(1.云南中烟工业有限责任公司 技术中心, 昆明 650202;2.北京工商大学 北京市食品添加剂工程技术研究中心, 北京 100048;3.农业农村部食物与营养发展研究所, 北京 100081)
(1.Technical Center of China Tobacco Yunnan Industrial Co Ltd, Kunming 650202, China;2.Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University,Beijing 100048, China;3.Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing 100081, China)
Natural food pigments are widely used, and trichromatic pigments can be mixed with different colors according to their concentrations. Traditional color matching methods are highly dependent on experience of color matching, which result in low production efficiency, big variation and poor product quality stability. In this study, according to the characteristic absorption peak of natural food pigments had no change during mixing, and based on the matching method of absorption spectrum, color matching models of multiple linear and neural network were established between the concentration of pigments and the absorption value of the characteristic absorption peak. And the optimal model was selected and tested by errors analysis. The results showed that the prediction accuracy and stability of neural network model were better than those in multivariate linear model, and the color differences between the prediction formula and the original formula were within 3, which could not be distinguished by naked eyes. Thus the neural network models were more suitable for color matching requirements. These results provided theoretical basis for the intelligent color matching of natural food pigments.