Amiel, C., Mariey, L., Curk-Daubié, M.C., Pichon, P. and Travert, J., 2000. Potentiality of Fourier transform infrared spectroscopy (FTIR) for discrimination and identification of dairy lactic acid bacteria. Le Lait 80: 445-459. Crossref, Google Scholar
	Ariana, D.P. and Lu, R., 2010. Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. Journal of Food Engineering 96: 583-590. Crossref, Google Scholar
	Ataş, M., Yardimci, Y. and Temizel, A., 2012. A new approach to aflatoxin detection in chili pepper by machine vision. Computers and Electronics in Agriculture 87: 129-141. Crossref, Google Scholar
	Barbedo, J.G., Tibola, C.S. and Fernandes, J.M., 2015. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosystems Engineering, 131: 65-76. Crossref, Google Scholar
	Barbin, D., Elmasry, G., Sun, D. W. and Allen, P., 2012. Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science 90: 259-268. Crossref, Google Scholar
	Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U. and Herppich, W.B., 2011. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture 75: 304-312. Crossref, Google Scholar
	Burlakoti, R.R., Rolando Estrada, J., Rivera, V.V., Boddeda, A., Secor, G.A. and Adhikari, T.B., 2007. Real-time pcr quantification and mycotoxin production of Fusarium graminearum in wheat inoculated with isolates collected from potato, sugar beet, and wheat. Phytopathology 97: 835. Crossref, Google Scholar
	Cen, H. and He, Y., 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science and Technology 18: 72-83. Crossref, Google Scholar
	Chen, K. and Qin, C., 2008. Segmentation of beef marbling based on vision threshold. Computers and Electronics in Agriculture 62: 223-230. Crossref, Google Scholar
	Cheng, J.H. and Sun, D.W., 2015. Recent applications of spectroscopic and hyperspectral imaging techniques with chemometric analysis for rapid inspection of microbial spoilage in muscle foods. Comprehensive Reviews in Food Science and Food Safety 14: 478-490. Crossref, Google Scholar
	Chu, X., Wang, W., Yoon, S.C., Ni, X. and Heitschmidt, G.W., 2017. Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging. Biosystems Engineering 157: 13-23. Crossref, Google Scholar
	Cozzolino, D., 2009. Near infrared spectroscopy in natural products analysis. Planta Medica 75: 746-756. Crossref, Google Scholar
	Cozzolino, D., Roumeliotis, S., and Eglinton, J., 2013. Exploring the use of near infrared (NIR) reflectance spectroscopy to predict starch pasting properties in whole grain barley. Food Biophysics 8: 256-261. Crossref, Google Scholar
	Creppy, E.E., 2002. Update of survey, regulation and toxic effects of mycotoxins in Europe. Toxicology Letters 127: 19-28. Crossref, Google Scholar
	De Girolamo, A., Cervellieri, S., Cortese, M., Porricelli, A.C.R., Pascale, M., Longobardi, F., Holst, C.V., Ciaccheri, L. and Lippolis, V., 2019. Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran. Journal of the Science of Food and Agriculture 99: 1946-1953. Crossref, Google Scholar
	De Girolamo, A., Lippolis, V., Nordkvist, E. and Visconti, A., 2009. Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy. Food Additives and Contaminants Part A 26: 907-917. Crossref, Google Scholar
	Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A.A., Bonifazi, C. and Fanelli, C., 2010. Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. International Journal of Food Microbiology 144: 64-71. https://doi.org/10.1016/j.ijfoodmicro.2010.08.001 Google Scholar
	Egerton, T.A. and Hardin, A.H., 1975. The application of Raman spectroscopy to surface chemical studies. Catalysis Reviews Science and Engineering 11: 71-116. Crossref, Google Scholar
	El Masry, G., Barbin, D.F., Sun, D.W. and Allen, P., 2012a. Meat quality evaluation by hyperspectral imaging technique: an overview. Critical Reviews in Food Science and Nutrition 52: 689-711. Crossref, Google Scholar
	El Masry, G., Kamruzzaman, M., Sun, D.W. and Allen, P., 2012b. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Critical Reviews in Food Science and Nutrition 52: 999-1023. Crossref, Google Scholar
	El Masry, G., Wang, N., El Sayed, A. and Ngadi, M., 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering 81: 98-107. Crossref, Google Scholar
	Fu, X., Kim, M.S., Chao, K., Qin, J., Lim, J., Lee, H., Garrido-Varod, A., Pérez-Marín, D. and Ying, Y., 2014. Detection of melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses. Journal of Food Engineering 124: 97-104. Crossref, Google Scholar
	Gaspardo, B., Del Zotto, S., Torelli, E., Cividino, S.R., Firrao, G., Della Riccia, G., and Stefanon, B., 2012. A rapid method for detection of fumonisins B1 and B2 in corn meal using Fourier transform near infrared (FT-NIR) spectroscopy implemented with integrating sphere. Food Chemistry 135: 1608-1612. Crossref, Google Scholar
	Gourama, H. and Bullerman, L.B., 1995. Detection of molds in foods and feeds: potential rapid and selective methods. Journal of Food Protection 58: 1389-1394. Crossref, Google Scholar
	Greene, R.V., Gordon, S.H., Jackson, M.A., Bennett, G.A., McClelland, J.F. and Jones, R.W., 1992. Detection of fungal contamination in corn: potential of FTIR-PAS and-DRS. Journal of Agricultural and Food Chemistry 40: 1144-1149. Crossref, Google Scholar
	Gupta, V.K., Tuohy, M.G., Ayyachamy, M., O’Donovan, A. and Turner, K.M., 2013. Laboratory protocols in fungal biology. Laboratory Protocols in Fungal Biology. Springer, New York, NY, USA. Google Scholar
	Guyon, I. and Elisseeff, A., 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3: 1157-1182. Google Scholar
	Hernandez-Hierro, J.M., Garcia-Villanova, R.J. and Gonzalez-Martin, I., 2008. Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market. Analytica Chimica Acta 622: 189-194. Crossref, Google Scholar
	Hruska, Z., Yao, H., Kincaid, R., Darlington, D. and Cleveland, T.E., 2013. Fluorescence imaging spectroscopy (FIS) for comparing spectra from corn ears naturally and artificially infected with aflatoxin producing fungus. Journal of Food Science 78: 1313-1320. Crossref, Google Scholar
	Huang, X., Ding, R. and Shi, J., 2015. Studies on non-destructive testing method of moldy and budding peanuts by near infrared spectroscopy. Journal of Agricultural Science and Technology 17: 27-32. Google Scholar
	Jia, B., Yoon, S. C., Zhuang, H., Wang, W. and Li, C., 2017. Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging. Journal of Food Engineering 208: 57-65. Crossref, Google Scholar
	Jiang, H., Wang, W., Ni, X.Z., Zhuang, H., Yoon, S.C. and Lawrence, K.C., 2018. Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University. NIR News 29: 19-23. Crossref, Google Scholar
	Jiang, J., Qiao, X. and He, R., 2016. Use of near-infrared hyperspectral images to identify moldy peanuts. Journal of Food Engineering 169: 284-290. Crossref, Google Scholar
	Jiang, X.S., Liu, P., Shen, F., Zhou, H. and Chen, Q., 2017. Analysis of moldy peanut kernel by attenuated total reflectance-Fourier transform infrared spectroscopy. Food Science 38: 315-320. Google Scholar
	Jin, C., Zhang, Q. and Zheng, X., 2016. Near infrared spectroscopy forecasting model of the total number storage rice mould. Journal of Agricultural Mechanization Research 10: 160-164. Google Scholar
	Jin, F., Bai, G., Zhang, D., Dong, Y., Ma, L., Bockus, W. and Dowell, F., 2014. Fusarium-damaged kernels and deoxynivalenol in Fusarium-infected U.S. winter wheat. Phytopathology 104: 472-478. Crossref, Google Scholar
	Jin, J., Tang, L., Hruska, Z. and Yao, H., 2009. Classification of toxigenic and atoxigenic strains of Aspergillus flavus with hyperspectral imaging. Computers and Electronics in Agriculture 69: 158-164. Crossref, Google Scholar
	Kandpal, L.M., Lee, S., Kim, M.S., Bae, H. and Cho, B.K., 2015. Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51: 171-176. Crossref, Google Scholar
	Karoui, R., Downey, G. and 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: 6144-6168. Crossref, Google Scholar
	Kaya-Celiker, H., Mallikarjunan, P.K. and Kaaya, A., 2015. Mid-infrared spectroscopy for discrimination and classification of aspergillus spp. contamination in peanuts. Food Control 52: 103-111. Crossref, Google Scholar
	Kaya-Celiker, H., Mallikarjunan, P.K. and Kaaya, A., 2016. Characterization of invasion of genus Aspergillus on peanut seeds using ftir-pas. Food Analytical Methods 9: 105-113. Crossref, Google Scholar
	Kaya-Celiker, H., Mallikarjunan, P.K., Schmale III, D. and Christie, M.E., 2014. Discrimination of moldy peanuts with reference to aflatoxin using FTIR-ATR system. Food Control 44: 64-71. Crossref, Google Scholar
	Kizil, R., Irudayaraj, J. and Seetharaman, K., 2002. Characterization of irradiated starches by using FT-Raman and FTIR spectroscopy. Journal of Agricultural and Food Chemistry 50: 3912-3918. Crossref, Google Scholar
	Kos, G., Lohninger, H. and Krska, R., 2002. Classification of maize contaminated with Fusarium graminearum using mid-infrared spectroscopy and chemometrics. Mycotoxin Research 18: 104-108. Crossref, Google Scholar
	Kos, G., Lohninger, H. and Krska, R., 2003. Development of a method for the determination of Fusarium fungi on corn using mid-infrared spectroscopy with attenuated total reflection and chemometrics. Analytical Chemistry 75: 1211-1217. https://doi.org/10.1021/ac0260903 Google Scholar
	Kos, G., Sieger, M., McMullin, D., Zahradnik, C., Sulyok, M., Öner, T., Mizaikoff, B. and Krska, R., 2016. A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits. Food Additives and Contaminants Part A 33: 1596-1607. Crossref, Google Scholar
	Lea, T., Steien, K. and Størmer, F.C., 1989. Mechanism of ochratoxin A-induced immunosuppresion. Mycopathologia 107: 153-159. Crossref, Google Scholar
	Lee, K.M. and Herrman, T.J., 2016. Determination and prediction of fumonisin contamination in maize by surface-enhanced Raman spectroscopy (SERS). Food and Bioprocess Technology, 9: 588-603. Crossref, Google Scholar
	Lee, K.M., Herrman, T.J., Bisrat, Y. and Murray, S.C., 2014b. Feasibility of surface-enhanced Raman spectroscopy for rapid detection of aflatoxins in maize. Journal of Agricultural and Food Chemistry 62: 4466-4474. Crossref, Google Scholar
	Lee, K.M., Herrman, T.J. and Yun, U., 2014a. Application of Raman spectroscopy for qualitative and quantitative analysis of aflatoxins in ground maize samples. Journal of Cereal Science 59: 70-78. Crossref, Google Scholar
	Leonard, K.J. and Bushnell, W.R., 2004. Fusarium head blight of wheat and barley. APS Press, St. Paul, MN, USA. Google Scholar
	Lim, J., Kim, G., Mo, C., Oh, K., Kim, G., Ham, H., Kim, S. and Kim, M.S., 2018. Application of near infrared reflectance spectroscopy for rapid and non-destructive discrimination of hulled barley, naked barley, and wheat contaminated with Fusarium. Sensors 18: 113. Crossref, Google Scholar
	Liu, X., Tang, Z., Duan, Z., He, Z. and Xu, Y., 2016. Nanobody-based enzyme immunoassay for ochratoxin a in cereal with high resistance to matrix interference. Talanta 164: 154-158. Crossref, Google Scholar
	Liu, Y., Delwiche, S.R. and Dong, Y., 2009. Feasibility of FT-Raman spectroscopy for rapid screening for DON toxin in ground wheat and barley. Food Additives and Contaminants Part A 26: 1396-1401. Crossref, Google Scholar
	Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J. and White, N.D.G., 2008. Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering 101: 50-57. Crossref, Google Scholar
	Makio, T., Hiroaki, I., Tomohiro, T., Hisaya, Y., Kumiko, N. and Nobuaki, T., 2007. Classification of pesticide residues in the agricultural products based on diffuse reflectance IR spectroscopy. In: SICE Annual Conference 2007. Institute of Electrics and Electronic Engineers, New York, NY, USA. https://doi.org/10.1109/SICE.2007.4420979 Google Scholar
	Manley, M., 2014. Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews 43: 8200-8214. Crossref, Google Scholar
	Maquelin, K., Kirschner, C., Choo-Smith, L.P., Van den Braak, N., Endtz, H.P., Naumann, D. and Puppels, G.J., 2002. Identification of medically relevant microorganisms by vibrational spectroscopy. Journal of Microbiological Methods 51: 255-271. Crossref, Google Scholar
	Mariey, L., Signolle, J.P., Amiel, C. and Travert, J., 2001. Discrimination, classification, identification of microorganisms using FTIR spectroscopy and chemometrics. Vibrational Spectroscopy 26: 151-159. Crossref, Google Scholar
	Martos, P., Thompson, W. and Diaz, G., 2010. Multiresidue mycotoxin analysis in wheat, barley, oats, rye and maize grain by high-performance liquid chromatography-tandem mass spectrometry. World Mycotoxin Journal 3: 205-223. Wageningen Academic Publishers, Google Scholar
	Matousek, P., 2006. Inverse spatially offset Raman spectroscopy for deep non-invasive probing of turbid media. Applied Spectroscopy 60: 1341-1347. Crossref, Google Scholar
	Munir, M.T., Wilson, D.I., Yu, W. and Young, B.R., 2018. An evaluation of hyperspectral imaging for characterising milk powders. Journal of Food Engineering 221: 1-10. Crossref, Google Scholar
	Orina, I., Manley, M. and Williams, P.J., 2017. Non-destructive techniques for the detection of fungal infection in cereal grains. Food Research International 100: 74-86. Crossref, Google Scholar
	Osborne, B.G., 2006. Near-infrared spectroscopy in food analysis. In: Meyers, R.A. and McGorrin, R.J. (eds.) Encyclopedia of analytical chemistry: applications, theory and instrumentation. https://doi.org10.1002/9780470027318.a1018 Google Scholar
	Paolesse, R., Alimelli, A., Martinelli, E., Natale, C.D., D’Amico, A., D’Egidio, M.G., Aureli, G., Ricelli, A. and Fanelli, C., 2006. Detection of fungal contamination of cereal grain samples by an electronic nose. Sensors and Actuators B: Chemical 119: 425-430. https://doi.org/10.1016/j.snb.2005.12.047 Google Scholar
	Pascale, M.N., 2009. Detection methods for mycotoxins in cereal grains and cereal products. Zbornik Matice Srpske za Prirodne Nauke 117: 15-25. Google Scholar
	Pearson, T.C. Wicklow, D.T. and Pasikatan, M.C., 2004. Reduction of aflatoxin and fumonisin contamination in yellow corn by high-speed bi-chromatic sorting. Cereal Chemistry 81: 490-498. Crossref, Google Scholar
	Peiris, K.H., Bockus, W.W. and Dowell, F.E., 2012. Infrared spectral properties of germ, pericarp, and endosperm sections of sound wheat kernels and those damaged by Fusarium graminearum. Applied Spectroscopy 66: 1053-1060. Crossref, Google Scholar
	Peiris, K.H., Dong, Y., Bockus, W.W. and Dowell, F.E., 2017. Estimation of bulk deoxynivalenol and moisture content of bulk wheat grain samples by FT-NIR spectroscopy. Cereal Chemistry 94: 677-682. Crossref, Google Scholar
	Peiris, K.H., Pumphrey, M.O. and Dowell, F.E., 2009. NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels. Journal of Near Infrared Spectroscopy 17: 213-221. Crossref, Google Scholar
	Peiris, K.H.S., Pumphrey, M.O., Dong, Y., Maghirang, E.B., Berzonsky, W. and Dowell, F.E., 2010. Near-infrared spectroscopic method for identification of Fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels. Cereal Chemistry 87: 511-517. Crossref, Google Scholar
	Pestka, J.J. and Smolinski, A.T., 2005. Deoxynivalenol: toxicology and potential effects on humans. Journal of Toxicology and Environmental Health Part B 8: 39-69. Crossref, Google Scholar
	Phillips, D.L., Xing, J., Liu, H., Pan, D.H. and Corke, H., 1999. Potential use of Raman spectroscopy for determination of amylose content in maize starch. Cereal Chemistry 76: 821-823. Crossref, Google Scholar
	Porep, J.U., Kammerer, D.R. and Carle, R., 2015. On-line application of near infrared (NIR) spectroscopy in food production. Trends in Food Science and Technology 46: 211-230. Crossref, Google Scholar
	Qiao, X., Jiang, J., Qi, X., Guo, H. and Yuan, D., 2017. Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts. Food Chemistry 220: 393-399. Crossref, Google Scholar
	Ravikanth, L., Jayas, D.S., White, N.D.G., Fields, P.G., and Sun, D.W., 2017. Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food and Bioprocess Technology, 10: 1-33. Crossref, Google Scholar
	Ravikanth, L., Singh, C.B., Jayas, D.S. and White, N.D., 2015. Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering 135: 73-86. Crossref, Google Scholar
	Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A. and Jent, N., 2007. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. Journal of Pharmaceutical and Biomedical Analysis 44: 683-700. Crossref, Google Scholar
	Scotter, C.N., 1997. Non-destructive spectroscopic techniques for the measurement of food quality. Trends in Food Science and Technology 8: 285-292. Crossref, Google Scholar
	Selvaraj, J.N., Zhao, L., Wang, Y., Zhao, Y.J., Xing, F.G., Dai, X.F. and Liu, Y., 2015. Mycotoxin detection-recent trends at global level. Journal of Integrative Agriculture 14: 2265-2281. Crossref, Google Scholar
	Senthilkumar, T., Jayas, D.S., White, N.D., Fields, P.G. and Gräfenhan, T., 2016a. Detection of fungal infection and ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. Journal of Stored Products Research 65: 30-39. Crossref, Google Scholar
	Senthilkumar, T., Jayas, D.S., White, N.D., Fields, P.G. and Gräfenhan, T., 2016b. Detection of fungal infection and ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging. Biosystems Engineering 147: 162-173. Crossref, Google Scholar
	Sforza, S., Dall’Asta, C. and Marchelli, R., 2006. Recent advances in mycotoxin determination in food and feed by hyphenated chromatographic techniques/mass spectrometry. Mass Spectrometry Reviews 25: 54-76. Crossref, Google Scholar
	Shahin, M.A. and Symons, S.J., 2012. Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. Journal of Food Measurement and Characterization 6: 3-11. Crossref, Google Scholar
	Shen, F., Liu, X., Pei, F., Li, P., Jiang, D. and Liu, Q., 2019. Rapid identification of deoxynivalenol contamination in wheat and its products by attenuated total reflectance-fourier transform infrared spectroscopy (ATR-FTIR). Food Science 40: 293-297. Google Scholar
	Shen, F., Wu, Q., Shao, X. and Zhang, Q., 2018. Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques. Journal of Food Science and Technology 55: 1175-1184. Crossref, Google Scholar
	Shen, F., Wu, Q., Tang, A., Shao, X. and Jiang, D., 2016. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) for rapid detection of aflatoxin B1 in brown rice. Food Science 37: 187-191. Google Scholar
	Siesler, H.W., 2011. Vibrational spectroscopy of polymers. International Journal of Polymer Analysis and Characterization 16: 519-541. Crossref, Google Scholar
	Siripatrawan, U. and Makino, Y., 2015. Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. International Journal of Food Microbiology 199: 93-100. Crossref, Google Scholar
	Sirisomboon, C.D., Putthang, R. and Sirisomboon, P., 2013. Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control 33: 207-214. Crossref, Google Scholar
	Smith, E. and Dent, G., 2019. Modern Raman spectroscopy: a practical approach, 2nd edition. John Wiley and Sons, Ltd., Chichester, UK. Google Scholar
	Su, W.H., He, H.J. and Sun, D.W., 2017. Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review. Critical Reviews in Food Science and Nutrition 57: 1039-1051. Crossref, Google Scholar
	Taabu, M.S., Birech, Z. and Kaduki, K., 2015. Application of Raman spectroscopy in detection of aflatoxin B1 in maize kernels. In: 2015 11th Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR). Institute of Electrics and Electronic Engineers, New York, NY, USA. https://doi.org/10.1109/CLEOPR.2015.7376500 Google Scholar
	Taghizadeh, M., Gowen, A.A. and O’Donnell, C.P., 2011. The potential of visible-near infrared hyperspectral imaging to discriminate between casing soil, enzymatic browning and undamaged tissue on mushroom (Agaricus bisporus) surfaces. Computers and Electronics in Agriculture 77: 74-80. Crossref, Google Scholar
	Talens, P., Mora, L., Morsy, N., Barbin, D.F., El Masry, G. and Sun, D.W., 2013. Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging. Journal of Food Engineering 117: 272-280. Crossref, Google Scholar
	Teena, M.A., Manickavasagan, A., Ravikanth, L. and Jayas, D.S., 2014. Near infrared (NIR) hyperspectral imaging to classify fungal infected date fruits. Journal of Stored Products Research 59: 306-313. Crossref, Google Scholar
	Thiex, N., Torma, L., Pickering, M., Nerkar, S. and Ofitserova, M., 2008. Multiresidue mycotoxin analysis in corn grain by column high-performance liquid chromatography with postcolumn photochemical and chemical derivatization: single-laboratory validation. Journal of Aoac International 92: 15-25. Crossref, Google Scholar
	Todescato, F., Antognoli, A., Meneghello, A., Cretaio, E., Signorini, R. and Bozio, R., 2014. Sensitive detection of ochratoxin A in food and drinks using metal-enhanced fluorescence. Biosensors and Bioelectronics 57: 125-132. Crossref, Google Scholar
	Turner, N.W., Subrahmanyam, S. and Piletsky, S.A., 2009. Analytical methods for determination of mycotoxins: a review. Analytica Chimica Acta 632: 168-180. Crossref, Google Scholar
	Ur-Rahman, H., Yue, X., Yu, Q., Zhang, W., Zhang, Q. and Li, P., in press. Current PCR-based methods for the detection of mycotoxigenic fungi in complex food and feed matrices. World Mycotoxin Journal. 13: 139-150. https://doi.org/10.3920/WMJ2019.2455 Google Scholar
	Vankeirsbilck, T., Vercauteren, A., Baeyens, W., Van der Weken, G., Verpoort, F., Vergote, G. and Remon, J.P., 2002. Applications of Raman spectroscopy in pharmaceutical analysis. Trends in Analytical Chemistry 21: 869-877. Crossref, Google Scholar
	Vidal, M. and Amigo, J.M., 2012. Pre-processing of hyperspectral images. essential steps before image analysis. Chemometrics and Intelligent Laboratory Systems 117: 138-148. Crossref, Google Scholar
	Wagacha, J.M. and Muthomi, J.W., 2008. Mycotoxin problem in Africa: current status, implications to food safety and health and possible management strategies. International Journal of Food Microbiology 124: 1-12. Crossref, Google Scholar
	Wang, L., Sun, D.W., Pu, H. and Cheng, J.H., 2017. Quality analysis, classification, and authentication of liquid foods by near-infrared spectroscopy: a review of recent research developments. Critical Reviews in Food Science and Nutrition 57: 1524-1538. Crossref, Google Scholar
	Wang, W., Heitschmidt, G.W., Ni, X., Windham, W.R., Hawkins, S. and Chu, X., 2014. Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging. Food Control 42: 78-86. Crossref, Google Scholar
	Wang, W., Heitschmidt, G.W., Windham, W.R., Feldner, P., Ni, X. and Chu, X., 2015a. Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging. Journal of Food Science 80: 116-122. Crossref, Google Scholar
	Wang, W., Ni, X., Lawrence, K.C., Yoon, S.C., Heitschmidt, G.W. and Feldner, P., 2015b. Feasibility of detecting aflatoxin B1 in single maize kernels using hyperspectral imaging. Journal of Food Engineering 166: 182-192. Crossref, Google Scholar
	Williams, P., Manley, M., Fox, G. and Geladi, P., 2010. Indirect detection of Fusarium verticillioides in maize (Zea mays L.) kernels by near infrared hyperspectral imaging. Journal of Near Infrared Spectroscopy 18: 49-58. Crossref, Google Scholar
	Williams, P.J., Geladi, P., Britz, T.J. and Manley, M., 2012. Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. Journal of Cereal Science 55: 272-278. Crossref, Google Scholar
	Wu, Q., Xie, L. and Xu, H., 2018. Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques. Food Chemistry 252: 228-242. Crossref, Google Scholar
	Wu, X., Gao, S., Wang, J.S., Wang, H., Huang, Y.W. and Zhao, Y., 2012. The surface-enhanced Raman spectra of aflatoxins: spectral analysis, density functional theory calculation, detection and differentiation. Analyst 137: 4226-4234. Crossref, Google Scholar
	Yang, D. and Ying, Y. 2011. Applications of Raman spectroscopy in agricultural products and food analysis: A review. Applied Spectroscopy Reviews 46: 539-560. Crossref, Google Scholar
	Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D., Cleveland, T.E., 2013. Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosystems Engineering 115: 125-135. Crossref, Google Scholar
	Yao, H., Hruska, Z., Kincaid, R., Ononye, A., Brown, R.L. and Cleveland, T.E., 2010. Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image. Spie Defense, Security, and Sensing 2010: 7676. Google Scholar
	Yazar, S. and Omurtag, G.Z., 2008. Fumonisins, trichothecenes and zearalenone in cereals. International Journal of Molecular Sciences 9: 2062-2090. Crossref, Google Scholar
	Yu, K.Q., Zhao, Y.R., Liu, Z.Y., Li, X.L., Liu, F. and He, Y., 2014. Application of visible and near-infrared hyperspectral imaging for detection of defective features in loquat. Food and Bioprocess Technology 7: 3077-3087. Crossref, Google Scholar
	Yuan, J., Sun, C., Guo, X., Yang, T., Wang, H., Fu, S., Li, C. and Yang, H., 2017. A rapid Raman detection of deoxynivalenol in agricultural products. Food Chemistry 221: 797-802. Crossref, Google Scholar
	Zhang, Q., Jia, F., Liu, C., Sun, J. and Zheng, X., 2014. Rapid detection of aflatoxin B1 in paddy rice as analytical quality assessment by near infrared spectroscopy. International Journal of Agricultural and Biological Engineering 7: 127-133. Google Scholar
	Zhang, W.W., Ye, Z.M., Jin, Y., Wang, S.Y., Zhang, L.S. and Pei, X.F., 2014. Management of mycotoxin contamination in food and feed in China. World Mycotoxin Journal 7: 53-62. https://doi.org/10.3920/wmj2013.1553 Google Scholar
	Zhu, F., Zhang, D., He, Y., Liu, F. and Sun, D.W., 2013. Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen-thawed fish fillets. Food and Bioprocess Technology 6: 2931-2937. Crossref, Google Scholar