Rapid Detection of Rice Adulteration Using a Low-Cost Electronic Nose and Machine Learning Modelling

Abstract

Food fraud is one of the primary issues that may threaten consumers’ trust and confidence in the food industry. Detecting food fraud, such as rice adulteration, is challenging since the adulterant looks identical to authentic rice. Moreover, the detection procedure is commonly time-consuming and requires high-cost instruments in order to analyse samples in the laboratory. Therefore, this study aimed to develop a rapid method to detect rice adulteration using a low-cost and portable electronic nose (e-nose) coupled with machine learning (ML). Six types of adulterated rice samples were prepared by mixing the authentic rice (i.e., premium grade rice, organic rice, aromatic rice) with the respec..