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Poster Presentations
Day 2, June 23（Mon.）
Room P (Maesato East, Foyer, Ocean Wing)
- 2P-PM-20
AI-Based Detection of Sesame Oil Adulteration Using Metabolomics and Lipidomics Analysis
(Sogang Univ.)
oSeungwoo Hong, Han Bin Oh
Sesame oil is highly valued for its distinctive scent and flavor, making it a premium product in the market.
However, cases of adulteration with cheaper edible oils such as soybean oil, rapeseed oil, and corn oil continue to emerge.
To address this issue, an objective analytical method is required to quantitatively evaluate the composition and purity of sesame oil.
This study aims to develop an AI-based analytical software capable of detecting the presence and proportion of adulterated edible oils in sesame oil.
Metabolomics and lipidomics analyses will be conducted using GC-MS, GC-MS/MS, and LC-MS/MS to obtain comprehensive chemical profiles of pure and adulterated sesame oil.
Clustering and supervised multivariate statistical methods will be applied to distinguish pure sesame oil from adulterated samples.
Furthermore, machine learning (AI-based modeling) will be employed to automatically predict the presence and proportion of adulterants in sesame oil.
The AI-based software developed in this study is expected to automate the detection of sesame oil adulteration, thereby enhancing food quality control and safety in distribution processes.
*This research was supported by a grant (24192MFDS052) from Ministry of Food and Drug Safety in 2025