shepherd, T., Verrall, S.R., Conner, S., McNicol, J.W. and Ramsay, G. (2008). Phytochemical diversity in tubers of potato cultivars and landraces using a GC–MS metabolomics approach. Journal of Agricultural and Food Chemistry, 56(21): 10280–10291.
· Dunn, W.B., Broadhurst, D.I., Atherton, H.J., Goodacre, R. and Griffin, J.L. (2011). Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical Society Reviews, 40(1): 387–426.
· Ellis, D.I., Dunn, W.B., Griffin, J.L., Allwood, J.W. and Goodacre, R. (2007). Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics, 8: 1243–1266.
· Ellis, D.I., Victoria, L.B., Warwick, B.D., Allwood, J.D., Alexander, P.G. and Goodacre, R. (2012). Fingerprinting food: Current technologies for the detection of food adulteration and contamination. Chemical Society Reviews, 41: 5706–5727.
· Esslinger, S., Riedl, J. and Fauhl-Hassek, C. (2014). Potential and limitations of nontargeted fingerprinting for authentication of food in official control. Food Research International, 60: 189–204.
· Esteki, M., Shahsavari, Z. and Simal-Gandara, J. (2018). Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products. Food Control, 91: 100-112.
· Fei, D. and Mei, L. (2008). Determination of paraquat in vegetable by ion chromatography with UV detection. Environmental Pollution and Control, 30(6): 31–33.
· Fu, X., Ying, Y. and Liu, Y. (2006). Detection of pear firmness using near infrared diffuse reflectance spectroscopy. Spectroscopy and Spectral Analysis, 26(6): 1038–1041.
· Gallo, M. and Ferranti, P. (2016). The evolution of analytical chemistry methods in food omics. Journal of Chromatography A, 14(28): 3–15.
· Gan, Z., Yang, Y., Li, J., Wen, X., Zhu, M., Jiang, Y. and Ni, Y. (2016). Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey. Journal of Food Engineering, 178: 151-158.
· Gil, M., Le Duarte, I.F., Godejohann, M., Braumann, U., Maraschin, M. and Spraul, M. (2003). Characterization of the aromatic composition of some liquid foods by nuclear magnetic resonance spectrometry and liquid chromatography with nuclear magnetic resonance and mass spectrometric detection. Analytica Chimica Acta, 488: 35–51.
· Gomez-Ariza, J.L., Arias-Borrego, A. and Garcıa-Barrera, T. (2006). Multi-elemental fractionation in pine nuts (pinus pinea) from different geographic origins by size-exclusion chromatography with UV and inductively coupled mass spectrometry detection. Journal of Chromatography A, 1121: 191–199.
· Guo, J., Yue, T. and Yuan, Y. (2012). Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin. Journal of Food Science, 77: 1090–1096.
· Han, B., Jin, J. and Wu, Q.Y. (2009). High order derivative spectrophotograph determination of Sudan red in the samples containing lycopene. Chinese Journal of Pharmaceutical Analysis, 29(5): 710–713.
· Hao, X., Hu, J.Z. and Zhong, H. (2007). Detection of Benzo Pyrene in meat food by high pressure liquid chromatography (HPLC). Food Science and Technology, 7: 219–221.
· He, X.Q., Xu, D. and Luo, M. (2005). A microwave-assisted derivatization and GC-MS method coupled with calibration transformation matrix for determination of fatty acid in edible oils. Journal of Instrumental Analysis, 24(1): 25–28.
· Herrero, A.M. (2008). Raman spectroscopy a promising technique for quality assessment of meat and fish: A review. Food Chemistry, 107(4): 1642–51.
· Hu, Y.F. (2005). Study on the photometry of nitrites in meat products. Physical Testing and Chemical Analysis Part B: Chemical Analysis, 41(3): 188–189.
· Huang, H., Yu, H., Xu, H. and Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. Journal of Food Engineering, 87(3): 303–13.
· Huanga, L.F., Wu, M.J., Zhongb, K.J., Sunb, X.J., Liang, Y.Z., Dai, Y.H., et al. (2007). Fingerprint developing of coffee flavor by gas chromatography mass spectrometry and combined chemometrics methods. Analytica Chimica Acta, 588: 216–223.
· Jiang, J.S., Zhao, B. and Zhou, L. (2009). Simultaneous determination of 36 pesticide residues in tea by GC-MS. Food Science, 30(14): 276-330.
· Jin, Z.Q., Zhang, J.S. and Lin, X.Y. (2007). Study on water loss rate and decay of strawberry by NMR and MRI. Food Science, 28(8): 108-111.
· Kim, H.J. and Jang, Y.P. (2009). Direct analysis of curcumin in turmeric by DART-MS. Phytochemical Analysis, 20: 372-377.
· Kim, J., Jung, Y., Song, B., Bong, Y.S., Ryu, D.H. and Lee, K.S. (2013). Discrimination of cabbage (Brassica rapa ssp. pekinensis) cultivars grown in different geographical areas using 1H NMR-based metabolomics. Food Chemistry, 137(137): 68–75.
· Kvasnicka, F. (2005). Capillary electrophoresis in food authenticity. Journal of Separation Science, 28: 813–825.
· Leme, G.M., Coutinho, I.D., Creste, S., Hojo, O., Carneiro, R.L. and Bolzani, V. (2014). HPLC-DAD method to metabolic fingerprinting to the phenotyping of sugarcane genotypes. Analytical Methods, 6: 7781–7788.
· Li, H.B., Wang, S. and Wang, H. (2008). Application of AFLP marker technology in the identification of greengrocery variety “Youdonger”. Journal of Anhui Agricultural Sciences, 36(1): 72–73.
· Li, J., He, X., Li, M., Zhao, W., Liu, L. and Kong, X. (2015). Chemical fingerprint and quantitative analysis for quality control of polyphenols extracted from pomegranate peel by HPLC. Food Chemistry, 176: 7–11.
· Lohumi, S., Lee, S., Lee, H. et al. (2015). A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science and Technology, 46: 85–98.
· Malheiro, R., Guedes de Pinho, P., Soares, S., Cesar da Silva Ferreira, A. and Baptista, P. (2013). Volatile biomarkers for wild mushrooms species discrimination. Food Research International, 54: 186–194.
· Medina, S., Perestrelo, R., Silva, P., Pereira, J.A.M. and Camara, J.S. (2019). Current trends and recent advances on food authenticity technologies and chemometric approaches, Trends in Food Science & Technology, 85: 163-176.
· Moore, J.C., Spink, J. and Lipp, M. (2012). Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. Journal of Food Science, 77: 118–126.
· Nicolaou, N., Xu, Y. and Goodacre, R. (2011). MALDI-MS and multivariate analysis for the detection and quantification of different milk species. Analytical and Bioanalytical Chemistry, 399(10): 3491–502.
· Parastar, H., Jalali, H.M., Sereshti, H. and Varnosfaderani, A.M. (2012). Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods. Journal of Chromatography A, 1251: 176–187.
· Peng, Y., Liu, F. and Ye, J. (2006). Quantitative and qualitative analysis of flavonoid markers in Frucus aurantii of different geographical origin by capillary electrophoresis with electrochemical detection. Journal of Chromatography B, 830: 224–230.
· Qin, J., Chao, K. and Kim, M.S. (2010). Raman chemical imaging system for food safety and quality inspection. Trans ASABE, 53(6): 1873–1882.
· Reid, L.M., O’Donnell, C.P. and Downey, G. (2006). Recent technological advances for the determination of food authenticity. Trends Food Science Technology, 17: 344–53.
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