基于线性回归分析法预测李果实干制后果干糖酸比

目的:建立一种科学的预测鲜李制作成李干后糖酸比的预测模型。方法:以11种李果为实验材料，利用相关性分析、多元线性回归分析方法，探究鲜果22项理化品质指标与制作为果干后糖酸比的关系。结果:以果干糖酸比为因变量Y，鲜果22项指标为自变量X，经过逐步筛选挑选出5个重要影响因素:总糖（X1）、a*（X2）、镁（X3）、可滴定酸（X4）以及可食率（X5），获得到多元线性回归方程Y=0.739+0.016X1-0.010X2+0.014X3-0.011X4-1.485X5 。回归方程决定系数R2为0.962，显著性F检验对应P值为0，有极显著影响（P＜0.01）。回归标准化残差分析结果显示，该方程符合正态分布，具有较高拟合度。结论:采用多元线性回归模型预测果干糖酸比可行性较高，预测结果较为精确，误差较低。

Prediction of sugar-acid ratio of Prunus salicina L. fruit after drying based on linear regression analysis method

Objective: To establish a scientific predictive model for the sugar-acid ratio of Li fruit after being processed into dried Li fruit. Methods: Eleven varieties of Li fruits were used as experimental materials. Correlation analysis and multiple linear regression analysis were employed to explore the relationship between 22 physicochemical quality indicators of fresh fruit and the sugar-acid ratio after drying. Results: Taking the sugar-acid ratio of dried fruit as the dependent variable (Y) and the 22 indicators of fresh fruit as the independent variables (X), five important influencing factors were selected through stepwise screening: total sugar (X1), a* (X2), magnesium (X3), titratable acidity (X4), and edible rate (X5). The obtained multiple linear regression equation was Y=0.739+0.016X1-0.010X2+0.014X3-0.011X4-1.485X5. The coefficient of determination (R2) of the regression equation was 0.962, and the significance F-test corresponding to the P-value was 0, indicating a highly significant impact (P<0.01). The regression standardized residuals analysis showed that the equation followed a normal distribution and had a high degree of fit. Conclusion: The use of a multiple linear regression model for predicting the sugar-acid ratio of dried fruit is feasible, and the predicted results are accurate with low errors.