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Selecting Key Wavelengths of Hyperspectral imagine for Nondestructive Classification of Moldy Peanuts using Ensemble Classifier
Infrared Physics & Technology ( IF 3.1 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.infrared.2020.103518
Deshuai Yuan , Jinbao Jiang , Xiaotong Qi , Zilin Xie , Guangmei Zhang

Abstract Peanuts can become moldy and produce aflatoxins if transported and stored under improper conditions. Detecting aflatoxin rapidly, non-destructively, and in real-time is important for practical application. This study aimed to identify moldy peanuts by using a small number of key wavelength bands and Ensemble Classifier (EC) based on hyperspectral images. In order to simulate the natural process of fungal infection, the peanuts of three varieties were used to grow mold, and the detailed hyperspectral images of healthy and moldy peanuts (two different degrees) were captured in the 960-2568nm range. A combination of genetic algorithm and successive projection algorithm was used to select key wavelengths based on the acquired hyperspectral image of peanuts. Following this, an EC consisting of support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), and cluster independent soft pattern classification Classifier (SIMCA) was used to identify healthy and moldy peanuts based on the selected key wavelengths (982nm, 1180nm, 1405nm, 1540nm, 1871nm, 1938nm, 1999nm). The pixel-wise overall classification accuracy of EC, SVM, PLS-DA, and SIMCA were 97.66%, 97.53%, 95.31%, and 97.36%, respectively. Finally, the kernel-scale classification maps showed the distribution of moldy peanuts amongst the healthy peanuts; this suggests that NIR-HSI is a reliable analytical method for the prediction of moldy peanuts. The overall results support the feasibility of establishing a fast, cost-effective, online multispectral imaging system using a small number of key wavelengths and EC to identify moldy peanuts.

中文翻译:

使用集成分类器选择用于霉变花生无损分类的高光谱图像关键波长

摘要 如果运输和储存条件不当,花生会发霉并产生黄曲霉毒素。快速、无损和实时检测黄曲霉毒素对于实际应用非常重要。本研究旨在通过使用少量关键波段和基于高光谱图像的集成分类器 (EC) 来识别发霉的花生。为了模拟真菌感染的自然过程,采用三个品种的花生进行霉菌生长,在960-2568nm范围内拍摄健康和发霉花生(两种不同程度)的详细高光谱图像。基于获取的花生高光谱图像,采用遗传算法和逐次投影算法相结合的方法选择关键波长。在此之后,一个由支持向量机 (SVM) 组成的 EC,使用偏最小二乘判别分析(PLS-DA)和聚类独立软模式分类分类器(SIMCA)根据选定的关键波长(982nm、1180nm、1405nm、1540nm、1871nm、1938nm、1999nm)识别健康和发霉的花生. EC、SVM、PLS-DA 和 SIMCA 的像素级整体分类精度分别为 97.66%、97.53%、95.31% 和 97.36%。最后,仁尺度分类图显示了发霉花生在健康花生中的分布;这表明 NIR-HSI 是预测发霉花生的可靠分析方法。总体结果支持建立使用少量关键波长和 EC 来识别发霉花生的快速、经济高效的在线多光谱成像系统的可行性。使用聚类无关软模式分类分类器(SIMCA)根据选定的关键波长(982nm、1180nm、1405nm、1540nm、1871nm、1938nm、1999nm)识别健康和发霉的花生。EC、SVM、PLS-DA 和 SIMCA 的像素级整体分类精度分别为 97.66%、97.53%、95.31% 和 97.36%。最后,仁尺度分类图显示了发霉花生在健康花生中的分布;这表明 NIR-HSI 是预测发霉花生的可靠分析方法。总体结果支持建立使用少量关键波长和 EC 来识别发霉花生的快速、经济高效的在线多光谱成像系统的可行性。使用聚类无关软模式分类分类器(SIMCA)根据选定的关键波长(982nm、1180nm、1405nm、1540nm、1871nm、1938nm、1999nm)识别健康和发霉的花生。EC、SVM、PLS-DA 和 SIMCA 的像素级整体分类精度分别为 97.66%、97.53%、95.31% 和 97.36%。最后,仁尺度分类图显示了发霉花生在健康花生中的分布;这表明 NIR-HSI 是预测发霉花生的可靠分析方法。总体结果支持建立使用少量关键波长和 EC 来识别发霉花生的快速、经济高效的在线多光谱成像系统的可行性。
更新日期:2020-12-01
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