Development of Adaptive Biosensor system approach for food products in storage (A Review)

Abstract

In recent years, the development of adaptive biosensor systems for food products in storage has been an increasingly vital topic of study. Designed to identify and monitor changes in food quality, safety, and freshness during storage, these biosensors can detect and track alterations in these factors. The strategy involves combining machine learning algorithms with the biosensor system to enable real-time data analysis and decision-making. This abstract provides an overview of the approach to the creation of an adaptive biosensor system for storing food products. The biosensor system consists of a variety of sensors and measuring devices, including as temperature sensors, humidity sensors, gas sensors, and microbial sensors, in order to offer a comprehensive picture of food quality during storage. The system employs algorithms for machine learning to assess sensor data and develop prediction models that may be used to optimise storage conditions and avoid food deterioration. These models may also be used to detect and identify particular pollutants in food products, such as bacteria or poisons. By offering real-time monitoring and management of food quality and safety, the development of adaptive biosensor systems for food items in storage has the potential to transform the food business. It is anticipated that these technologies will aid in decreasing waste from food, enhancing food safety, and extending the shelf life of food products.