Development of Sensor System for Food Adulterant Detection Using Machine Learning Algorithms

Abstract

Food is of paramount importance as it is essential for life, providing the newlinenecessary nutrients for the growth, repair, and maintenance of body tissues. A good newlinequantity of food is crucial for maintaining good health and ensuring the proper newlinefunctioning of the body. Adulteration in food is an old and familiar problem often seen newlineglobally for gaining extra profit, which may cause severe harmful effects on human newlinebeings. For example, chalk powder into the turmeric powder, color in fruit juices, brick newlinepowder to spicy masala powders, etc. Some harmful chemical substances are added as newlineadulterant to the food substances to make it sustain for long period and for its freshness. newlineDeleting some nutrients from the food is also a kind of adulteration. Adulteration newlinedetection is essential to ensure the safety of consumers. So, the proposed research newlinefocused on adulteration detection on major consumable food items such as milk, newlinevegetables, pepper and rice. newlineAdulteration in milk is a common scenario for gaining extra profit, which newlinemay cause severe harmful effects on humans. The qualitative spectroscopic technique newlineprovides a better solution for detecting the toxic contents of milk. All the available newlinespectroscopic methods for milk adulterant detection are based on laboratory analysis newlinewith costly equipment. This laboratory-based detection takes a long time newline