Improved quality control and sustainability in food production by machine learning

Abstract

In recent years, the food industry has faced a number of complex challenges related to both quality control and sustainability. Ensuring consumer safety and satisfaction remains a cornerstone of the food industry, supported by stringent standards that address the risks of contamination and spoilage. However, variability in raw materials, processing techniques and storage conditions are just some of the factors that affect quality in the food industry. To manage this high variability, it is essential to analyse the production process and factors that most influence food quality, aiming to predict and minimise food waste, thereby ensuring a sustainable process. This convergence of quality control and sustainability goals provides fertile ground for machine learning applications. By improving defect detection, process optimisation, resource allocation and predictive maintenance, these models help to improve product quality and reduce environmental impact. This article aims to explore the various applications of machine learning models in the food industry, where the variability of raw materials and the difficulty of controlling production and environmental factors challenge the use of traditional methods. The quality control and sustainability of an industrial corn cakes production process is used as a case study.