Optimization of potato noodles technology by response surface methodology and principal component analysis

In this paper, wheat flour and potato granules were used as main raw materials, and the Premna microphylla Turcz leaves were used as auxiliary materials.The single factor and Box-Behnken experimental design, response surface analysis ( RSA) and principal component analysis ( PCA) were used to optimize the processing technology of potato noodles in this paper.The results showed that the first to third principal components cumulative percentages contribution was 86.10%, which could be sufficient to reflect the quality of noodles with comprehensive response of the texture indexes, sensory, cooking loss rate and dry matter water absorption rate. The regression coefficient of the two polynomial regression model established by principal component analysis was significant, and had better fit degree ( p < 0.0001, R2= 0.9699) . Partial least squares regression analysis predicted that the best comprehensive score for the process parameters was: potato granules of 31%, Premna microphylla Turcz Juice of 9%, fermentation time of 31 min, fermentation temperature of 25 ℃.The comprehensive score of the theory reached 0.9194.The actual measured value was 0.9116 which was consistent with the model predicted value.The results indicated that the regression model established by principal component analysis has had a good predictive ability.