Document Type
Oral Presentation
Location
Oxford Conference Center, Oxford MS
Event Website
https://oxfordicsb.org/
Start Date
7-4-2025 1:00 PM
Description
European Elder berry, Sambucus nigra L. has a long history of traditional use as a cold and flu remedy. Since COVID-19 pandemic, the increased demand and limited supply have increased adulteration risk. Major concerns related to adulteration are substitution of Elder berry with black rice extract, addition of synthetic dyes and species mislabeling. HPTLC analysis of a total of 167 samples from different types of ingredients marketed as containing S. nigra was conducted, including whole fruits, juice concentrates, dry juices, fruit powders, liquid extracts, aqueous extracts and Dietary Supplements (DS) products (capsules and syrups). Related species (S. cerulea, S. canadensis, S. ebulus and S. rubra) and confounding anthocyanin sources were also analyzed, including black rice extract as the main adulterant of Elder berry ingredients. The recently modernized HPTLC method of the USP European Elder berry Dry Extract monograph published in PF 49(3), was applied following USP <203> HPTLC for Article of Botanical Origin. HPTLC images and peak profiles from images (PPI) were used to generate detailed fingerprints for various sample types. Advanced AI-driven techniques, dimensionality reduction methods such as t-SNE, were employed to simplify the data structure while preserving key features. Clustering algorithms were then applied to the processed data, revealing hidden patterns and groupings among sample types that corroborated the initial visual interpretations of the HPTLC chromatograms. This presentation will demonstrate the powerful synergy between HPTLC and AI-driven data analysis, showcasing how these tools can enhance species differentiation, detect adulteration, and distinguish cofounding material from diverse anthocyanin sources. By integrating cutting-edge AI methodologies, HPTLC analysis reaches new heights in precision and insight, paving the way for innovative advancements in botanical quality assessment.
Recommended Citation
Monagas, Maria; Perera, Wilmer; Upton, Roy; Reich, Eike; and Do, Tiên, "Innovative Integration of HPTLC and AI for Detecting Adulteration and Ensuring Quality in European Elder berry Products" (2025). Oxford ICSB. 5.
https://egrove.olemiss.edu/icsb/2025_ICSB/Schedule/5
Publication Date
April 2025
Accessibility Status
Screen reader accessible, Searchable text
Included in
Innovative Integration of HPTLC and AI for Detecting Adulteration and Ensuring Quality in European Elder berry Products
Oxford Conference Center, Oxford MS
European Elder berry, Sambucus nigra L. has a long history of traditional use as a cold and flu remedy. Since COVID-19 pandemic, the increased demand and limited supply have increased adulteration risk. Major concerns related to adulteration are substitution of Elder berry with black rice extract, addition of synthetic dyes and species mislabeling. HPTLC analysis of a total of 167 samples from different types of ingredients marketed as containing S. nigra was conducted, including whole fruits, juice concentrates, dry juices, fruit powders, liquid extracts, aqueous extracts and Dietary Supplements (DS) products (capsules and syrups). Related species (S. cerulea, S. canadensis, S. ebulus and S. rubra) and confounding anthocyanin sources were also analyzed, including black rice extract as the main adulterant of Elder berry ingredients. The recently modernized HPTLC method of the USP European Elder berry Dry Extract monograph published in PF 49(3), was applied following USP <203> HPTLC for Article of Botanical Origin. HPTLC images and peak profiles from images (PPI) were used to generate detailed fingerprints for various sample types. Advanced AI-driven techniques, dimensionality reduction methods such as t-SNE, were employed to simplify the data structure while preserving key features. Clustering algorithms were then applied to the processed data, revealing hidden patterns and groupings among sample types that corroborated the initial visual interpretations of the HPTLC chromatograms. This presentation will demonstrate the powerful synergy between HPTLC and AI-driven data analysis, showcasing how these tools can enhance species differentiation, detect adulteration, and distinguish cofounding material from diverse anthocyanin sources. By integrating cutting-edge AI methodologies, HPTLC analysis reaches new heights in precision and insight, paving the way for innovative advancements in botanical quality assessment.
https://egrove.olemiss.edu/icsb/2025_ICSB/Schedule/5