Shen Yun-xia, Zhao Yan-li, Zhang Ji, Jing Hang, Wang Yuan-zhong
Guang Pu Xue Yu Guang Pu Fen Xi. 2016 May;36(5):1358-62.
The harvest time of traditional Chinese medicine (TCM) is a very essential part for the production and quality of TCM which is the prerequisite for safe and effective clinical use of TCM. It is of great importance to carry out the research of timely harvest time of TCM. With Fourier transform infrared spectroscopy (FTIR) to study harvest time of Seventy-two Gentiana Rigescens samples. First derivative, second derivative, standard normal variate, multiplicative scatter correction and Savitaky-Golay(15,3) smoothing of all original spectra were pretreated with TQ8.0 software. Samples were divided into calibration set and prediction set at the ratio of 3∶1. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) model were established. The result indicated that after removing noise spectrum, the spectra range was from 1 800 to 600 cm-1, the method SNV combined with SD and SG present the best result of spectra pretreatment. The contribution rates of first three principal components were 92.47% with PCA. Small differences were found for the samples harvested in May, September and October. Same spectrum range was chosen and PLS-DA was applied to establish the model. The R2 and RMSEE were 0.967 8, 0.086 0, respectively, and the prediction accuracy is 100%. The methods of PCA and PLS-DA have good ability to classify and identify different harvest time of Gentiana Rigescens. It provided a basis for the identification of different harvest time of TCM.
中药的采收时间是中药生产和质量的非常重要的部分，这是中药安全有效临床应用的前提。开展中药适时采收时间的研究具有重要意义。采用傅里叶变换红外光谱法（FTIR）研究72个滇龙胆样本的采收时间。用TQ8.0软件对所有原始光谱进行一阶导数、二阶导数、标准正态变量变换、多元散射校正和Savitzky-Golay（15,3）平滑预处理。样本按3∶1的比例分为校正集和预测集。建立主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）模型。结果表明，去除噪声光谱后，光谱范围为1800～600 cm-1，SNV结合SD和SG的方法呈现出最佳的光谱预处理效果。PCA中前三个主成分的贡献率为92.47%。5月、9月和10月采收的样本差异较小。选择相同的光谱范围并应用PLS-DA建立模型。R2和RMSEE分别为0.967 8、0.086 0，预测准确率为100%。PCA和PLS-DA方法对滇龙胆不同采收时间具有良好的分类和识别能力。为中药不同采收时间的鉴别提供了依据。
