Analysing Infant Food Digestion through Artificial Neural Network

Abstract

The symbiotic relationship between nutrition and health is crucial, especially during the formative years of infancy when dietary intake has a significant impact on growth and development. New developments in food engineering highlight how important Artificial Intelligence (AI) is to the best possible optimization of baby food products, making them more inexpensive while maintaining great nutritional characteristics. The way that factors related to food processing affect an infant's ability to digest food and absorb nutrients has a big impact on their development and overall health. To evaluate the nutritional qualities of baby food products, this work elaborates on digestive models. It examines the three stages of digestive models static, semi-dynamic, and dynamic shedding light on the enzymes used in each stage and their corresponding benefits and drawbacks. The work also explores the theoretical developments in machine learning and adaptation and how they might be used to predict nutritional characteristics that are important for baby health. However, making use of Artificial Neural Networks (ANN) in a variety of food processing unit operations, this work will navigate the complex dynamics between food processing techniques and infant digestive stages. This method makes it easier to comprehend how food engineering and sophisticated computational models work together to forecast and maximize the nutritional benefits of foods designed specifically for infants. Fundamentally, the combination of cutting-edge food engineering methods and digestive modeling especially when it comes to baby food has great potential for guaranteeing that baby food offers are nutrient-dense and of high quality, both of which are essential for healthy growth and development.