Application of artificial neural networks (ANN) for predicting the effect of processing on the digestibility of foods

The relationship between health and diet is well established. In recent years, it was also demonstrated the importance of optimizing food engineering techniques with artificial intelligence to develop affordable products with better nutritional conditions. Therefore, there is a need to understand how food processing variables affect the digestion and absorption processes of nutrients, mathematically optimize them and, consequently, determine the nutritional quality of the products. This chapter discusses the digestion models commonly used to evaluate the nutritional attributes of food products and how these attributes can be predicted by means of the application of ANN in different unit operations in food processing. The different digestion stages and models (static, semi-dynamic and dynamic), the enzymes used in each one, and their advantages or disadvantages are presented. Also, the theoretical developments related to adaptability and machine learning, for predicting the nutritional attributes of the food product are discussed.