Improving Reproducibility of HPTLC Analysis for Cranberry Supplements Through Digitization and Chemometric Preprocessing

Résumé fourni par la source

BACKGROUND: High-performance thin-layer chromatography (HPTLC) is widely used for the identification and quality assessment of botanical supplements. However, traditional interpretation methods are subjective, and variability between plates hinders reproducibility and inter-plate comparisons. OBJECTIVE: This study aimed to enhance the reproducibility and analytical utility of HPTLC by digitizing chromatograms and applying chemometric preprocessing to cranberry dietary supplement analysis. METHOD: Cranberry supplements of diverse dosage forms were extracted and analyzed using a standardized HPTLC protocol. Plates were derivatized with natural products and anisaldehyde reagents and imaged under multiple lighting conditions. Digital chromatograms were processed using normalization and retention factor (RF) alignment. Chemometric methods, including principal component analysis (PCA) and analysis of variance principal component analysis (ANOVA-PCA), were applied to assess variability and improve classification. RESULTS: The digitization and preprocessing workflow significantly reduced plate-related variability while enhancing classification accuracy. RF alignment lowered between-plate variance from 23 to 11%, while increasing sample-type variance from 59 to 79%. Combining data from multiple derivatization and imaging conditions improved chemical fingerprinting and enabled tighter clustering in PCA models. CONCLUSIONS: The integration of digitized HPTLC data with chemometric preprocessing modernizes the analytical workflow, improves reproducibility, and enables more robust and interpretable botanical fingerprinting. This approach supports improved quality control of botanical products and aligns with emerging standards for data transparency and reusability. HIGHLIGHTS: Digitization and alignment reduce HPTLC variability and enhance reproducibility. Combined profiles from multiple derivatization conditions improve sample classification. Chemometric analysis enables better interpretation and data-driven quality control and assessment for botanicals.

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