Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction

Artificial intelligence (AI) is being applied by the bakery industry to substitute existing practices with data-informed strategies to enhance the accuracy of quality predictions, process optimization, and shelf-life prediction. This review discusses how AI, specifically machine learning (ML) algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forests (RF) and deep neural networks (DNNs), can be applied in bread-making and product innovation. With AI models, there is the possibility of analyzing the interactions between formulation, processing and quality features in a complex manner to predict the loaf volume, crumb structure, staling and risk of spoilage. The optimization of formulations with the help of AI is also used to produce cost-effective, nutritionally fortified products without losing the sensory quality. Interest of AI in combination with improved methods like gas chromatography-olfactometry and texture profiling, <i>E</i>-nose/E-tongue systems improves the senses prediction prior to execution. Besides, the combination of AI and digital twins, kinetic models, and IoT systems enhance real-time analysis and operational performance, which can lead to sustainability, less food waste, and individualized and health-focused bakery items.