Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA.
USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA.
J Food Prot. 2024 Sep;87(9):100335. doi: 10.1016/j.jfp.2024.100335. Epub 2024 Jul 27.
The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.
利用 785nm 激发线激光的拉曼高光谱成像技术的潜力，用于检测玉米颗粒中的黄曲霉毒素污染。在实验室中人工接种了 900 颗玉米，其中 300 颗分别接种了产黄曲霉毒素（黄曲霉毒素产生）真菌、非产黄曲霉毒素（非黄曲霉毒素产生）真菌和无菌蒸馏水（对照）。每种处理的 100 颗玉米随后分别在 3、5 和 8 天进行培养。从胚乳侧和胚侧提取单个玉米籽粒的平均光谱，并根据单独计算的黄曲霉毒素阴性和阳性类别的参考光谱来确定局部拉曼峰。使用不同类型的变量输入，包括原始全光谱、预处理全光谱和鉴定的内核胚乳侧、胚侧和两侧的局部峰，建立了主成分分析-线性判别分析模型。建立的判别模型结果表明，胚侧光谱的性能优于胚乳侧光谱。基于 20ppb 阈值，使用双侧组合形式的原始光谱，对黄曲霉毒素阴性类别获得了 82.6%的最佳平均预测准确率，使用预处理的胚侧光谱，对黄曲霉毒素阳性类别获得了 86.7%的最佳平均预测准确率。基于 100ppb 阈值，使用相同类型的变量输入，对黄曲霉毒素阴性和阳性类别分别获得了 85.0%和 89.6%的最佳平均预测准确率。在整体预测准确率方面，使用双侧组合形式的原始光谱建立的模型无论阈值如何，都具有最佳的预测性能。使用 20ppb 和 100ppb 阈值，平均整体预测准确率分别为 81.8%和 84.5%。
