Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology

结合人工神经网络和响应面法优化咖啡果肉酒酿造工艺

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Abstract

Coffee pulp wine was made from coffee pulp. The level range of fermentation factors was determined by one-factor experiment. The significance factors affecting fermentation were screened by Plackett-Burman and steepest climbing experiments, which were material-liquid ratio, initial pH, initial sugar and yeast amount, respectively. The screened factors were then subjected to a central combination design, and the results were optimized using RSM and ANN-GA. The ANN-GA shows a more accurate optimization effect compared with RSM and a higher degree of model fitting. The coefficient of determination (R(2)) of the ANN-GA predicted value was 0.9140, while the RMSE was 0.0896. The best results of optimization process showed that the material-liquid ratio was 4.25 : 95.75, the initial pH value was 6.92, the initial sugar concentration was 22.248%, the yeast addition was 1.98%, and the final predicted value was 10.255 mg/L. The research results provided a technical reference for the production of coffee pulp wines.

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