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Food Adulteration Detection using Artificial Intelligence: A Systematic Review

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Abstract

Food Adulteration is a deceptive act of misleading food buyers for economic gain. It has been a major concern due to its risk to public health, reduction of food quality or nutritional value. It is a food fraud that has incensed the food industry and has attracted the attention of the community since the last century. To ensure consumer protection against fraudulent activities, authentication of food and the detection of adulterants in various food items should be taken into consideration. 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.

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Acknowledgements

This work was supported by Thapar-TAU Center for Excellence in Food Security (T2CEFS), under research project “A Data-Driven Approach to Precision Agriculture in Small Farms Project”.

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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Kashish Goyal, Parteek Kumar & Karun Verma

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Goyal, K., Kumar, P. & Verma, K. Food Adulteration Detection using Artificial Intelligence: A Systematic Review. Arch Computat Methods Eng 29, 397–426 (2022). https://doi.org/10.1007/s11831-021-09600-y

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