中药炮制是根据中医学理论, 改变中药的性味和功效, 以达到缓和药性、 减毒增效等作用。 炮制对中药的活性成分、 药效、 毒副作用影响甚大, 建立一个系统鉴别和评价中药不同炮制品的方法, 可为中药质量和临床用药安全提供重要支撑。 采用红外光谱法对9种云南重楼不同炮制品进行对比分析, 结合化学计量学建立主成分-马氏距离(PCA-MD)判别模型进行鉴别分析。 云南重楼不同炮制品的红外光谱经自动基线校正和纵坐标归一化预处理后, 取其平均光谱图。 九种重楼不同炮制品的平均红外光谱和二阶导数光谱显示: (1)其主要特征吸收峰为3 387, 2 923, 1 745, 1 463, 1 338, 1 240, 1 207, 1 158, 1 180, 1 080, 1 048, 1 020, 988, 921, 895, 859, 833, 765, 708, 572和529 cm-1； (2)重楼不同炮制品红外图谱的峰形基本相似, 可显示出重楼所特有的红外光谱特征； (3)重楼不同炮制品红外图谱中少数特征吸收峰数目、 位置和吸收强度存在差异, 表明重楼经不同炮制后化学成分和含量发生了改变。 红外光谱经多元散射校正(MSC), 标准正态变量(SNV), 一阶求导(1st Der), 二阶求导(2nd Der)和平滑(SG)优化处理后, 采用Kennard-Stone算法筛选训练集和预测集(3∶1), 建立PCA-MD判别分析模型。 结果显示, 重楼不同炮制品的最佳预处理方法为1st Der+SG(11∶3)。 提取前5个主成分, 变量特征的解释能力为88.2%, 以PC1, PC2和PC3为坐标轴建立PCA-MD三维得分图可知, 九种炮制品可完全区分； 其中重楼I, H, G和F的聚类效果最好, 且前三种炮制品距离较近, 表明晒干和烘干处理重楼与传统炮制重楼所含化学成分相似； 重楼D和E空间距离较近, 推测其经过微波和蒸汽高温处理后化学成分变化相似。 预测集样本可准确的归属于训练集, PCA-MD判别模型的准确率为100%。 红外光谱结合PCA-MD判别分析可准确区分云南重楼的不同炮制品, 为云南重楼炮制品的临床应用提供参考, 同时为中药炮制品的鉴别提供了借鉴。

Based on the theory of Chinese medicine, the processing which improves efficacy, property and flavor of traditional Chinese medicine (TCM) is an effective way to moderate property, enhance therapeutic effect or reduce toxicity of TCM. The processing has significant influence on the chemical components, efficacy and toxicity of TCM. Therefore, it is vital to establish a system method to discriminate and evaluate different processed products of TCM, which can provide an important support for the quality and clinical medication security of TCM. In this paper, Paris polyphylla var. yunnanensis which were processed with nine different methods were conducted comparative analysis by infrared spectroscopy combined with chemometrics, and the principal component analysis-Mahalanobis distance (PCA-MD) discriminant model was established to differentiate them. The original infrared spectra data was preprocessed by automatic baseline correction and ordinate normalization, and the averaged spectra were obtained. The averaged and second derivative spectra showed that: (1) The main characteristic absorption peaks were 3 387, 2 923, 1 745, 1 463, 1 338, 1 240, 1 207, 1 158, 1 180, 1 080, 1 048, 1 020, 988, 921, 895, 859, 833, 765, 708, 572 and 529 cm-1. (2) The peak shape of samples was almost alike, which could exhibit the infrared spectral features of processed P. yunnanensis. (3) Some differences of a few characteristic absorption peaks existed in number, position and absorption intensity, which indicated that the chemical components and content were changed after different processing. The infrared spectra data was pretreated by multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (1st Der), second derivative (2nd Der) and Savitzky-Golay (SG) smoothing. Samples were divided into calibration set and prediction set at the ratio of 3∶1 by Kennard-Stone algorithm. Then, the optimized spectra data were used to establish the discrimination model based on PCA-MD. The results showed that the best spectral pretreatment of PCA-MD model was 1st Der+SG (11∶3). The cumulative accounting was 88.2%, when extracted the first five principal components. The first three principal components were selected for establishing the 3D scattered plot of PCA-DA model. It is obvious that samples with different processed methods could be grouped completely. The clustering result of P. yunnanensis I, H, G and F were better than others, and the first three (I, H and G) were nearer. It indicated that the chemical composition of processing by sun-drying and oven drying were similar to traditional processing method. Additionally, P. yunnanensis D was close to P. yunnanensis E, it conjectured that chemical compositions of processing by microwave drying and steam treatment were similar. The prediction set could accurately conform to the calibration set, and the accuracy of PCA-MD model was 100%. Infrared spectroscopy combined with PCA-MD could distinguish different processed P. yunnanensis accurately. Furthermore, it could provide references for clinical application, discriminating of processed P. yunnanensis as well as other processed TCM.