Evaluation of Apium graveolens from different geographical origins based on TLC-fingerprint and chemometrics

Apium graveolens (celery) has various roles both in the food and medicine sectors. It grows very well in the tropical and subtropical areas of Africa and Asia, including Indonesia. This Apiaceae member contains a number of phytoconstituents, and geographical origin is known to significantly determin...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2021-11, Vol.913 (1), p.12062
Main Authors: Kartini, K, Jannah, M, Wulandari, F, Oktaviyanti, N D, Setiawan, F, Jayani, N I E
Format: Article
Language:English
Subjects:
Apium graveolens
Celery
Chemometrics
Cluster analysis
Design optimization
Dichloromethane
Evaluation
Fingerprinting
Fingerprints
Geographical distribution
geographical origins
herbal quality
Ions
Origins
Principal Components Analysis
Propanol
Thin layer chromatography
Toluene
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Summary:Apium graveolens (celery) has various roles both in the food and medicine sectors. It grows very well in the tropical and subtropical areas of Africa and Asia, including Indonesia. This Apiaceae member contains a number of phytoconstituents, and geographical origin is known to significantly determine the type and concentration of phytochemicals in plant material. This study was carried out to validate and develop thin layer chromatography (TLC)-based fingerprinting combined with chemometrics, i.e., Principal Component Analysis (PCA) and Cluster Analysis (CA), to evaluate the quality of celery harvested from thirteen different geographical origins in Indonesia. The mobile phase was first optimized with a simplex axial design, resulting in 2-propanol, toluene, and dichloromethane (1:6:1) as the optimum mobile phase for a stable and precise TLC system in the celery sample analysis. When analyzed with chemometrics, the TLC-fingerprints could discriminate celeries from various origins. The PCA score plot of the first two principal components (PCs) and CA clearly distinguished the samples’ properties and classified them into four clusters. Samples grouped into one cluster were concluded to have comparable quality, while those in different clusters had different qualities.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/913/1/012062