Introduction: Obtaining correct data on the chemical composition of food products is required for solving different problems, including those related to human health. It is important not only to organize the process of collecting sufficient data, but also to develop an analytical algorithm that considers different periods of data collection and types of foods. Objective: To test and adjust the algorithm for obtaining statistically correct values of average concentrations and variability of the main micro- and macronutrients in bakery products. Materials and methods: In order to test and then improve the algorithm, we used the results of laboratory testing of bakery products collected within the framework of the Federal Project on Public Health Strengthening in 2020–2021 by the laboratories of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor). Results: An increase in the sample size made it possible to identify new groups of bakery products. At the same time, the data of the sample combined over two years demonstrated the best convergence. The sodium content was determined as an additional clustering feature. The implementation of the algorithm on the pooled data enabled further reduction of the coefficient of variability. Conclusion: Sequential processing of laboratory test results using the developed algorithm allowed us to update information on the chemical composition of bread currently sold by retailers and determine the presence of products that are critical in terms of their sodium content. It is of interest to expand capabilities of the algorithm in terms of automating the selection of priority indicators for clustering and, as a result, the possibility of processing similar data arrays.

Introduction: Data on the chemical composition of food products are important for solving many problems in medical and social spheres. The development of mechanisms for updating existing databases of the chemical composition of foodstuffs, including the need to change approaches to obtaining primary data and develop algorithms of their processing, is in demand. Objective: To develop an algorithm of obtaining statistically correct values of average concentrations and variability of the main micro– and macronutrients in bakery products. Materials and methods: To develop and test the algorithm, we used the results of testing bakery products obtained in 2020 within the Federal Project on Public Health Strengthening by the laboratories of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor). Results: A good separating power was demonstrated by k-means clustering into two groups by the fat content. An algorithm for generalization of data obtained from different laboratories is proposed due to impossibility to assess the whole aggregate of potential errors related to testing, laboratory personnel, data entry, etc. To assess the effectiveness of each stage and the algorithm as a whole, we used the value of the deviation of the resulting variability from the initial one. As a result of processing, this indicator ranged from 5 % for the carbohydrate content to 72 % for the fat content. For the contents of carbohydrates, ash, dietary fiber, vitamin B1, sodium and moisture in both clusters, statistically significant differences were obtained between the processed and original data. This result and the comparability of the obtained values of the mean and variability with the reference ones may indicate the correctness of the algorithm. There were no statistically significant differences between the obtained values of fat and protein content, but the consistency of the order of values with the reference ones was also recorded. Conclusion: The developed algorithm made it possible to obtain up-to-date information about the chemical composition of bakery products. Further research should be aimed at testing and, if necessary, adjusting the algorithm for all major food groups.

Introduction: The assessment of actual nutrition of the population, both at the individual and population level, strongly depends on the accuracy of data on the chemical composition of food products. Milk is an important component of a diet, and a precise estimation of micro- and macronutrients consumed with it is essential for public health assessment. Objective: To develop an algorithm for obtaining statistically accurate values of average concentrations and variability of basic micro- and macronutrients in milk. Materials and methods: To elaborate and check the algorithm, we used milk fat test results collected within the Federal Project on Public Health Strengthening by the laboratories of the Federal Service for Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) in the years 2020–2021. Results: Due to numerous missing and outlying values of milk composition testing, an appropriate adjustment of the algorithm was necessary. The best separating ability was demonstrated by the approach of dividing types of milk into clusters based on their fat and calcium content. The three clusters obtained included milk with a 2.5 % fat content and the average calcium concentration of 1,144 mg/L, milk with a 3.2 % fat content and the average calcium concentration of 1,180 mg/L, and milk with both fat contents and the mean calcium level of 597 mg/L. The algorithm was validated by checking the completeness of data on the fatty acid composition and a low variability of values. Conclusion: The developed algorithm has enabled us to obtain up-to-date information on the chemical composition of milk sold by food retailers in the Russian Federation. Low-calcium milk on the market is of special concern as its average consumption fails to satisfy human physiological needs. At the same time, the content of saturated fat was below 2.2 g/100 g in the cluster of milk types with the maximum fat content, thus raising no additional health concerns. Further studies should be aimed at determining the acceptable and correct stages of data preprocessing that maintain a balance between the obtained accuracy of values and their actual reproducibility.