"Application of Electronic Nose Technology for Aroma Profiling and Quality Assessment of Seasonings"
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Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105031732553
Journal Title
2025 7th International Conference on Power Energy and Innovations Icpei 2025 Proceedings
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SCOPUS
Bibliographic Citation
2025 7th International Conference on Power Energy and Innovations Icpei 2025 Proceedings (2025)
Suggested Citation
Chujan K., Somaudon V., Kerdcharoen T. "Application of Electronic Nose Technology for Aroma Profiling and Quality Assessment of Seasonings". 2025 7th International Conference on Power Energy and Innovations Icpei 2025 Proceedings (2025). doi:10.1109/ICPEI66116.2025.11282680 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115624
Title
"Application of Electronic Nose Technology for Aroma Profiling and Quality Assessment of Seasonings"
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Abstract
Seasonings such as fish sauce, seasoning sauce, soy sauce, and oyster sauce are essential for augmenting taste and fragrance in culinary practices. Their sensory attributes are shaped by ingredients, fermentation, and production techniques. Conventional sensory evaluation techniques dependent on human panels frequently exhibit subjectivity and inconsistency. This study utilizes an electronic nose (e-nose) with eight sensors to detect volatile organic compounds (VOCs) emitted from four types of spice sauces. Principal Component Analysis (PCA) was employed to examine sensor responses, derive aromatic characteristics, and classify scent profiles. The findings indicate that the electronic nose successfully differentiated among sauce varieties, with Sensor 4 exhibiting the greatest sensitivity to critical volatile organic chemicals, including sulfur compounds and amines. PCA demonstrated clear clustering patterns among samples, indicating variations in raw materials and processing methods. These findings validate the efficacy of e-nose technology as a dependable instrument for product categorization, differentiation, and quality assurance in the food sector. The integration of sensor-based VOC detection with statistical modeling underscores the potential for real-time quality evaluation in food production settings.