Using an artificial neural network (ANN), the values of the antiradical potential of 1315 items of food and agricultural raw materials were calculated. We used an ANN with the structure of a “multilayer perceptron” (MLP) and with the hyberbolic tangent (Tanh) as an activation function. Values reported in the United States Food and Nutrient Database for Dietary Studies (FNDDS) were taken as input to the analysis. When training the ANN, 60 parameters were used, such as the content of plastic substances, food calories, the amount of mineral components, vitamins, the composition of fatty acids and additional substances presented in this database. The analysis revealed correlations, namely, a direct relationship between the value of the antiradical potential (ARP) of food and the concentration of dietary fiber (r = 0.539) and a negative correlation between the value of ARP and the total calorie content of food (r = −0.432) at a significance level of p < 0.001 for both values. The average ARP value for 10 product groups within the 95% CI (confidence interval) was ≈23–28 equivalents (in terms of ascorbic acid) per 1 g of dry matter. The study also evaluated the range of average values of the daily recommended intake of food components (according to Food and Agriculture Organization—FAO, World Health Organization—WHO, Russia and the USA), which within the 95% CI, amounted to 23.41–28.98 equivalents per 1 g of dry weight. Based on the results of the study, it was found that the predicted ARP values depend not only on the type of raw materials and the method of their processing, but also on a number of other environmental and technological factors that make it difficult to obtain accurate values.

Depending on the active acidity of an aqueous solution, the colloidal stability of a titanium dioxide suspension in the form of anatase (food additive E171

In this work, it is shown that intensive treatment of dispersed systems with ultrasound is an effective method for enhancing the interaction of particles and polymer with the subsequent formation of adsorption layers of high thickness and strength. This makes possible to obtain highly stable dispersed systems with a modified surface of the solid phase. The studies proved the important role of intensive ultrasonic action for controlling the properties of the nanoparticles surface in heterophase systems. It is shown that an increase in the activity of the surface of nanoparticles as a result of ultrasonic treatment is of great importance for the study of their antibacterial activity: after ultrasonic activation, nanoparticles are more active in interaction with cell membranes.

Determination of the history of food processing, specifically the detection of undeclared use of reconstituted ingredients instead of fresh ones, remains a serious challenge. In dairy systems, such detection is difficult because conventional chemical markers reflect the cumulative history of time and temperature changes rather than specific processing steps. Although Raman spectroscopy is widely recognized as a non-destructive, high-throughput tool for screening molecular alterations, physicochemically interpretable ratiometric indices are still required, particularly for use in explainable AI models. To identify diagnostic spectral variances, a single batch of raw whole milk was evaluated across distinct states: raw, pasteurized (76, 88, and 98 °C), and spray-dried at outlet air temperatures of ≤70 °C and ~95 °C. The resulting whole milk powders were reconstituted to a 12.1% total solids mass fraction. Raman spectra were acquired using an M532 microscope (λ = 532 nm), and the analytical band areas at 1005, 1063, 1083, and 1121 cm⁻¹ were identified as key diagnostic markers. The structural indices I_lac and I_prot, which are mathematically orthogonal within the principal component space, were subsequently established. As a preliminary proof-of-concept, a binary logistic regression model achieved complete geometric separation between non-dried milk (including high-temperature pasteurized samples) and reconstituted milk. Within the training dataset, the false-positive rate for non-dried milk did not exceed 3.2%, whereas reconstituted milk classification model-predicted probability of belonging to the reconstituted class ranged from 96.8% to 99.9%. Furthermore, the proposed indices show great promise for classifying milk powders based on their heat treatment level. Nevertheless, further validation on independent commercial batches is strictly necessary to evaluate the proposed method’s robustness against matrix and instrumental variability.

Fluorescence emission-excitation matrices for cow milk samples with different fat contents in the range of 0.05–10% and a constant protein content of 3%, as well as for butter and extracted milk components such as casein and lactose, have been measured using a spectrofluorometer. The influence of the increased fat content on the shape of the fluorescence spectra of milk has been studied. In addition, fluorescence spectra measured for serial dilutions of high-fat milk with water and skim milk, along with aqueous dilutions of skim milk, have shown that the fluorescence diagnostics of fat and protein content in milk can be implemented using excitation at only two wavelengths: 280 and 320 nm. The optimal spectral ranges proposed for detecting the content of milk components via fluorescence measurements can be useful when designing UV LED-based fluorescence analyzers of milk composition.

The main process that ensures the production of bread as a marketable product is the external thermal energy supply during baking. This process is accompanied by a complex physical phenomenon - moisture exchange between the dough-bread with the external environment and the steam-air environment of the baking chamber, as well as internal heat and mass transfer in the heated dough piece. At the same time, biochemical processes associated with the transformation of starch and proteins. Taking into account that the main factor realizing the efficiency and quality indicators of baked bread is heat exchange in the dough piece, in this article, taking into account the peculiarities of the dynamics of the heat transfer phenomenon, a quantitative analysis of the course of this process is carried out. In this work, in contrast to the results obtained by other researchers in the field of calculating the processes of heat treatment of food products, a quantitative analysis of the process is proposed based on a new methodological approach to the problem of physical and mathematical substantiation of the heat transfer process in a dough piece. That can be effectively used in the simulation of innovative samples of baking equipment.