The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product–polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency.

中文翻译：

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使用新型集成光密度反相高效薄层色谱法区分不同埃及橄榄品种的无监督模式识别化学计量学—图像分析技术

在这项研究中，实现了化学计量学基于不同成分的埃及橄榄化学分类的优点。在天然产物-聚乙二醇（NP / PEG）试剂和图像色谱分离后的衍生化前后，使用反相高效薄层色谱（RP-HPTLC）建立了在埃及种植的9种不同橄榄叶品种的指纹图谱使用ImageJ®软件进行分析，以构建2个独立的数据矩阵。使用主成分分析（PCA）和层次聚类分析（HCA）算法分别对色谱指纹图谱进行无监督模式识别多变量分析，以建立2个单独的模型，以探索不同化学特征的分布模式。色谱分析后衍生化后涉及橄榄样品指纹的第二种模型表现出更大的能力，能够以更高的灵敏度揭示更宽范围的植物成分。通过新开发和验证的方法，使用Sorbfil TLCVideodensitometer®将橄榄苦苷标记物的光密度RP-HPTLC定量与图像分析方法进行了比较。密度测定法比图像分析法表现出更好的性能特征，因此可用于测定9种埃及橄榄中橄榄苦苷的浓度。发现仅橄榄苦甙标记不足以标准化橄榄叶品种。这项研究展示了一种用于快速分类埃及不同橄榄品种的综合方法，这对于保证其化学一致性至关重要，并且