Department of Chemistry, Babeş-Bolyai University, Faculty of Chemistry and Chemical Engineering, Arany János, No. 11, Cluj-Napoca, Romania.
Department of Chemistry, Babeş-Bolyai University, Faculty of Chemistry and Chemical Engineering, Arany János, No. 11, Cluj-Napoca, Romania.
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Apr 30;1170:122590. doi: 10.1016/j.jchromb.2021.122590. Epub 2021 Feb 21.
A chemometric evaluation of the information provided by different color scale fingerprints in thin layer chromatographic analysis of complex samples is proposed for the correct classification of a set of medicinal plant extracts. The fingerprints of the samples were acquired on HPTLC Silica gel 60 F and HPTLC Silica gel 60 plates using multiple levels of visualization under UV light. Images processing on red (R), green (G), blue (B) and respectively grey (K) color scale selection was used in order to evaluate the complete chromatographic profile of the extracts. Combination of Principal Component Analysis (PCA) and Factor Analysis (FA) method was applied in order to reveal the individual contribution of each color scales in the analysis of chromatographic fingerprints. The suggested technique provides an applicable strategy to screen for efficacy-associated color scale for grouping/classification of the extracts exploiting the information provided by HPTLC fingerprints. The principal component analysis and linear discriminant analysis (PCA-LDA) method was applied for the evaluation of numerical data provided by color scale fingerprints digitization and for samples classification. A correct classification of the analyzed extracts according to the plants phylum was revealed by color scale fingerprints analysis. The proposed methodology could be considered as a promising tool with future applications in plant material investigations even from the taxonomic perspective classification.
提出了一种化学计量学评价方法，用于评估复杂样品薄层色谱分析中不同颜色比例尺指纹图谱提供的信息，以正确分类一组药用植物提取物。使用不同的颜色比例尺（R、G、B 和 K）对 HPTLC 硅胶 60 F 和 HPTLC 硅胶 60 板上的样品指纹进行了多次可视化水平的采集。对颜色比例尺的选择进行图像处理，以评估提取物的完整色谱轮廓。应用主成分分析（PCA）和因子分析（FA）方法组合，以揭示每种颜色比例尺在色谱指纹分析中的个体贡献。该技术提供了一种可行的策略，用于筛选与功效相关的颜色比例尺，以利用 HPTLC 指纹图谱提供的信息对提取物进行分组/分类。主成分分析和线性判别分析（PCA-LDA）方法用于评估颜色比例尺指纹数字化提供的数值数据和样品分类。通过颜色比例尺指纹图谱分析，根据植物门对分析提取物进行了正确的分类。该方法可以被视为一种有前途的工具，甚至可以从分类学的角度应用于植物材料的研究。
