基于随机森林回归模型的小麦粉灰分含量快速测定

Study on rapid determination method of ash content in wheat flour based on stochastic forest regression model

[目的]实现小麦粉灰分含量的快速精准测定。[方法]通过预处理小麦原料并深入分析磨粉时间和电导率等关键影响因素,将这些因素作为特征变量引入随机森林回归模型,构建小麦粉灰分含量测定模型。通过算术平均值计算得出最终测定结果,实现小麦粉灰分含量的快速测定。[结果]该方法与实际结果基本一致,测定误差低于0.01 g/100 g,且重复性波动差距低于0.01 g/100 g,平均测定时间为24 min。[结论]试验方法具有较高的测量精度和重复性,显著提升了测定效率。

[Objective]To achieve rapid and accurate determination of ash content in wheat flour.[Methods]By preprocessing wheat raw materials and analyzing key influencing factors such as milling time and conductivity in depth,these factors were introduced as characteristic variables into a random forest regression model to construct a wheat flour ash content determination model.The final determination result was obtained by calculating the arithmetic mean,achieving rapid determination of ash content in wheat flour.[Results]The method was basically consistent with the actual results,with a measurement error of less than 0.01 g/100 g and a repeatability fluctuation difference of less than 0.01 g/100 g.The average measurement time was 24 minutes.[Conclusion]The experimental method has high measurement accuracy and repeatability,significantly improving the measurement efficiency.