اصالت‌سنجی و تشخیص تقلب مواد غذایی بر اساس تکنیک‌های انگشت‌نگاری و ابزارهای شیمی‌سنجی (مقاله مروری)
محورهای موضوعی : علوم و صنایع غذایی
احسان سرلکی
1
(دانشجوی دکتری مهندسی مکانیک بیوسیستم، گروه مهندسی فنی کشاورزی، پردیس ابوریحان، دانشگاه تهران)
محمد ابونجمی
2
(دانشیار، گروه مهندسی فنی کشاورزی، پردیس ابوریحان، دانشگاه تهران)
کلید واژه: تقلب, روش‌های تحلیلی, اصالت‌سنجی, شیمی‌سنجی, اثرانگشت مواد غذایی,
چکیده مقاله :
اصالت‌سنجی یک مسئله مهم در کنترل کیفیت، بهداشت و ایمنی مواد غذایی است. شناسایی و تعیین تقلب در مواد غذایی به‌منظور بررسی اجزای آن‌ها، کیفیت و صحت و اطمینان از ایمنی ماده غذایی و رضایت مصرف‌کنندگان نیاز به توسعه روش‌های تحلیلی نوین و مؤثر دارد. فنون انگشت‌نگاری شامل انگشت‌نگاری کروماتوگرافی، انگشت‌نگاری الکتروفورز، انگشت‌نگاری طیف‌سنجی و انگشت‌نگاری حسگرهای الکترونیکی هستند. در حال حاضر از میان این فنون، روش‌های کروماتوگرافی مایع (LC)، کروماتوگرافی گازی (GC)، طیف‌سنجی‌های مادون قرمز نزدیک (NIR) و مادون قرمز متوسط (MIR)، رامان (Raman)، تصویربرداری ابر طیفی (HSI) و رزونانس مغناطیسی هسته (NMR) به‌عنوان ابزارهای تحلیلی مرسوم موجود هستند و برای جلوگیری از تقلب مواد غذایی به‌کار گرفته می‌شوند. فنون NIR، MIR و Raman و هم‌چنین فنون انگشت‌نگاری حسگر-مبنا (بینی الکترونیکی (E-Nose)، زبان الکترونیکی (E-Tongue) و چشم الکترونیکی (E-Eye))، دارای مزایای بسیار مهمی از قبیل آنالیز سریع، پیشرفته و غیر-مخرب با هزینه‌های پایین هستند. انگشت‌نگاری مواد غذایی در ترکیب با ابزارهای شیمی‌سنجی یک تکنیک ارزشمند برای تشخیص تقلب و کنترل مواد غذایی به‌شمار می‌آید. در این مقاله مروری، انواع فنون انگشت‌نگاری مورداستفاده در شناسایی و تشخیص تقلب برای آنالیز اثرانگشت مواد غذایی موردبررسی قرار گرفته است و بر مزایا و معایب هر یک از فنون پرداخته شده و یافته‌های مقالات اخیر برای این فنون در حوزه اصالت‌سنجی مواد غذایی مورد بحث قرار گرفته است.
چکیده انگلیسی:
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.
منابع و مأخذ:
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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.
· Aprea, E., Gika, H., Carlin, S., Theodoridis, G., Vrhovsek, U. and Mattivi, F. (2011). Metabolite profiling on apple volatile content based on solid phase microextraction and gas chromatography time of flight mass spectrometry. Journal of Chromatography A, 1218: 4517-4524.
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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.
· Aprea, E., Gika, H., Carlin, S., Theodoridis, G., Vrhovsek, U. and Mattivi, F. (2011). Metabolite profiling on apple volatile content based on solid phase microextraction and gas chromatography time of flight mass spectrometry. Journal of Chromatography A, 1218: 4517-4524.
· Bahmani, L., Aboonajmi, M. and Arabhosseini, A. (2018). ANN modeling of extraction kinetics of essential oil from tarragon using ultrasound pre-treatment. Engineering in Agriculture, Environment and Food, 11(1): 25-29.
· Bansal, S., Singh A., Mangal, M., Mangal, AK. and Kumar, S. (2015). Food adulteration: Sources, health risks, and detection methods. Critical Reviews in Food Science and Nutrition, 57(6):1174-1189.
· Bing, Y., Luo, Y.B. and Huang, K.L. (2005). Application of near-infrared diffuse reflectance spectroscopy to the detection and identification of transgenic corn. Spectroscopy and Spectral Analysis, 25(10): 1580–1583.
· Bosque-Sendra, J.M., Cuadros-Rodriguez, L., Ruiz-Samblas, C. and Mata, A.P. (2012). Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data-a review. Analytica Chimica Acta, 724:1-11.
· Bothwell, J.H. and Grifin, J.L. (2011). An introduction to biological nuclear magnetic resonance spectroscopy. Biological reviews of the Cambridge Philosophical Society, 86(2): 493–510.
· 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.
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