应用高光谱成像技术, 基于光谱主成分信息和图像信息的融合实现名优绿茶不同品牌的鉴别。 首先采集6个品牌名优绿茶在380～1 023 nm波长范围的512幅光谱图像, 然后提取并分析绿茶样本的可见近红外光谱响应特性, 结合主成分分析法找到了最能体现这6类样本差异的2个特征波段(545和611 nm), 并从这2个特征波段图像中分别提取12个灰度共生矩阵纹理特征参量包括中值、 协方差、 同质性、 能量、 对比度、 相关、 熵、 逆差距、 反差、 差异性、 二阶距和自相关,最后融合这12个纹理特征和三个主成分特征变量得到名优绿茶品牌识别的特征信息, 利用LS-SVM建立区分模型, 预测集识别率达到了100%, 同时采用ROC曲线的评估方法来评估分类模型。 结果表明综合应用灰度共生矩阵变量和光谱主成分变量作为LS-SVM模型输入可实现对绿茶品牌的鉴别。

Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA information and image information fusion. First 512 spectral images of six brands of famous green tea in the 380～1 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system. Then, 12 gray level co-occurrence matrix (GLCM) features (i.e., mean, covariance, homogeneity, energy, contrast, correlation, entropy, inverse gap, contrast, difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image. Finally, integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM. Experimental results showed that discriminating rate was 100% in the prediction set. The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm. Overall results sufficiently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea.