Somaudon V.Kerdcharoen T.Mahidol University2024-08-242024-08-242024-01-01Proceedings - 21st International Joint Conference on Computer Science and Software Engineering, JCSSE 2024 (2024) , 678-681https://repository.li.mahidol.ac.th/handle/20.500.14594/100599The dynamic shifts in the chemical composition of coffee with roasting have been successfully tracked using the electronic nose, representing its potential as a tool for profiling the aromatic complexity of coffee. Traditional methods have confirmed the physical transformation of beans during roasting, a well-known phenomenon. Complementing these findings, the e-nose demonstrates its efficacy by capturing the aromatic changes that occur throughout the roasting process. Furthermore, machine learning models applied to the e-nose data such as kNN, SVM, decision tree, and ANN, have shown promising results. Among these, the SVM model provides the greatest accurately reflecting the roasting profiles. This innovative, non-invasive approach provides a valuable alternative for the industry, paving the way for future applications in quality control and flavor profiling within the coffee industry.MathematicsComputer ScienceDecision SciencesConference PaperSCOPUS10.1109/JCSSE61278.2024.106137152-s2.0-85201387765