基于从高光谱图像中提取的小波光谱特征识别发霉花生仁

摘要：发霉的花生可能含有黄曲霉毒素，这是一种高度致癌的物质，威胁着人类和牲畜的健康。本研究旨在使用高光谱 测量和连续小波变换 (CWT) 识别发霉的花生。在模拟真菌感染的自然过程的过程中，让花生长出霉菌；捕捉健康和发 霉花生的详细高光谱图像。基于这些光谱数据，进行了 CWT 和可分离性分析，生成了 Jeffries Matusita 距离尺度图，该 尺度图总结了不同波长下小波功率的可分离性以及健康和发霉花生之间的分解尺度。使用阈值分离出五个小波特征 (WF) 来识别发霉的花生。此外，将从连续投影算法中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) 作为分类器，对 WF 和最佳波段进行评估。结果表明，根据 WF，PLS-DA 和 SVM 都比使 用通过连续投影算法 (SPA) 选择的最佳波段获得更高的整体分类结果（（ 测试数据至少 96.19%）。CWT 被发现是一种分 析花生真菌感染的有前途的方法。

关键词：连续小波变换.真菌污染.偏最小二乘判别分析.花生.支持向量机

期刊：Food Analytical Methods

DOI：https://DOI.org/10.1007/s12161-019-01670-w

设备：GaiaSorter

Moldy Peanut Kernel Identification Using Wavelet Spectral Features Extracted from Hyperspectral Images

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 ina simulation of natural process offungal infection; detailed hyperspectral images of healthy and moldypeanuts werecaptured. Based onthese spectraldata, CWTwithseparabilityanalysiswas conducted, generating a Jeffries Matusita distance scalogram that summarized the separability of the wavelet power at different wavelengths and the decompo sition scales between healthy and moldy peanuts.Using thresholding, five wavelet features(WFs) wereisolatedto 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 CWTwas found to be a promising method for analyzing the fungal infection of peanuts.

Keywords：Continuouswavelettransform .Fungalcontamination .Partialleastsquaresdiscriminationanalysis .Peanuts .Support vector machine