Detection of aflatoxin contamination in single kernel almonds using multispectral imaging system

Agriopoulou, 2020, Advances in occurrence, importance, and mycotoxin control strategies: Prevention and detoxification in foods, Foods, 9, 137, 10.3390/foods9020137 Atas, 2011, Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning, Vol. 8027, 108 Campbell, 2003, Current research on reducing pre‐and post‐harvest aflatoxin contamination of US almond, pistachio, and walnut., J. Toxicol.: Toxin Rev., 22, 225 Chu, 2017, Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging, Biosyst. Eng., 157, 13, 10.1016/j.biosystemseng.2017.02.005 Gupta, 2011, Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks, Talanta, 83, 1014, 10.1016/j.talanta.2010.11.017 Han, 2019, Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging, Comput. Electron. Agric., 164, 10.1016/j.compag.2019.104888 Jin, 2009, Classification of toxigenic and atoxigenic strains of Aspergillus flavus with hyperspectral imaging, Comput. Electron. Agric., 69, 158, 10.1016/j.compag.2009.07.023 LaValley, 2008, Logistic regression, Circulation, 117, 2395, 10.1161/CIRCULATIONAHA.106.682658 Le Cessie, 1992, Ridge estimators in logistic regression, J. R. Stat. Soc.: Ser. C. Appl. Stat., 41, 191 Mercier, G., & Lennon, M. (2003, July). Support vector machines for hyperspectral image classification with spectral-based kernels. In Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 288–290. Mishra, 2021, Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts, Compr. Rev. Food Sci. Food Saf., 20, 4612, 10.1111/1541-4337.12801 Mishra, 2022, Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds, LWT, 155, 10.1016/j.lwt.2021.112954 Panda, 2022, Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis, J. Food Eng., 318, 10.1016/j.jfoodeng.2021.110889 Ramirez, 2022, Multispectral camera system design for replacement of hyperspectral cameras for detection of aflatoxin B1, Comput. Electron. Agric., 198 Rinnan, 2009, Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends Anal. Chem., 28, 1201, 10.1016/j.trac.2009.07.007 Sadighara, 2021, The aflatoxin B1 content of peanut-based foods in Iran: a systematic review, Rev. Environ. Health Schwanninger, 2011, A review of band assignments in near infrared spectra of wood and wood components, J. Infrared Spectrosc., 19, 287, 10.1255/jnirs.955 Şengül, 2016, Comparing determination methods of detection and quantification limits for aflatoxin analysis in hazelnut, J. Food Drug Anal., 24, 56, 10.1016/j.jfda.2015.04.009 Singh, 2009, Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging, J. Stored Prod. Res., 45, 151, 10.1016/j.jspr.2008.12.002 Srivastava, 2019, Probabilistic artificial neural network and E-nose based classification of Rhyzopertha dominica infestation in stored rice grains, Chemom. Intell. Lab. Syst., 186, 12, 10.1016/j.chemolab.2019.01.007 Su, 2018, Trends in food authentication, 731 Tao, 2020, Detection of aflatoxin B 1 on corn kernel surfaces using visible-near infrared spectroscopy, J. Infrared Spectrosc., 28, 59, 10.1177/0967033519895686 Vapnik, 1999, The nature of statistical learning theory, Springe Sci. Bus. Media, 138 Vita, 2022, Aflatoxins contamination in nuts for direct human consumption: analytical findings from three years of official control in Italy, Int. J. Food Sci. Technol., 10.1111/ijfs.15723 Wei, 2019, Visual detection of the moisture content of tea leaves with hyperspectral imaging technology, J. Food Eng., 248, 89, 10.1016/j.jfoodeng.2019.01.004 Whitaker, 2006, Sampling almonds for aflatoxin, Part I: Estimation of uncertainty associated with sampling, sample preparation, and analysis, J. AOAC Int., 89, 1027, 10.1093/jaoac/89.4.1027 WHO (2018). Aflatoxins. Food safety digest. 〈https://www.who.int/foodsafety/FSDigest_Aflatoxins_EN.pdf〉 (Accessed on February 23, 2021. Yada, 2011, A review of composition studies of cultivated almonds: Macronutrients and micronutrients, J. Food Compos. Anal., 24, 469, 10.1016/j.jfca.2011.01.007 Yan, 2020, Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics, J. Food Process Eng., 43, 10.1111/jfpe.13378 Zhang, 2022, Application of hyperspectral imaging in the detection of aflatoxin B1 on corn seed, J. Food Meas. Charact., 16, 448, 10.1007/s11694-021-01171-7 Zhang, 2020, Moisture content detection of maize seed based on visible/near‐infrared and near‐infrared hyperspectral imaging technology., Int. J. Food Sci. Technol., 55, 631, 10.1111/ijfs.14317 Zhongzhi, 2018, Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data, Comput. Electron. Agric., 153, 248, 10.1016/j.compag.2018.08.018 Zhongzhi, 2020, Aflatoxin contaminated degree detection by hyperspectral data using band index, Food Chem. Toxicol., 137, 10.1016/j.fct.2020.111159