穿山甲色谱指纹谱的建立及其在基源鉴别和质量等级区分方面的应用

中药穿山甲为鳞鲤科动物穿山甲的鳞甲,常用于治疗乳汁不通、痈肿疮毒、风湿痹痛等症,饮片来源上经常存在基源不清、等级不明的问题。针对以上问题,目前已发展出了多种分析方法,包括薄层色谱、质谱和DNA检测,但由于特异性低、设备成本高、步骤繁琐等不足,以上方法均不适合作为穿山甲质量监测的常规手段,而色谱指纹谱技术恰好可以弥补以上不足。该文旨在建立穿山甲的色谱指纹谱,并探讨其在等级及基源鉴别方面的可行性。穿山甲粉末经1 mol/L HCl加热水解得供试液,色谱柱采用Waters Symmetry 300 C18,以0.1%(v/v)三氟乙酸/水、0.1%(v/v)三氟乙酸/乙腈为流动相,280 nm为检测波长,采用梯度洗脱获得穿山甲指纹谱。经考察,该方法的精密度、日内日间重复性及样品稳定性均表现良好(RSD<5%)。以12批中华穿山甲一等品的平均指纹谱为对照指纹谱,同时确认17个共有峰,继而以共有峰绝对峰面积为原始数据,计算样品指纹谱与对照指纹谱的相似度。结果表明,中华穿山甲与其他动物来源穿山甲的相似度不高于0.776,体现了相似度评价在基源鉴别上的有效性;但不同等级穿山甲之间交叉严重,表明相似度评价在等级鉴别上的局限性。以24批不同等级中华穿山甲为分析对象建立判别分析模型,经十折交叉验证,模型无偏差正确率为95.83%,说明该模型在等级区分上可行性很高。该文通过建立穿山甲的色谱指纹谱,并结合不同数据处理方法验证了其在基源鉴别和等级区分方面的可行性,为保证穿山甲临床用药的科学性、准确性提供新的思路。

Establishment of chromatographic fingerprint of Squama Manis and its applications in animal source identification and quality grade discrimination

Squama Manis, or “Chuanshanjia” in Chinese, is a traditional Chinese medicine (TCM) for promoting blood circulation and reducing swelling and discharge; the only animal source used in TCM is the scales of Manis pentadactyla. However, in today’s pharmaceutical market, there are many scales from other species of the same genus that are difficult to distinguish from Squama Manis. High-quality and low-quality scales are also severely confused. To solve the above problems, various analytical methods have been developed, such as thin-layer chromatography, mass spectrometry and DNA detection. Owing to their low resolving ability, high equipment cost, and inconvenient operation, none of these methods are appropriate for routine identification of Squama Manis. A chromatographic fingerprint can comprehensively reflect the synergic action of multiple chemical compositions in TCM and has been widely used for the quality control of TCM. In the present study, we established a fingerprint of Squama Manis and explored its feasibility in identifying the origin and quality grade of scales. First, Squama Manis powder was hydrolyzed by hydrochloric acid (1 mol/L). Next, the extract was analyzed on a Symmetry 300 C18 column by linear gradient elution, using 0.1% trifluoroacetic acid (v/v) in water and 0.1% trifluoroacetic acid (v/v) in acetonitrile as the mobile phase and 280 nm as the detection wavelength. The established method was systematically validated, demonstrating good precision, repeatability and sample stability (relative standard deviation (RSD)<5%). Subsequently, samples of different sources and quality grades were distinguished by similarity evaluation and discrimination analysis based on the fingerprint data. In the similarity evaluation, the reference fingerprint was defined as the average fingerprint of twelve first-class samples, and seventeen chromatographic peaks were identified as common peaks. Similarities between the reference fingerprint and fingerprints with different base sources and quality grades were calculated using the absolute area of common peaks as original data. The similarities between Squama Manis and scales from other animals were all less than 0.776, while the similarities between Squama Manis of different grades overlapped significantly, varying from 0.988 to 0.996 for first-class samples and 0.950 to 0.995 for general samples. The results reflected the feasibility of similarity evaluation for discriminating base source and its limitation in the distinguishing between quality grades. Nonetheless, first-class scales showed higher average similarity and lower RSD than general scales, which indicates some level of revelation between fingerprint similarity and quality grade. Thus, a better algorithm or discriminant model is required to distinguish between quality grades. Therefore, a supervised chemometric technique, kernel-based support vector machine (SVM), was applied to construct predictive models. The SVM is a common discriminant model that classifies samples by constructing a separate hyperplane in n-dimensional space, maximizing the margin between classes. Combination with a kernel function can effectively avoid “dimension disaster” when dealing with nonlinear data. In the model, the quality grade was defined as a sample label, and the absolute peak areas constituted the data matrix. Verified by 10-fold cross-validation, the unbiased prediction accuracy was up to 95.83%. The predicted results were highly consistent with the actual classifications. The results indicate the high feasibility of the established model for determining quality grade, as it performed significantly better than the similarity evaluation. Samples from batches A and B were completely discriminated and only two samples from batch S were incorrectly classified. Given the batch bias, we believe that model error may have been caused by man-made tag errors rather than the model itself. In conclusion, we established a chromatographic fingerprint for Squama Manis quality analysis and demonstrated its feasibility in animal source identification and quality determination by combining different data analysis methods. The established strategy may provide a new method for improving the the validity and accuracy of Squama Manis in clinical use.