With the advent of such a method as ANN based analysis, it has been repeatedly used to work with chemometric data (chemical compositions) since the 1990s of the last century [15]. A little later, works related to food processing and analysis of food raw materials appeared, for example, in predicting the nutritional value and composition of seven amino acids in food products using ANN [16]. A little later, another work was carried out to assess the possibility of using ANN to study individual groups of products. Thus, based on 144 varieties of Scotch whiskey, a new model of sensory characteristics and organoleptic qualities, known to be important signs of authenticity of this group of drinks, was developed. The ANN in this study was also used to eliminate personal preferences of tasting commissions members, which could previously influence the results [17]. Similarly, the possibility of using ANN for the classification of white varietal wines is demonstrated. The most successful use in this study was the ANN architecture in the form of a perceptron. The chemistry data for network training were obtained using a gas chromatograph-mass spectrometer (GC-MS). As a result of the analysis, contributions of each of the groups of volatile substances to the aromatization of wine were segregated. Good convergence of data obtained using ANN and classical statistical methods was also noted [7]. Neural networks were also used to create a model for assessing the rate of aging of wines; in the course of these works, it was found that when comparing linear and non-linear activation functions, the latter are preferable [18]. In addition to the above, ANNs have been used previously to predict the rate of drying of potato slices in the sun. As authors have shown, networks with four neurons in the hidden layer are suitable for such purposes. Despite the apparent simplicity of the ANN architecture, a mathematical model with high predictive ability was created. This made it possible to take into account the environmental parameters, product moisture and heat diffusion in food raw materials, as well as the geometry of potato slices [19]. Similar goals were pursued by authors of a study on the evaluation of the speed and parameters of drying of raw materials of wormwood, suitable for obtaining absinthe. The mathematical model made it possible to optimize temperature conditions, air supply rate, etc. [20]. Resemblant goals were pursued by the authors in assessing the effect of drying products from bananas (_Musa nana_) under different temperature conditions. It was shown that ANNs created by authors successfully predict the quantitative concentrations of polyphenol compounds with antiradical properties in banana-processing products [21]. Other works were carried out on the study of the rate of drying of grape berries [22], of the relationship between the concentration of nutrients in kiwi fruits and their keeping quality [23], of kinetics of the release of oils from tarragon raw materials during its ultrasonic treatment [24], etc. No less interesting were works on the use of photo and video images of food raw materials. Thus, for example, a team of authors, in the absence of a spectrophotometer, conducted successful studies to assess the ARA activity of food raw materials. This was achieved by using DPPH, based on a calibration scale built by ANN from cell phone photographs for reaction mixtures with a known number of Trolox units (a synthetic antioxidant). All other reaction mixtures were compared by a neural network with already known color standards, which made it possible to perform a semi-quantitative analysis [25]. A somewhat similar research design was demonstrated when trying to use ANN to assess the end of the fermentation of cocoa beans. The color of beans, obtained by photographic equipment or an electronic scanner, was compared with an assessment of their organoleptic qualities.