Development of a zero trans margarine from soybean-based interesterified fats formulated using artificial neural networks.

Abstract

The formulation of products with low levels of saturated and trans fatty acids is a new challenge for industries, and alternative raw materials have been studied. Artificial neural networks (ANNs) have been used for this process. The objective of the present study was to formulate blends, with the help of an ANN, using soybean-based interesterified fats for the production of a zero trans fat margarine similar to a margarine produced using a specific commercial fat. The software was trained with three raw materials to generate formulations with a solid fat content (SFC) and a melting point (MP) similar to specific commercial fats. The SFC, MP, fatty acid and triacylglycerol composition were determined for all ANN blends and commercial fats. Margarines were produced in a pilot plant and evaluated for consistency and stability under temperature cyclization. The ANN showed efficiency in to predict SFC and MP of the suggested formulations, although there were differences at low temperatures for the desired SFC. Differences in the consistency of the commercial fats and ANN blends were observed; however, the margarines produced in the pilot plant had a similar consistency. The margarine prepared with ANN formulation had a higher emulsion stability. Overall, the margarine produced with ANN formulation had characteristics very similar to margarine produced with the commercial fat, and the margarine with soybean-based fat contained reduced saturated and trans fat levels.