Predicting beta-glucan content in food using neural networks

Abstract Beta-glucan, a bioactive polysaccharide found in foods like oats, barley, yeast, and mushrooms, is widely recognized for its significant health benefits, including immune modulation and cholesterol reduction. Accurate prediction of beta-glucan content in food products is essential for optimizing formulations, improving quality control, and enhancing dietary recommendations. This study presents a machine learning approach utilizing neural networks to predict beta-glucan content based on key physicochemical and structural attributes of food samples. Input features such as food type, molecular weight, solubility, moisture content, processing conditions, and chemical composition were analyzed and used to train a feedforward neural network. The model was optimized through hyperparameter tuning and validated using cross-validation techniques. Performance evaluation metrics, including R2, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), demonstrated the model’s ability to achieve high predictive accuracy and generalizability. The proposed neural network-based framework provides a cost-effective, time-saving alternative to traditional laboratory-based beta-glucan measurements. This predictive tool has practical applications in food manufacturing, nutritional research, and quality assurance, enabling the design of functional foods with enhanced beta-glucan content tailored for specific health benefits.