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The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.
Yudhistira et al. (Tue,) studied this question.