Trends in application of NIR and hyperspectral imaging for food authentication



Palabras clave:

food fraud, spectroscopy, discrimination, regression.


Food fraud can cause damage to consumer health and affect their confidence, destroy brands and generate large economic losses in the industry. Food authenticity allows to identify if food composition, geographical origin, genetic variety and farming system corresponds to what has been declared on the label. Although there are currently standardized methods to identify certain adulterants, the complexity of the food, the complexity of the supply chain and the appearance of new adulterants require the continuous development of analytical techniques to detect food fraud. NIR and Hyperspectral imaging (HSI) in tandem with chemometrics are non-destructive, non-invasive and accurate techniques for food authentication. This review focuses on NIR and HIS approaches to food authentication, including adulteration by substitution, geographical origin and farming system. In this context, the advances in NIR and HSI approaches reported since 2014 are discussed regarding their potential use in food authentication. Both techniques have shown to have efficiency, precision and selectivity to detect adulterants and identify geographic origin, genetic variety and farming system. Portability and remote access are shown as the next step for the industrialization of NIR and HSI devices.


Abbas, O.; Zadravec, M.; Baeten, V.; Mikuš, T.; Lešić, T.; Vulić, A.; Prpić, J.; Jemeršić, L.; Pleadin, J. 2018. Analytical methods used for the authentication of food of animal origin. Food Chemistry 246: 6-17.

Alamprese, C.; Amigo, J. M.; Casiraghi, E.; Engelsen, S. B. 2016. Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics. Meat Science 121: 175-181.

Alamprese, C.; Casale, M.; Sinelli, N.; Lanteri, S.; Casiraghi, E. 2013. Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy. LWT - Food Science and Technology 53: 225-232.

Amaral, J.; Meira, L.; Oliveira, M. B. P. P.; Mafra, I. 2016. 14 - Advances in Authenticity Testing for Meat Speciation. In G. Downey (Ed.), Advances in Food Authenticity Testing. Woodhead Publishing. Pp. 369 - 414.

Amigo, J.M.; Babamoradi, H.; Elcoroaristizabal, S. 2015. Hyperspectral image analysis. A tutorial. Analytica Chimica Acta 896: 34-51.

Amigo, J. M.; Martí, I.; Gowen, A. 2013. Hyperspectral imaging and chemometrics: a perfect combination for the analysis of food structure, composition and quality. In data handling in science and technology. Elsevier. Pp. 343 - 370.

Aredo, V.; Velásquez, L.; Siche, R. 2017. Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR). Scientia Agropecuaria 8: 169-174.

Azizian, H.; Mossoba, M.M.; Fardin-Kia, A.R.; Karunathilaka, S.R.; Kramer, J.K.G. 2016. Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils. Lipids 51: 1309-1321.

Ballabio, D.; Robotti, E.; Grisoni, F.; Quasso, F.; Bobba, M.; Vercelli, S.; Gosetti, F.; Calabrese, G.; Sangiorgi, E.; Orlandi, M.; Marengo, E. 2018. Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey. Food Chemistry 266: 79-89.

Banerjee, R.; Tudu, B.; Bandyopadhyay, R.; Bhattacharyya, N. 2016. A review on combined odor and taste sensor systems. Journal of Food Engineering 190: 10-21.

Bao, Y.-d.; Chen, N.; He, Y.; Liu, F.; Zhang, C.; Kong, W.-w. 2015. Rapid identification of coffee bean variety by near infrared hyperspectral imaging technology. Optics and Precision Engineering 23: 349-355.

Barbin, D.F.; Felicio, A.L.d.S.M.; Sun, D.-W.; Nixdorf, S.L.; Hirooka, E.Y. 2014. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Research International 61: 23-32.

Barbin, D.F.; Kaminishikawahara, C.M.; Soares, A.L.; Mizubuti, I.Y.; Grespan, M.; Shimokomaki, M.; Hirooka, E.Y. 2015. Prediction of chicken quality attributes by near infrared spectroscopy. Food Chemistry 168: 554-560.

Barreto, A.; Cruz-Tirado, J. P.; Siche, R.; Quevedo, R. 2018. Determination of starch content in adulterated fresh cheese using hyperspectral imaging. Food Bioscience 21: 14-19.

Basri, K.N.; Hussain, M.N.; Bakar, J.; Sharif, Z.; Khir, M.F.A.; Zoolfakar, A.S. 2017. Classification and quantification of palm oil adulteration via portable NIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 173: 335-342.

Başar, B.; Özdemir, D. 2018. Determination of honey adulteration with beet sugar and corn syrup using infrared spectroscopy and genetic-algorithm-based multivariate calibration. Journal of the Science of Food and Agriculture 98: 5616-5624.

Bertone, E.; Venturello, A.; Giraudo, A.; Pellegrino, G.; Geobaldo, F. 2016. Simultaneous determination by NIR spectroscopy of the roasting degree and Arabica/Robusta ratio in roasted and ground coffee. Food Control 59: 683-689.

Biancolillo, A.; Bucci, R.; Magrì, A. L.; Magrì, A. D.; Marini, F. 2014. Data-fusion for multiplatform characterization of an italian craft beer aimed at its authentication. Analytica Chimica Acta 820: 23-31.

Binetti, G.; Del Coco, L.; Ragone, R.; Zelasco, S.; Perri, E.; Montemurro, C.; Valentini, R.; Naso, D.; Fanizzi, F.P.; Schena, F.P. 2017. Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data. Food Chemistry 219: 131-138.

Bona, E.; Marquetti, I.; Link, J.V.; Makimori, G.Y.F.; da Costa Arca, V.; Guimarães Lemes, A.L.; Ferreira, J. M.G.; dos Santos Scholz, M. B.; Valderrama, P.; Poppi, R.J. 2017. Support vector machines in tandem with infrared spectroscopy for geographical classification of green arabica coffee. LWT - Food Science and Technology 76: 330-336.

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.

Borràs, E.; Ferré, J.; Boqué, R.; Mestres, M.; Aceña, L.; Calvo, A.; Busto, O. 2016. Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA). Food Chemistry 203: 314-322.

Boyacı, I.H.; Temiz, H.T.; Uysal, R.S.; Velioğlu, H.M.; Yadegari, R.J.; Rishkan, M.M. 2014. A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food chemistry 148: 37-41.

Branigan, T. 2008. In the Guardian, Guardian News and Media, London.

Brown, C.D.; Vega-Montoto, L.; Wentzell, P.D. 2000. Derivative Preprocessing and Optimal Corrections for Baseline Drift in Multivariate Calibration. Applied Spectroscopy 54: 1055-1068.

Bázár, G.; Romvári, R.; Szabó, A.; Somogyi, T.; Éles, V.; Tsenkova, R. 2016. NIR detection of honey adulteration reveals differences in water spectral pattern. Food chemistry 194: 873-880.

Böhme, K.; Calo-Mata, P.; Barros-Velázquez, J.; Ortea, I. 2019. Recent applications of omics-based technologies to main topics in food authentication. TrAC Trends in Analytical Chemistry 110: 221-232.

Cajka, T.; Hajslova, J.; Pudil, F.; Riddellova, K. 2009. Traceability of honey origin based on volatiles pattern processing by artificial neural networks. Journal of Chromatography A 1216: 1458-1462.

Callao, M.P.; Ruisánchez, I. 2018. An overview of multivariate qualitative methods for food fraud detection. Food Control 86: 283-293.

Calvini, R.; Amigo, J.M.; Ulrici, A. 2017. Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee. Analytica Chimica Acta 967: 33-41.

Calvini, R.; Ulrici, A.; Amigo, J.M. 2015. 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.

Caporaso, N.; Whitworth, M. B.; Grebby, S.; Fisk, I. D. 2018. Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging. Journal of Food Engineering 227: 18-29.

Chen, H.; Lin, Z.; Tan, C. 2018. Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models. Vibrational Spectroscopy 99: 178-183.

Chen, H.; Tan, C.; Lin, Z. 2018. Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description. International journal of analytical chemistry.

Cheng, J.-H.; Nicolai, B.; Sun, D.-W. 2017. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Science 123: 182-191.

Cheng, J.-H.; Sun, D.-W.; Pu, H.-B.; Chen, X.; Liu, Y.; Zhang, H.; Li, J.-L. 2015. Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets. Journal of Food Engineering 161: 33-39.

Chiesa, L.; Panseri, S.; Bonacci, S.; Procopio, A.; Zecconi, A.; Arioli, F.; Cuevas, F. J.; 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.

Correia, R.M.; Tosato, F.; Domingos, E.; Rodrigues, R.R.T.; Aquino, L.F.M.; Filgueiras, P.R.; Lacerda, V.; Romão, W. 2018. Portable near infrared spectroscopy applied to quality control of Brazilian coffee. Talanta 176: 59-68.

Cozzolino, D. 2016. 16 - Authentication of Cereals and Cereal Products. In G. Downey (Ed.), Advances in Food Authenticity Testing. Woodhead Publishing. Pp. 441 - 457.

Crocombe, R.A. 2018. Portable Spectroscopy. Applied Spectroscopy 72: 1701-1751.

Dai, S.; Lin, Z.; Xu, B.; Wang, Y.; Shi, X.; Qiao, Y.; Zhang, J. 2018. Metabolomics data fusion between near infrared spectroscopy and high-resolution mass spectrometry: A synergetic approach to boost performance or induce confusion. Talanta 189: 641-648.

Danezis, G. P.; Tsagkaris, A. S.; Brusic, V.; Georgiou, C. A. 2016. Food authentication: state of the art and prospects. Current Opinion in Food Science 10: 22-31.

Dang, T.T.; Vasanthan, T. 2019. Modification of rice bran dietary fiber concentrates using enzyme and extrusion cooking. Food Hydrocolloids 89: 773-782.

Dankowska, A.; Kowalewski, W. 2019. Tea types classification with data fusion of UV–Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 211: 195-202.

da Silva, I.J.S.; Paim, A.P.S.; da Silva, M.J. 2018. Composition and estimate of daily mineral intake from samples of Brazilian rice. Microchemical Journal 137: 131-138.

de la Fuente, E.; Sanz, M.L.; Martínez-Castro, I.; Sanz, J. 2006. Development of a robust method for the quantitative determination of disaccharides in honey by gas chromatography. Journal of Chromatography A 1135: 212-218.

De Luca, S.; De Filippis, M.; Bucci, R.; Magrì, A.D.; Magrì, A.L.; Marini, F. 2016. Characterization of the effects of different roasting conditions on coffee samples of different geographical origins by HPLC-DAD, NIR and chemometrics. Microchemical Journal 129: 348-361.

Di Rosa, A.R., Leone, F.; Cheli, F.; Chiofalo, V. 2017. Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review. Journal of Food Engineering 210: 62-75.

Doeswijk, T. G.; Smilde, A. K.; Hageman, J. A.; Westerhuis, J. A.; van Eeuwijk, F. A. 2011. On the increase of predictive performance with high-level data fusion. Analytica Chimica Acta 705: 41-47.

Erich, S.; Schill, S.; Annweiler, E.; Waiblinger, H.-U.; Kuballa, T.; Lachenmeier, D.W.; Monakhova, Y.B. 2015. Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk. Food Chemistry 188: 1-7.

Erkinbaev, C.; Henderson, K.; Paliwal, J. 2017. Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique. Food Control 80: 197-203.

Escuredo, O.; González-Martín, M. I.; Rodríguez-Flores, M. S.; Seijo, M. C. 2015. Near infrared spectroscopy applied to the rapid prediction of the floral origin and mineral content of honeys. Food Chemistry 170: 47-54.

Esteki, M.; Shahsavari, Z.; Simal-Gandara, J. 2018. Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products. Food Control 91: 100-112.

Esteki, M.; Simal-Gandara, J.; Shahsavari, Z.; Zandbaaf, S.; Dashtaki, E.; Vander Heyden, Y. 2018. A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 93: 165-182.

Ferreiro-González, M.; Espada-Bellido, E.; Guillén-Cueto, L.; Palma, M.; Barroso, C. G.; Barbero, G. F. 2018. Rapid quantification of honey adulteration by visible-near infrared spectroscopy combined with chemometrics. Talanta 188: 288-292.

Forina, M.; Oliveri, P.; Bagnasco, L.; Simonetti, R.; Casolino, M.C.; Nizzi Grifi, F.; Casale, M. 2015. Artificial nose, NIR and UV–visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil. Talanta 144: 1070-1078.

Gan, Z.; Yang, Y.; Li, J.; Wen, X.; Zhu, M.; Jiang, Y.; 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.

Giraudo, A.; Grassi, S.; Savorani, F.; Gavoci, G.; Casiraghi, E.; Geobaldo, F. 2019. Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis. Food Control 99: 137-145.

Gossner, C.M.-E.; Schlundt, J.; Embarek, P.B.; Hird, S.; Lo-Fo-Wong, D.; Beltran, J.J.O.; Teoh, K.N.; Tritscher, A. 2009. The melamine incident: implications for international food and feed safety. Environmental health perspectives 117: 1803.

Guelpa, A.; Marini, F.; du Plessis, A.; Slabbert, R.; Manley, M. 2017. Verification of authenticity and fraud detection in South African honey using NIR spectroscopy. Food Control 73: 1388-1396.

Hong, B.; Jun, L.; Xian-Yun, Z.; Jing, Z. 2003. A fire detection system based on intelligent data fusion technology. In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693) 2: 1096-1101.

Jiménez-Carvelo, A.M.; Lozano, V.A.; Olivieri, A.C. 2019. Comparative chemometric analysis of fluorescence and near infrared spectroscopies for authenticity confirmation and geographical origin of Argentinean extra virgin olive oils. Food Control 96: 22-28.

Kamruzzaman, M.; Makino, Y.; Oshita, S. 2016. Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chemistry 196: 1084-1091.

Karaman, I.; Tanriverdi, H.; Özdemir, D. 2009. Prediction of Lignin and Extractive Content of Pinus nigra Arnold. var. Pallasiana Tree Using Near Infrared Spectroscopy and Multivariate Calibration AU - Uner, B. Journal of Wood Chemistry and Technology 29: 24-42.

Kartakoullis, A.; Comaposada, J.; Cruz-Carrión, A.; Serra, X.; Gou, P. 2019. Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures. Food Chemistry 278: 314-321.

Karunathilaka, S. R.; Kia, A.-R. F.; Srigley, C.; Chung, J. K.; Mossoba, M. M. 2016. Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses. Journal of Food Science 81: C2390-C2397.

Kennedy, J.; Delaney, L.; McGloin, A.; Wall, P.G. 2009. Public perceptions of the dioxin crisis in Irish pork.

Kumar, Y.; Chandrakant-Karne, S. 2017. Spectral analysis: A rapid tool for species detection in meat products. Trends in Food Science & Technology 62: 59-67.

Kumaravelu, C.; Gopal, A. 2015. Detection and quantification of adulteration in honey through near infrared spectroscopy. International Journal of Food Properties 18: 1930-1935.

Laroussi-Mezghani, S.; Vanloot, P.; Molinet, J.; Dupuy, N.; Hammami, M.; Grati-Kamoun, N.; Artaud, J. 2015. Authentication of Tunisian virgin olive oils by chemometric analysis of fatty acid compositions and NIR spectra. Comparison with Maghrebian and French virgin olive oils. Food Chemistry 173: 122-132.

Li, H.; Liang, Y.; Xu, Q.; Cao, D. 2009. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 648: 77-84.

Li, S.; Zhang, X.; Shan, Y.; Su, D.; Ma, Q.; Wen, R.; Li, J. 2017. Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy. Food Chemistry 218: 231-236.

Li, Y.; Xiong, Y.; Min, S. 2019. Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration. Vibrational Spectroscopy 101: 20-27.

Liu, Y.; Ma, D.-h.; Wang, X.-c.; Liu, L.-p.; Fan, Y.-x.; Cao, J.-x. 2015. Prediction of chemical composition and geographical origin traceability of Chinese export tilapia fillets products by near infrared reflectance spectroscopy. LWT - Food Science and Technology 60: 1214-1218.

Luna, A.S.; da Silva, A.P.; Alves, E.A.; Rocha, R.B.; Lima, I.C.A.; de Gois, J.S. 2017. Evaluation of chemometric methodologies for the classification of Coffea canephora cultivars via FT-NIR spectroscopy and direct sample analysis. Analytical Methods 9: 4255-4260.

López-Maestresalas, A.; Insausti, K.; Jarén, C.; Pérez-Roncal, C.; Urrutia, O.; Beriain, M.J.; Arazuri, S. 2019. Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control 98: 465-473.

Ma, J.; Pu, H.; Sun, D.-W.; Gao, W.; Qu, J.-H.; Ma, K.-Y. 2015. Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles. International Journal of Refrigeration 50: 10-18.

Maione, C.; Barbosa, R.M. 2018. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Critical reviews in food science and nutrition 1-12.

Marquetti, I.; Link, J.V.; Lemes, A.L.G.; Scholz, M.B.d.S.; Valderrama, P.; Bona, E. 2016. Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee. Computers and Electronics in Agriculture 121: 313-319.

Mendes, T.O.; da Rocha, R.A.; Porto, B.L.S.; de Oliveira, M.A.L.; dos Anjos, V.d.C.; Bell, M.J.V. 2015. Quantification of Extra-virgin Olive Oil Adulteration with Soybean Oil: a Comparative Study of NIR, MIR, and Raman Spectroscopy Associated with Chemometric Approaches. Food Analytical Methods 8: 2339-2346.

Minaei, S.; Shafiee, S.; Polder, G.; Moghadam-Charkari, N.; van Ruth, S.; Barzegar, M.; Zahiri, J.; Alewijn, M.; Kuś, P. M. 2017. VIS/NIR imaging application for honey floral origin determination. Infrared Physics & Technology 86: 218-225.

Mo, C.; Lim, J.; Kwon, S.W.; Lim, D.K.; Kim, M. S.; Kim, G.; Kang, J.; Kwon, K.-D.; Cho, B.-K. 2017. Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. Journal of Biosystems Engineering 42: 293-300.

Monahan, F.J.; Schmidt, O.; Moloney, A.P. 2018. Meat provenance: Authentication of geographical origin and dietary background of meat. Meat Science 144: 2-14.

Monteiro, P.I.; Santos, J.S.; Alvarenga Brizola, V.R.; Pasini Deolindo, C.T.; Koot, A.; Boerrigter-Eenling, R.; van Ruth, S.; Georgouli, K.; Koidis, A.; Granato, D. 2018. Comparison between proton transfer reaction mass spectrometry and near infrared spectroscopy for the authentication of Brazilian coffee: A preliminary chemometric study. Food Control 91: 276-283.

Moran, L.; Andres, S.; Allen, P.; Moloney, A. P. 2018. Visible and near infrared spectroscopy as an authentication tool: Preliminary investigation of the prediction of the ageing time of beef steaks. Meat Science 142: 52-58.

Moseholm, L. 1988. Analysis of air pollution plant exposure data: the soft independent modelling of class analogy (SIMCA) and partial least squares modelling with latent variable (PLS) approaches. Environmental Pollution 53: 313-331.

Mossoba, M.M.; Azizian, H.; Fardin-Kia, A.R.; Karunathilaka, S.R.; Kramer, J.K.G. 2017. First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA. Lipids 52: 443-455.

Mouazen, A. M.; Al-Walaan, N. 2014. Glucose adulteration in Saudi honey with visible and near infrared spectroscopy. International journal of food properties 17: 2263-2274.

Mu, T.; Chen, S.; Zhang, Y.; Chen, H.; Guo, P.; Meng, F. 2016. Portable Detection and Quantification of Olive Oil Adulteration by 473-nm Laser-Induced Fluorescence. Food Analytical Methods 9: 275-279.

Murniece, I.; Straumite, E. 2014. The information presented on labels for bread produced in Latvia. Food Chemistry 162: 117-121.

Márquez, C.; López, M.I.; Ruisánchez, I.; Callao, M.P. 2016. FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud. Talanta 161: 80-86.

Naila, A.; Flint, S.H.; Sulaiman, A.Z.; Ajit, A.; Weeds, Z. 2018. Classical and novel approaches to the analysis of honey and detection of adulterants. Food Control 90: 152-165.

Nakyinsige, K.; Man, Y.B.C.; Sazili, A.Q. 2012. Halal authenticity issues in meat and meat products. Meat science 91: 207-214.

Nenadis, N.; Tsimidou, M.Z. 2017. Perspective of vibrational spectroscopy analytical methods in on‐field/official control of olives and virgin olive oil. European Journal of Lipid Science and Technology 119: 1600148.

Nolasco, I.M.; Badaró, A.T.; Barbon, S.; Barbon, A.P.A.C.; Pollonio, M.A.R.; Barbin, D.F. 2018. Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning. Applied Spectroscopy 72: 1774-1780.

Ortea, I.; O'Connor, G.; Maquet, A. 2016. Review on proteomics for food authentication. Journal of Proteomics 147: 212-225.

Ouchemoukh, S.; Louaileche, H.; Schweitzer, P. 2007. Physicochemical characteristics and pollen spectrum of some Algerian honeys. Food Control 18: 52-58.

Ozen, B. F.; Mauer, L. J. 2002. Detection of hazelnut oil adulteration using FT-IR spectroscopy. Journal of agricultural and food chemistry 50: 3898-3901.

Paradkar, M.M.; Irudayaraj, J. 2002. Discrimination and classification of beet and cane inverts in honey by FT-Raman spectroscopy. Food Chemistry 76: 231-239.

Pasquini, C. 2018. Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. Analytica Chimica Acta 1026: 8-36.

Peršurić, Ž.; Saftić, L.; Mašek, T.; Kraljević Pavelić, S. 2018. Comparison of triacylglycerol analysis by MALDI-TOF/MS, fatty acid analysis by GC-MS and non-selective analysis by NIRS in combination with chemometrics for determination of extra virgin olive oil geographical origin. A case study. LWT - Food Science and Technology 95: 326-332.

Picouet, P.A.; Gou, P.; Hyypiö, R.; Castellari, M. 2018. Implementation of NIR technology for at-line rapid detection of sunflower oil adulterated with mineral oil. Journal of Food Engineering 230: 18-27.

Pieszczek, L.; Czarnik-Matusewicz, H.; Daszykowski, M. 2018. Identification of ground meat species using near-infrared spectroscopy and class modeling techniques – Aspects of optimization and validation using a one-class classification model. Meat Science 139: 15-24.

Pita-Calvo, C.; Guerra-Rodríguez, M.E.; Vázquez, M. 2017. Analytical Methods Used in the Quality Control of Honey. Journal of Agricultural and Food Chemistry 65: 690-703.

Pu, H.; Sun, D.-W.; Ma, J.; Cheng, J.-H. 2015. Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Science 99: 81-88.

Rady, A.; Adedeji, A. 2018. Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Science 136: 59-67.

Ravikanth, L.; Singh, C.B.; Jayas, D.S.; White, N.D.G. 2015. Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering 135: 73-86.

Revilla, I.; Lastras, C.; González-Martín, M.I.; Vivar-Quintana, A.M.; Morales-Corts, R.; Gómez-Sánchez, M.A.; Pérez-Sánchez, R. 2019. Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy. Journal of Food Composition and Analysis 77: 84-90.

Rinnan, Å.; Berg, F.v.d.; Engelsen, S.B. 2009. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry 28: 1201-1222.

Sanaeifar, A.; Jafari, A.; Golmakani, M.-T. 2018. Fusion of dielectric spectroscopy and computer vision for quality characterization of olive oil during storage. Computers and Electronics in Agriculture 145: 142-152.

Sanz, J.A.; Fernandes, A.M.; Barrenechea, E.; Silva, S.; Santos, V.; Gonçalves, N.; Paternain, D.; Jurio, A.; Melo-Pinto, P. 2016. Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms. Journal of Food Engineering 174: 92-100.

Schmutzler, M.; Beganovic, A.; Böhler, G.; Huck, C. W. 2015. Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis. Food Control 57: 258-267.

Sentandreu, M.Á.; Sentandreu, E. 2014. Authenticity of meat products: Tools against fraud. Food Research International 60: 19-29.

Shafiee, S.; Polder, G.; Minaei, S.; Moghadam-Charkari, N.; van Ruth, S.; Kuś, P. M. 2016. Detection of Honey Adulteration using Hyperspectral Imaging. IFAC-PapersOnLine 49: 311-314.

Shao, Y.; Xuan, G.; Hu, Z.; Gao, X. 2018. Identification of adulterated cooked millet flour with Hyperspectral Imaging Analysis. IFAC-PapersOnLine 51: 96-101.

Shi, J.; Tang, Y.; Wei, H.; Zhang, L.; Zhang, D.; Shi, J.; Gong, W.; He, X.; Yang, K.; Liu, D. 2012. Temperature dependence of threshold and gain coefficient of stimulated Brillouin scattering in water. Applied Physics B 108: 717-720.

Shi, J.; Yuan, D.; Hao, S.; Wang, H.; Luo, N.; Liu, J.; Zhang, Y.; Zhang, W.; He, X.; Chen, Z. 2019. Stimulated Brillouin scattering in combination with visible absorption spectroscopy for authentication of vegetable oils and detection of olive oil adulteration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 206: 320-327.

Sivakesava, S.; Irudayaraj, J. 2001. Detection of inverted beet sugar adulteration of honey by FTIR spectroscopy. Journal of the Science of Food and Agriculture 81: 683-690.

Sliwinska, M.; Wisniewska, P.; Dymerski, T.; Namiesnik, J.; Wardencki, W. 2014. Food analysis using artificial senses. Journal of agricultural and food chemistry 62: 1423-1448.

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: 1072-1100.

Su, W.-H.; Sun, D.-W. 2016. Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging. Talanta 155: 347-357.

Su, W.-H.; Sun, D.-W. 2017. Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour. Journal of Food Engineering 200: 59-69.

Sun, J.; Lu, X.; Mao, H.; Jin, X.; Wu, X. 2017. A Method for Rapid Identification of Rice Origin by Hyperspectral Imaging Technology. Journal of Food Process Engineering 40: e12297.

Sun, W.; Zhang, X.; Zhang, Z.; Zhu, R. 2017. Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 171: 72-79.

Thorburn-Burns, D.; Tweed, L.; Walker, M.J. 2017. Ground Roast Coffee: Review of Analytical Strategies to Estimate Geographic Origin, Species Authenticity and Adulteration by Dilution. Food Analytical Methods 10: 2302-2310.

Trifković, J.; Andrić, F.; Ristivojević, P.; Guzelmeric, E.; Yesilada, E. 2017. Analytical methods in tracing honey authenticity. Journal of AOAC International 100: 827-839.

Tähkäpää, S.; Maijala, R.; Korkeala, H.; Nevas, M. 2015. Patterns of food frauds and adulterations reported in the EU rapid alert system for food and feed and in Finland. Food Control 47: 175-184.

van Ruth, S. M.; Luning, P. A.; Silvis, I. C. J.; Yang, Y.; Huisman, W. 2018. Differences in fraud vulnerability in various food supply chains and their tiers. Food Control 84: 375-381.

Vandeginste, B.G.M.; Massart, D.L.; De Jong, S.; Massaart, D.L.; Buydens, L.M.C. 1998. Handbook of chemometrics and qualimetrics: Part B: Elsevier.

Verdú, S.; Vásquez, F.; Grau, R.; Ivorra, E.; Sánchez, A. J.; Barat, J. M. 2016. Detection of adulterations with different grains in wheat products based on the hyperspectral image technique: The specific cases of flour and bread. Food Control 62: 373-380.

Vermeulen, P.; Suman, M.; Fernández Pierna, J. A.; Baeten, V. 2018. Discrimination between durum and common wheat kernels using near infrared hyperspectral imaging. Journal of Cereal Science 84: 74-82.

Wang, P.; Sun, J.; Zhang, T.; Liu, W. 2016. Vibrational spectroscopic approaches for the quality evaluation and authentication of virgin olive oil. Applied Spectroscopy Reviews 51: 763-790.

Wu, J.; Dong, J.; Dong, W.; Chen, Y.; Liu, C. 2016. Rapid authentication of adulteration of olive oil by near-infrared spectroscopy using support vector machines. In International Symposium on Optoelectronic Technology and Application 2016, vol. 10157. Pp. 12: SPIE.

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.

Wójcicki, K.; Khmelinskii, I.; Sikorski, M.; Caponio, F.; Paradiso, V. M.; Summo, C.; Pasqualone, A.; Sikorska, E. 2015. Spectroscopic techniques and chemometrics in analysis of blends of extra virgin with refined and mild deodorized olive oils. European Journal of Lipid Science and Technology 117: 92-102.

Xiao, R.; Liu, L.; Zhang, D.; Ma, Y.; Ngadi, M. O. 2018. Discrimination of organic and conventional rice by chemometric analysis of NIR spectra: a pilot study. Journal of Food Measurement and Characterization 1-12.

Xiong, Z.; Sun, D.-W.; Pu, H.; Zhu, Z.; Luo, M. 2015. Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats. LWT - Food Science and Technology 60: 649-655.

Xu, J.-L.; Riccioli, C.; Sun, D.-W. 2017. Comparison of hyperspectral imaging and computer vision for automatic differentiation of organically and conventionally farmed salmon. Journal of Food Engineering 196: 170-182.

Zhang, C.; Liu, F.; He, Y. 2018. Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Scientific Reports 8: 2166.

Zhang, X.; Li, W.; Yin, B.; Chen, W.; Kelly, D. P.; Wang, X.; Zheng, K.; Du, Y. 2013. Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 114: 350-356.

Zhao, H.; Guo, B.; Wei, Y.; Zhang, B. 2013. Near infrared reflectance spectroscopy for determination of the geographical origin of wheat. Food Chemistry 138: 1902-1907.

Zhao, H.; Guo, B.; Wei, Y.; Zhang, B. 2014. Effects of grown origin, genotype, harvest year, and their interactions of wheat kernels on near infrared spectral fingerprints for geographical traceability. Food Chemistry 152: 316-322.

Zheng, X.; Li, Y.; Wei, W.; Peng, Y. 2019. Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging. Meat Science 149: 55-62.

Zhou, X.; Yang, Z.; Haughey, S. A.; Galvin-King, P.; Han, L.; Elliott, C. T. 2015. Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study. Food Chemistry 189: 13-18.

Zhu, H.; Basir, O. 2006. A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing. Soft Computing 10: 1169-1180.

Ziegler, J.U.; Leitenberger, M.; Longin, C.F.H.; Würschum, T.; Carle, R.; Schweiggert, R.M. 2016. Near-infrared reflectance spectroscopy for the rapid discrimination of kernels and flours of different wheat species. Journal of Food Composition and Analysis 51: 30-36.

Received December 10, 2018.

Accepted March 2, 2019.

Corresponding author: (R. Siche).



Cómo citar

Mendez, J., Mendoza, L., Cruz-Tirado, J., Quevedo, R., & Siche, R. (2019). Trends in application of NIR and hyperspectral imaging for food authentication. Scientia Agropecuaria, 10(1), 143-161.



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