meatTheory and practice of meat processingТеория и практика переработки мяса2414-438X2414-441XФГБНУ «Федеральный научный центр пищевых систем им. В.М. Горбатова» РАН10.21323/2414-438X-2024-9-1-15-23meat-318Research ArticleСтатьиDevelopment of an approach to predicting the bioavailability of enteral nutrition productsDevelopment of an approach to predicting the bioavailability of enteral nutrition productshttps://orcid.org/0000-0002-0913-5644KondratenkoV. V.KondratenkoV. V.

Vladimir V. Kondratenko, Candidate of Technical Sciences, Docent, Senior Researcher, Laboratory of Technology of MilkProtein Concentrates, Food Additives and Production of Products Based on Them

35/7, Lyusinovskaya str., 115093, Moscow

Tel.: +7–499–237–04–02

Vladimir V. Kondratenko, Candidate of Technical Sciences, Docent, Senior Researcher, Laboratory of Technology of MilkProtein Concentrates, Food Additives and Production of Products Based on Them

35/7, Lyusinovskaya str., 115093, Moscow

Tel.: +7–499–237–04–02

v_kondratenko@vnimi.orghttps://orcid.org/0000-0001-8967-7074AgarkovaE. Yu.AgarkovaE. Yu.

Evgeniya Yu. Agarkova, Candidate of Technical Sciences, Head of the Laboratory, Laboratory of Technology of MilkProtein Concentrates, Food Additives and Production of Products Based on Them

35/7, Lyusinovskaya str., 115093, Moscow

Tel.: +7–499–237–04–02

Evgeniya Yu. Agarkova, Candidate of Technical Sciences, Head of the Laboratory, Laboratory of Technology of MilkProtein Concentrates, Food Additives and Production of Products Based on Them

35/7, Lyusinovskaya str., 115093, Moscow

Tel.: +7–499–237–04–02

e_agarkova@vnimi.orgAll-Russian Dairy Research InstituteРоссияAll-Russian Dairy Research InstituteRussian Federation202401042024911523Copyright © Kondratenko V.V., Agarkova E.Y., 20242024Kondratenko V.V., Agarkova E.Y.Kondratenko V.V., Agarkova E.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.meatjournal.ru/jour/article/view/318

One of the key factors while developing nutritional supplements is their bioavailability. To determine it, expensive and timeconsuming clinical studies of developed products are necessary. Using in silico methods may speed up and reduce the costs of such clinical studies. The purpose of this study is to develop an approach to predicting the integral bioavailability of enteral nutrition products (ENPs) based on a comprehensive analysis of the matrices of components and indicators. The includes a comprehensive empirical study based on a comparative statistical analysis of the matrix of studied ENPs components. Available information on the composition and indicators of 52 commercial ENPs was used as a research object. This information was compiled into a matrix of components and indicators, marked according to the intended purposes of the products. The set of products included in the matrix was divided into 2 subsets: ENPs corresponding to a given intended purpose and other ENPs. This made it possible to separate statistically significant components and indicators that define the intended purpose of the product with a given threshold of the maximum error probability for inequality of mean values. Using Harrington’s desirability principle in relation to the identified components and indicators made it possible to obtain an integral estimate of desirability for a given intended purpose. A vector characterizing the distance from the integral estimate to the ideal value was introduced as equivalent predicted bioavailability. The upper limit of the optimal range is 0.37, the upper limit of the acceptable range is 0.63. The predicted bioavailability vector scale is the inverse of the integral desirability scale. In contrast to Harrington scaling, the lower the predicted bioavailability value, the more preferable it is. Analysis of the introduced indicator allowed us to establish significant variability in commercial ENPs with respect to predicted bioavailability for diabetes mellitus and thermal injury. Based on the proposed predicted bioavailability vector, a principle has been developed for the evolutionary development of a statistical approach to predicting bioavailability when designing ENPs. This principle is a universal addition to the principle of food combinatorics while developing meat, dairy and plant-based ENPs.

One of the key factors while developing nutritional supplements is their bioavailability. To determine it, expensive and timeconsuming clinical studies of developed products are necessary. Using in silico methods may speed up and reduce the costs of such clinical studies. The purpose of this study is to develop an approach to predicting the integral bioavailability of enteral nutrition products (ENPs) based on a comprehensive analysis of the matrices of components and indicators. The includes a comprehensive empirical study based on a comparative statistical analysis of the matrix of studied ENPs components. Available information on the composition and indicators of 52 commercial ENPs was used as a research object. This information was compiled into a matrix of components and indicators, marked according to the intended purposes of the products. The set of products included in the matrix was divided into 2 subsets: ENPs corresponding to a given intended purpose and other ENPs. This made it possible to separate statistically significant components and indicators that define the intended purpose of the product with a given threshold of the maximum error probability for inequality of mean values. Using Harrington’s desirability principle in relation to the identified components and indicators made it possible to obtain an integral estimate of desirability for a given intended purpose. A vector characterizing the distance from the integral estimate to the ideal value was introduced as equivalent predicted bioavailability. The upper limit of the optimal range is 0.37, the upper limit of the acceptable range is 0.63. The predicted bioavailability vector scale is the inverse of the integral desirability scale. In contrast to Harrington scaling, the lower the predicted bioavailability value, the more preferable it is. Analysis of the introduced indicator allowed us to establish significant variability in commercial ENPs with respect to predicted bioavailability for diabetes mellitus and thermal injury. Based on the proposed predicted bioavailability vector, a principle has been developed for the evolutionary development of a statistical approach to predicting bioavailability when designing ENPs. This principle is a universal addition to the principle of food combinatorics while developing meat, dairy and plant-based ENPs.

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The authors declare that there are no conflicts of interest present.