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Automated tea quality classification by hyperspectral imaging

机译：通过高光谱成像对茶叶质量进行自动分类

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A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98percent and 95percent in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.
机译：尝试使用高光谱成像技术对绿茶进行分类。尝试了五个等级的绿茶样品。开发了高光谱成像系统，用于茶样品的数据采集。对高光谱数据进行主成分分析，以确定三个最佳波段图像。对每个最佳波段图像进行纹理分析，以提取特征变量。支持向量机（SVM）用于构建分类模型。在训练和预测集中的分类率分别为98％和95％。与其他模式识别分类器相比，SVM算法在分类结果中显示出出色的性能。总体结果表明，结合SVM分类器的高光谱成像技术可以有效地对绿茶进行分类。

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