Abstract

Moldy peanuts may contain aflatoxin, a highly carcinogenic substance that threatens the health of humans and livestock. This study aimed to identify moldy peanuts using hyperspectral measurements and continuous wavelet transform (CWT). Peanuts were allowed to develop mold in a simulation of natural process of fungal infection; detailed hyperspectral images of healthy and moldy peanuts were captured. Based on these spectral data, CWT with separability analysis was conducted, generating a Jeffries–Matusita distance scalogram that summarized the separability of the wavelet power at different wavelengths and the decomposition scales between healthy and moldy peanuts. Using thresholding, five wavelet features (WFs) were isolated to identify moldy peanuts. In addition, seven optimal bands obtained from a successive projection algorithm were compared with the WFs. Partial least squares discrimination analysis (PLS-DA) and support vector machines (SVM) were adopted as classifiers for evaluating the WFs and optimal bands. The results show that according to the WFs, both PLS-DA and SVMs achieved higher overall classification results (at least 96.19% for the test data) than those using optimal bands selected via the successive projection algorithm (SPA). The CWT was found to be a promising method for analyzing the fungal infection of peanuts.

中文翻译：

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利用从高光谱图像中提取的小波光谱特征识别发霉的花生仁

摘要

发霉的花生可能含有黄曲霉毒素，黄曲霉毒素是一种高度致癌的物质，威胁着人类和牲畜的健康。这项研究旨在使用高光谱测量和连续小波变换（CWT）来识别发霉的花生。花生被允许在模拟真菌感染的自然过程中发展霉菌。捕获了健康和发霉的花生的详细高光谱图像。基于这些光谱数据，进行了CWT的可分离性分析，生成了Jeffries–Matusita距离比例尺图，总结了小波功率在不同波长下的可分离性以及健康和发霉的花生之间的分解规模。使用阈值处理，分离了五个小波特征（WFs）以识别发霉的花生。此外，从连续投影算法获得的七个最佳波段与WF进行了比较。采用偏最小二乘判别分析（PLS-DA）和支持向量机（SVM）作为评估WF和最佳频带的分类器。结果表明，根据WF，与使用通过连续投影算法（SPA）选择的最佳频段的分类结果相比，PLS-DA和SVM均获得了更高的总体分类结果（测试数据至少为96.19％）。发现CWT是分析花生真菌感染的一种有前途的方法。与使用通过连续投影算法（SPA）选择的最佳频段的分类结果相比，PLS-DA和SVM均获得了更高的总体分类结果（测试数据至少为96.19％）。发现CWT是分析花生真菌感染的一种有前途的方法。与使用通过连续投影算法（SPA）选择的最佳频段的分类结果相比，PLS-DA和SVM均获得了更高的总体分类结果（测试数据至少为96.19％）。发现CWT是分析花生真菌感染的一种有前途的方法。