This paper established a model of partial least squares regression analysis (PLS), the use of the 2012 National Undergraduate Mathematical Contest in Modeling A title given data , including selected aromatic substances , including all of the physical and chemical indicators of wine grapes and wine as independent variables to wine quality as the dependent variable to model . Extracts calculated by the independent variables and the dependent variable contribution rate can be explained by the independent variables selected ratio was 86.28% of the 13 ingredients right, get the PLS regression equation with all the physical and chemical quality of the wine between indicators . The use of standardized indicators variable regression equation to obtain a greater impact on the quality of the wine has 23 physical and chemical indicators , the dry matter content of which 13 positive indicators , 10 negative indicators , positive indicators are the biggest factor affecting grapes ( impact factor of 0.3614 ) , negative indicator is the biggest factor affecting the degree of browning grapes ( impact factor is -0.3788 ) . Matlab programming for the accuracy of the model was tested . Test results show that if you do not consider the technical , environmental factors such as brewing process , the use of physical and chemical indicators have high credibility based on partial least squares regression model to predict the quality of the wine establishment.