The study of wax gelators is a new direction with the potential for use in foods for special dietary uses. Waxes of both plant and animal origin, which consist of various combinations of organic compounds: hydrocarbons, wax esters, free fatty acids and free higher alcohols, are among the studied ones. One of the main characteristics of these gelling agents is their melting point, which largely depends on the component composition of the wax. However, at present there are no models capable to reliably predict this parameter depending on the gel-forming agent composition. of this work was to build models predicting the influence of wax composition on its melting temperature. . Preparative flash chromatography was used to obtain individual fractions of the beeswax (obtained from a Russian apiary). On the basis of these fractions, combinations were made and their melting temperatures were measured using the method of differential scanning calorimetry. Two approaches were used to predict the melting temperatures of wax gel-formers: multiple linear regression and the construction of artificial neural networks. . A total of 68 gelling agent combinations were analyzed. The model obtained on the basis of multiple linear regression was characterized by high values of the multiple correlation coefficient (r=0.77). Nevertheless, high values of the standard deviation in the most cases led to false prediction results. The application of artificial neural networks made it possible to get a more reliable prognosticating system with high values of the correlation coefficient (r=0.97) and the standard deviation not exceeding 3 °С. . As a result of the conducted work it has been shown that it is possible to predict melting temperatures on the basis of the component composition of gel-forming fractions. The use of the data obtained in the article will allow to develop oleogels with the specified composition and properties for their use in new types of foodstuffs.
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