Discrimination of basmati rice adulteration using colorimetric sensor array system

Abstract

This study involved the fabrication of colorimetric sensor array system for rapid discrimination of adulterants in premium grade basmati rice. Purposely, low-grade white rice was used to adulterate the pure basmati rice at 5%, 10%, 15%, 25%, 50%, and 75% weight ratios. Sensor array system was used to capture the odors of prepared samples, resulting in color difference map based on chemical environment. The principal component analysis (PCA), hierarchical cluster analysis (HCA), and k-nearest neighbors (kNN) algorithm were subsequently used to identify the similarity between authentic and adulterated samples. A decent discrimination between authentic and adulterated rice samples was observed in the scatter plot of PCA and HCA dendrogram. The multilayered kNN models were able to effectively discriminate the prepared rice samples. The study concludes that fabricated sensor array system may be used as an effective tool for rapid discrimination of authentic and adulterated samples. • Colorimetric sensor array was fabricated for discrimination of rice adulteration. • Volatile compounds of basmati rice and adulterant were identified using GC-MS. • PCA and HCA were able to discriminate different levels of adulteration. • Optimal discrimination results were achieved with multilayered kNN algorithm. • Fabricated system promotes the development and application of sensor arrays.