拉曼高光谱成像是快速、无损地识别玉米粒中黄曲霉毒素污染的潜在工具。

IF 2.1 4区 农林科学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal of food protection Pub Date : 2024-07-27 DOI:10.1016/j.jfp.2024.100335
Feifei Tao , Haibo Yao , Zuzana Hruska , Kanniah Rajasekaran , Jianwei Qin , Moon Kim , Kuanglin Chao
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引用次数: 0

摘要

利用 785 纳米激发线激光器对拉曼高光谱成像技术的潜力进行了研究,以检测玉米粒中黄曲霉毒素污染的情况。在实验室中人工接种了九百颗玉米粒,其中每 300 颗分别接种了 AF13(黄曲霉毒素)真菌、AF36(非黄曲霉毒素)真菌和无菌蒸馏水(对照组)。随后将每种处理的 100 粒果仁分别培养 3、5 和 8 天。从胚乳一侧和胚芽一侧的胚区提取单个核仁的平均光谱,并分别根据计算出的黄曲霉毒素阴性和阳性类别的参考光谱确定局部拉曼峰。利用不同类型的变量输入建立了主成分分析线性判别分析模型,这些变量包括原始全光谱、预处理全光谱以及在核仁胚乳侧、胚芽侧和两侧识别的局部峰值。建立的判别模型结果表明,胚芽侧光谱的性能优于胚乳侧光谱。在阈值为 20 ppb 的情况下,使用两侧核仁组合形式的原始光谱,黄曲霉毒素阴性类别的平均预测准确率达到 82.6%,而使用预处理的胚芽侧光谱,黄曲霉毒素阳性类别的平均预测准确率达到 86.7%。在阈值为 100 ppb 的情况下,使用与阈值为 20 ppb 相同类型的变量输入,黄曲霉毒素阴性和-阳性类别的最佳平均预测准确率分别为 85.0% 和 89.6%。就总体预测准确率而言,无论阈值如何,根据两个核边组合形式的原始光谱建立的模型都取得了最佳预测性能。在阈值为 20 ppb 和 100 ppb 时,平均总体预测准确率分别为 81.8% 和 84.5%。
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Raman Hyperspectral Imaging as a Potential Tool for Rapid and Nondestructive Identification of Aflatoxin Contamination in Corn Kernels

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.

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来源期刊
Journal of food protection
Journal of food protection 工程技术-生物工程与应用微生物
CiteScore
4.20
自引率
5.00%
发文量
296
审稿时长
2.5 months
期刊介绍: The Journal of Food Protection® (JFP) is an international, monthly scientific journal in the English language published by the International Association for Food Protection (IAFP). JFP publishes research and review articles on all aspects of food protection and safety. Major emphases of JFP are placed on studies dealing with: Tracking, detecting (including traditional, molecular, and real-time), inactivating, and controlling food-related hazards, including microorganisms (including antibiotic resistance), microbial (mycotoxins, seafood toxins) and non-microbial toxins (heavy metals, pesticides, veterinary drug residues, migrants from food packaging, and processing contaminants), allergens and pests (insects, rodents) in human food, pet food and animal feed throughout the food chain; Microbiological food quality and traditional/novel methods to assay microbiological food quality; Prevention of food-related hazards and food spoilage through food preservatives and thermal/non-thermal processes, including process validation; Food fermentations and food-related probiotics; Safe food handling practices during pre-harvest, harvest, post-harvest, distribution and consumption, including food safety education for retailers, foodservice, and consumers; Risk assessments for food-related hazards; Economic impact of food-related hazards, foodborne illness, food loss, food spoilage, and adulterated foods; Food fraud, food authentication, food defense, and foodborne disease outbreak investigations.
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