Using deep learning model to predict micronutrients in food after processing

Abstract

Understanding the nutritional content of food after processing is important for the food processing industry. Choosing the appropriate processing method allows users to retain healthy micronutrients. In fact, collecting nutrient information of food before and after processing poses many challenges due to biological changes and interactions of nutritional components. Currently, the approach is to collect data on each nutritional component before and after processing. Conventional machine learning models will then use this data to produce good prediction results but with limited stability. Therefore, we proposed to use a deep learning model to conduct training on a data set with 27 nutritional components that change through two processing processes, boil and fry, extracted from a standard reference data set of the United States. The results show accurate prediction and improve the stability by 8.6%. The study shows the potential of improving deep learning models in predicting post-processing nutritional composition in food processing.