PDF(Pubmed)
Abstract：
Aflatoxin B1, a major global food safety concern, is produced by toxigenic fungi during crop growing, drying, and storage, and shows increasing annual prevalence. This study aimed to detect aflatoxin B1 in chili samples using ATR-FTIR coupled with machine learning algorithms. We found that 83.6% of the chili powder samples were contaminated with Aspergillus and Penicillium species, with aflatoxin B1 levels ranging from 7.63 to 44.32 μg/kg. ATR-FTIR spectroscopy in the fingerprint region (1800-400 cm-1) showed peak intensity variation in the bands at 1587, 1393, and 1038 cm-1, which are mostly related to aflatoxin B1 structure. The PCA plots from samples with different trace amounts of aflatoxin B1 could not be separated. Vibrational spectroscopy combined with machine learning was applied to address this issue. The logistic regression model had the best F1 score with the highest %accuracy (73%), %sensitivity (73%), and %specificity (71%), followed by random forest and support vector machine models. Although the logistic regression model contributed significant findings, this study represents a laboratory research project. Because of the peculiarities of the ATR-FTIR spectral measurements, the spectra measured for several batches may differ, necessitating running the model on multiple spectral ranges and using increased sample sizes in subsequent applications. This proposed method has the potential to provide rapid and accurate results and may be valuable in future applications regarding toxin detection in foods when simple onsite testing is required.
摘要：
黄曲霉毒素B1是全球关注的主要食品安全问题,是由作物生长过程中的产毒真菌产生的，干燥,和存储,并显示出每年的患病率在增加。本研究旨在使用ATR-FTIR结合机器学习算法检测辣椒样品中的黄曲霉毒素B1。我们发现83.6%的辣椒粉样品被曲霉和青霉菌污染,黄曲霉毒素B1的水平范围为7.63至44.32μg/kg。指纹区（1800-400cm-1）的ATR-FTIR光谱显示，在1587、1393和1038cm-1的条带中，峰强度变化主要与黄曲霉毒素B1结构有关。无法分离具有不同痕量黄曲霉毒素B1的样品的PCA图。振动光谱学结合机器学习被应用于解决这个问题。logistic回归模型的F1评分最好，准确率最高（73%），%灵敏度（73%），和%特异性(71%)，其次是随机森林和支持向量机模型。尽管逻辑回归模型贡献了重要的发现，这项研究代表了一个实验室研究项目。由于ATR-FTIR光谱测量的特殊性,几批测量的光谱可能不同，需要在多个光谱范围上运行模型，并在后续应用中使用增加的样本大小。当需要简单的现场测试时，这种提出的方法具有提供快速准确结果的潜力，并且在有关食品中毒素检测的未来应用中可能很有价值。
