A smart olfactory visualization system based on colorimetric sensor array and chemometrics for the identification of...

A smart olfactory visualization system based on colorimetric sensor array was fabricated for rapid identification of adulterants in freshly harvested premium Super India Kainat 1121 (2024 harvest) rice. Purposely, inferior quality stored rice of same variety (2023, 2022, and 2021 harvests) was used to adulterate at 10 %, 20 %, 30 %, 40 %, and 75 % levels. Characteristic profile of volatile compounds in rice procured during different years was identified using HS-SPME-GC/MS. Olfactory sensor arrays were employed to capture the volatile compounds, yielding a characteristic colorimetric map depending upon chemical environment. Discrimination of rice adulteration was realized using PCA, HCA, and kNN algorithms. Distinct grouping of rice samples based on variable levels of adulteration was achieved using PCA and HCA models. The kNN algorithm demonstrated excellent performance for identification of rice adulteration with inferior quality grains. Conclusively, fabricated olfactory sensor arrays were able to successfully discriminate rice adulteration, offering rapid, simple, and low-cost analysis.