Food Adulteration And Its Detection Using Artificial Intelligence

Abstract

The food processing sector in India has witnessed remarkable growth in recent years, driven by government initiatives aimed at promoting private investment and infrastructure development. This paper provides an overview of the evolving regulatory framework governing food safety in India, with a focus on recent developments, challenges, and proposed reforms and different new technologies to curb food adulteration.. However, challenges such as delays in implementation and gaps in enforcement have highlighted the need for continuous improvement. The infamous 'Nestle-Maggi' dispute catalyzed reform, prompting the government to reassess existing regulations and strengthen enforcement mechanisms. Therefore, to protect human life new simple rapid approaches are needed to determine the concentration of adulterants in food products. In earlier, several approaches including spectroscopy, chromatography, ELISA are used for determination of adulterants. But these techniques are expensive, time-consuming, and require a skilled person to operate. Recently, nanotechnology-based techniques are successfully used for the identification of adulterates/contaminants. These techniques are simple and sensitive and avoid the use of costly instrumentation. Artificial Intelligence has been proved to be an advanced technology in food science and engineering. In this paper, we intend to proclaim the role of artificial intelligence in food adulteration detection in a systematic way. The potential for machine learning and deep learning in food quality has been analyzed through its applications. Various data sources that are available online to detect food quality have been discussed in this review. The different techniques used to detect food adulteration and the parameters considered while evaluating the food quality have been highlighted. The various comparisons have been done among the state-of-the-art methods along with their datasets sets and results. This study will assist the researchers in analyzing the best method available to detect food quality. It will help them in finding the food products that are studied by different researchers along with relevant future research directions