Studying the features of the use of neural networks and machine learning in the design of food systems

Abstract

The features of the use of neural networks and machine learning methods for the design of food systems are studied using the example of processed cheese. A database has been developed that includes 869 cheese recipes, and a program for managing an electronic recipe directory. A neural network has been developed, for the training of which the method “Training with a teacher”, the activation function “ReLu” and the author’s program written in the Python programming language were used. The neural network consisted of 9 neurons in the input layer, two hidden layers of 65 neurons each, and an output layer consisting of 1 neuron. To determine the linear correlation between columns, a matrix was used showing the relationship between values using the Pearson coefficient. A training sample containing 80% of the total number of recipes, a test sample of 10% of the total number of recipes, and a test sample of 10% of the total number of recipes were selected from the primary data set. As a result of training the neural network, an information-advising system for a food technologist has been developed. The system is designed to predict the quality of food recipes. The information-advising system will speed up the correction of existing recipes and the development of recipes for new products, theoretically predict their quality before launching into production. The information-advising system was tested on a test recipe of a new processed cheese. It has been established that with a certainty of 63.6%, the integral indicator of the quality of the new cheese will be 7.7 conventional units. This predicted value was confirmed during the practical production of cheese according to the designed recipe in laboratory conditions and during approbation in production conditions. The new cheese is really distinguished by high quality, good organoleptic and physico-chemical parameters.