Enhanced Detection of Aflatoxin B1 in Single Peanut Kernels Using Laser-Induced Fluorescence and a Weighted Algorithm

Chenghong Wang, Zhongjun Yan,Fei Shen,Qiuhui Hu, Xirong Huang

Food Control（2025）SCI 1区

Cited 0|Views2

Abstract

Peanuts, a globally significant crop, are prone to aflatoxin B1 (AFB1) contamination, posing a significant threat to food safety. This study employed laser-induced fluorescence spectroscopy (LIFS) to detect AFB1 in single peanuts. Natural contamination conditions were simulated to obtain peanuts with different AFB1 levels, and surface fluorescence signals were collected using single-probe and three-probe methods. Toxin content was quantified through wet chemistry, and machine learning was applied for classification. The results showed that increasing the number of probes significantly improved detection accuracy and reduced the false negative rate (FNR). A weighted algorithm was proposed to enhance spectral analysis, which can amplify the differences between contaminated and uncontaminated samples. A linear SVM based on the three-probe weighted fluorescence spectral data achieved best discriminant ability (accuracy = 100%). Additionally, the Random Forest (RF) algorithm identified six key wavelengths, enabling an SVM classifier to predict contamination with 94.12% accuracy and a 0% FNR. This high-sensitivity, high-accuracy method provides a reliable technical solution for rapid, nondestructive AFB1 detection in peanuts, offering promise for critical applications in food safety monitoring.

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Key words

Peanut,Aflatoxin B1,Fluorescence spectroscopy

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【要点】：本研究利用激光诱导荧光光谱（LIFS）技术和加权算法提高了花生中黄曲霉毒素B1（AFB1）的检测准确性和效率，实现了对单颗花生无损、快速、高灵敏度的AFB1检测。

【方法】：通过激光诱导荧光光谱技术收集花生表面的荧光信号，并结合机器学习分类方法对AFB1进行定性和定量分析。

【实验】：实验模拟了自然污染条件，使用单探针和三探针方法收集不同AFB1含量花生的表面荧光信号，并采用湿化学法量化毒素含量。实验数据通过加权算法处理，最终三探针加权荧光光谱数据基于线性支持向量机（SVM）实现了100%的识别准确率，随机森林（RF）算法识别出六个关键波长，使用SVM分类器预测污染的准确率达到94.12%，且无假阴性率。