Department of Chemistry, University of Isfahan, Isfahan 81746-73441, Iran.
Department of Chemistry, University of Isfahan, Isfahan 81746-73441, Iran.
J Chromatogr A. 2014 Jan 24;1326:63-72. doi: 10.1016/j.chroma.2013.12.045. Epub 2013 Dec 22.
In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples.
在这项研究中，提出了多元曲线分辨（MCR）和多元分类方法，以开发一种新的化学计量学策略，用于综合分析来自五个不同地理区域的 60 个丹参样本的高效液相色谱-二极管阵列吸收检测（HPLC-DAD）指纹。在 HPLC-DAD 分析丹参样品时，出现了不同的色谱问题，如基线/背景贡献和噪声、低信噪比（S/N）、不对称峰、洗脱时间偏移和峰重叠等，本研究提出的策略可以解决这些问题。通过这种方式，使用局部秩分析将 60 个样品的色谱指纹适当分段为十个常见的色谱区域，然后对相应的片段进行列增强，以便后续的 MCR 分析。扩展多元曲线分辨交替最小二乘法（MCR-ALS）用于获得每个片段中的纯组分轮廓。通常，在 60 个丹参样品中，使用 MCR-ALS 解析了 31 种化学物质，并且在所有情况下，MCR-ALS 模型的失拟（LOF）值均低于 10.0%。通过将解析得到的光谱与标准光谱进行比较，确定了纯光谱图谱用于鉴定化学物质，31 种化学物质中有 24 种得到了鉴定。此外，使用纯洗脱图谱通过主成分分析（PCA）和 k-最近邻（kNN）获得不同样品中化学物质的相对浓度，进行多元分类分析。检查 PCA 得分图（解释了三个 PC 解释的方差的 76.1%）表明，丹参样品属于四个聚类。计算了四个聚类的聚类分离度（DCS），它量化了聚类之间的距离与每个聚类内部的分散程度之间的关系，其范围在 1.6-5.8 之间。kNN 也证实了这些结果。此外，根据 31 个变量的 PCA 加载图和 kNN 树状图，鉴定出木樨草素-7-O-葡萄糖苷、丹酚酸 D、迷迭香酸、虎杖苷和三萜酮 A 等五种化学成分为区分聚类的最重要变量（即化学标志物）。最后，使用对传人工神经网络（CP-ANN）方法研究了不同化学标志物对样品分化的影响。结果表明，所提出的策略可以成功应用于复杂天然样品的色谱指纹综合分析。
