FTIR结合统计分析对竹类植物的鉴别分类研究

傅里叶变换红外光谱结合主成分分析和系统聚类分析用于竹类植物鉴别分类研究。六种竹亚科植物54个竹子叶片的红外光谱测试结果显示竹叶光谱主要由蛋白质、碳水化合物、脂类等吸收带组成，竹叶光谱相似，仅在1 800～700 cm-1范围峰数、峰位、峰强上存在较小的差异。六种竹子叶片红外光谱的二阶导数谱在1 800～700 cm-1范围显示明显差异。用1 800～700 cm-1范围二阶导数光谱进行主成分分析，在主成分PC1，PC2，PC3三维空间图中，所测试竹叶样本分类正确率达98%;在PC3-PC4二维投影图显示所有竹叶样本正确分成六个区域;用1 800～700 cm-1范围二阶导数光谱进行聚类分析，所测竹叶样本正确聚为六类。表明FTIR结合统计分析能够在种水平对竹亚科植物鉴别分类。

Discrimination of Bamboo Using FTIR Spectroscopy and Statistical Analysis

Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to identify and classify bamboo leaves. FTIR spectra of fifty-four bamboo leaf samples belonging to six species were obtained. The results showed that the infrared spectra of bamboo leaves were similar, and mainly composed of the bands of polysaccharides, protein and lipids. The original spectra exhibit minor differences in the region of 1 800~700cm-1. The second derivative spectra show apparent differences in the same region. Principal component analysis and hierarchical cluster analysis were performed on the second derivative infrared spectra in the range from 1 800 to 700 cm-1. The leaf samples were separated into 6 groups with accuracy of 98% with the first three principal components, and with 100% accuracy according to the third and fourth principal components. Hierarchical cluster analysis can correctly cluster the bamboo leaf samples. It is proved that Fourier transform infrared spectroscopy combined with PCA and HCA could be used to discriminate bamboo at species level with only a tiny leaf sample.