Investigation of reflectance, fluorescence, and Raman hyperspectral imaging techniques for rapid detection of aflatoxins in ground maize. (February 2022)

• Record Type: Journal Article
• Authors: Kim, Yong-Kyoung; Baek, Insuck; Lee, Kyung-Min; Qin, Jianwei; Kim, Geonwoo; Shin, Byeung Kon; Chan, Diane E.; Herrman, Timothy J.; Cho, Soon-kil; Kim, Moon S.
• Is Part Of: Food control. Volume 132(2022)
• Publication Date: 2022-02
• Subjects: Hyperspectral imaging -- Aflatoxin -- Maize -- Machine learning -- Classification; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2021.108479
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Aflatoxins, commonly found in corn and corn-derived products, can cause severe illness in animals and humans if consumed in significant amounts. Early detection is critical to preventing illness, but the most sensitive and effective of commonly used screening tools for aflatoxins are expensive and cumbersome methods based on chromatography or imunoassays that require technical expertise to perform. Multiple hyperspectral imaging techniques, including reflectance in the visible and near-infrared (VNIR) region and short-wave infrared (SWIR) region, fluorescence by 365 nm ultraviolet (UV) excitation, and Raman by 785 nm laser excitation, were used for detection of aflatoxin in ground maize. Four classification models based on linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines (QSVM) algorithms were developed for classification with each hyperspectral imaging mode. The multivariate classification models in combination with different preprocessing methods were applied for screening of maize samples naturally contaminated with aflatoxin. The classification accuracies for fluorescence with QSVM, VNIR with QSVM, SWIR with LSVM, and Raman with LSVM were 95.7%, 82.6%, 95.7%, and 87.0%, respectively, with no false-negative error at the cutoff of 10 μg/kg. The SWIR and fluorescence models showed slightly higher performance accuracies, suggesting that they may be more effective and efficient analytical tools for aflatoxin analysis in maize compared to conventional wet-chemical methods. These methods show promise as inexpensive, and easy-to-use screening tools for food safety, to rapidly detect aflatoxins in maize or other food ingredients intended for animal or human consumption. Highlights: Comparison of reflectance, fluorescence, and Raman imaging for aflatoxin in maize Machine learning algorithms for analysis of hyperspectral images of ground maize Higher classification accuracies from fluorescence and SWIR reflectance imaging Easy-to-use screening tools to rapidly detect aflatoxin contamination for food safety