Establishment and Validation of Prediction Model for the Secondary Precipitate in High-salt Diluted-state Soy Sauce
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Abstract:
In order to establish the prediction model of secondary precipitate of high-salt dilute-state soy sauce, the physicochemical indexes related to the formation of secondary precipitate of soy sauce were comparatively analyzed, including pH value, iron ion, ferrous ion, polyphenol, polysaccharide, sodium chloride, ethanol, glutamic acid and temperature. SPSS software was used to analyze the correlation between the amount of secondary precipitate and each index, a multiple linear regression prediction model based on the correlation analyses was established. The results showed that the amount of secondary precipitate (Y, g/L) was significantly correlated with pH value (X1) (p<0.01), iron ion (X2, mg/L), polyphenol (X4, g/100 mL) and sodium chloride contents (X6, g/100 mL) (p<0.05), and the multiple linear regression equation amongst them was successfully established. The results showed that there was a good correlation between the predicted value of multiple linear regression equation and the measured value after 3 months storage (R2=0.8517). The prediction model is helpful to find the soy sauce that will produce serious secondary precipitate in advance, and avoid its inflow into the market and cause economic and reputation losses to enterprises, thus it has important application value.
