Predictive AI Models for Food Spoilage and Shelf-Life Estimation

Abstract

Food spoilage is a global problem which causes food waste, economic loss and foodborne illness. The shelf life and spoilage estimation of food is traditionally done with fixed expiration dates and this leads to disposal of still eatable food or eating spoiled food. Recently, with the development of the Artificial Intelligence (AI), the predictive models have been developed to better evaluate the food spoilage based on such factors as temperature, humidity, microbial activities and gas emissions. This paper discusses the part played by AI in the prediction of food spoilage, while also outlining various machine learning and deep learning models (regression, classification, convolutional neural network – CNN and hybrid AI). Food spoilage estimation powered by AI relies on multiple sources of data including IoT enabled sensors, Spectroscopy as well as real time environmental monitoring. The practical use in the food industry of such data driven models is in the context of real life applications as smart packaging, AI powered quality in supply chains, retail inventory product optimization. However, the adoption of AI in this field is limited as the data is scarce and of low quality, the models have limited accuracy, ethical concerns exist, and implementation is expensive. In this review, potential for AI in transforming food spoilage estimation is highlighted and this could be achieved by working on obtaining greater accuracy, scalability, and adoption of the model in different food sectors. The role of AI in enhancing food security, sustainability and efficient use of resources, waste reduction and increasing accessibility of good quality perishables to every consumer will gain increasing feasibility with the improvement in AI.