Abstract

Peanuts are easily contaminated by a variety of mycotoxins during growth, transportation, and storage, of which aflatoxin B1 is the most common. Aflatoxin B1 is one of the most toxic carcinogens known, and it can cause liver damage to varying degrees after ingestion. To explore the feasibility of detecting aflatoxin B1 contamination in peanuts by near-infrared spectroscopy, 115 peanut samples with aflatoxin B1 content in the range of 2.44 to 223.76 μg/kg were prepared. The near-infrared spectroscopy data of the peanut sample in the 940–1660 nm band was obtained, and the naive Bayes qualitative discrimination model based on the whole band was established. To improve accuracy and reduce dimensions, simplified models were built using characteristic wavelengths screened by Successive Projection Algorithm and Elimination of Uninformative Variables. The built model is verified internally and externally by omitting cross-validation. In comparison, the second derivative Savitzky–Golay Elimination of Uninformative Variables normal kernel density estimation naive Bayes model reached the optimum discriminant accuracy, with the comprehensive overall accuracy of validation set and prediction set were over 91.00%, areas under receiver operating characteristic curves was over 0.90. The results showed that the accuracy of the quantitative determination of aflatoxin B1 in peanuts by near-infrared spectroscopy was high, with a boundary of 20 μg/kg. This method is suitable for the qualitative determination of peanut aflatoxin B1.

摘要

花生在生长、运输和贮藏过程中极易受到多种霉菌毒素的污染，其中以黄曲霉毒素B1最为常见。黄曲霉毒素B1是已知毒性最强的致癌物之一，摄入后可造成不同程度的肝脏损害。为探讨近红外光谱法检测花生中黄曲霉毒素B1污染的可行性，制备了115份黄曲霉毒素B1含量在2.44～223.76 μg/kg范围内的花生样品。获得花生样品在940-1660 nm波段的近红外光谱数据，建立了基于全波段的朴素贝叶斯定性判别模型。为了提高精度并减少尺寸，使用逐次投影算法和消除无信息变量筛选的特征波长构建了简化模型。构建的模型通过省略交叉验证在内部和外部进行验证。相比之下，二阶导数Savitzky-Golay Elimination of Uninformative Variables正态核密度估计朴素贝叶斯模型达到了最佳判别精度，验证集和预测集的综合总体精度超过91.00%，接收器操作特征曲线下面积超过0.90。结果表明，近红外光谱法定量测定花生中黄曲霉毒素B1的准确度较高，界限值为20 μg/kg。本方法适用于花生黄曲霉毒素B1的定性测定。二阶导数Savitzky-Golay Elimination of Uninformative Variables正态核密度估计朴素贝叶斯模型达到了最优判别精度，验证集和预测集的综合综合精度超过91.00%，受试者工作特征曲线下面积超过0.90。结果表明，近红外光谱法定量测定花生中黄曲霉毒素B1的准确度较高，界限值为20 μg/kg。本方法适用于花生黄曲霉毒素B1的定性测定。二阶导数Savitzky-Golay Elimination of Uninformative Variables正态核密度估计朴素贝叶斯模型达到了最优判别精度，验证集和预测集的综合综合精度超过91.00%，受试者工作特征曲线下面积超过0.90。结果表明，近红外光谱法定量测定花生中黄曲霉毒素B1的准确度较高，界限值为20 μg/kg。本方法适用于花生黄曲霉毒素B1的定性测定。结果表明，近红外光谱法定量测定花生中黄曲霉毒素B1的准确度较高，界限值为20 μg/kg。本方法适用于花生黄曲霉毒素B1的定性测定。结果表明，近红外光谱法定量测定花生中黄曲霉毒素B1的准确度较高，界限值为20 μg/kg。本方法适用于花生黄曲霉毒素B1的定性测定。