基于多元线性回归分析和随机森林算法的水稻贮藏霉变风险控制  

The control of moldy risk during rice storage based on multivariate linear regression analysis and random forest algorithm

作　　者：邓玉睿 程旭东[1] 唐芳 周勇[1] Yurui Deng;Xudong Cheng;Fang Tang;Yong Zhou(State Key Laboratory of Fire Science,University of Science and Technology of China,Hefei 230027,China;Academy of National Food and Strategic Reserves Administration,Beijing 100037,China)

机构地区：[1]中国科学技术大学火灾科学国家重点实验室,安徽合肥230027 [2]国家粮食和物资储备局研究院,北京100037

出　　处：《中国科学技术大学学报》2022年第1期44-51,F0003,共9页JUSTC

基　　金：supported by National Key Research and Development Program of China(2017YFC0805903)。

摘　　要：阐明真菌的生长机理对于减少储粮损失具有重要意义。在影响真菌孢子生长的因素中,最重要的因素是环境温度、稻谷含水量和储藏时间。因此,本研究基于实验数据建立了孢子数和温度、含水率和储藏天数等几个重要因素之间的多元线性回归模型。为了建立更准确的模型,我们将随机森林算法引入稻谷储藏过程中的真菌孢子数目预测模型,用于预测储藏过程中不同温度、含水率和储藏天数下的孢子数。对于随机森林模型,99%的预测值和其对应的原始数据可以达到同一数量级,对于预测孢子数具有很高的准确性。此外,我们绘制了预测曲面图,将环境条件控制在低风险区域可以有效降低稻谷在储藏过程中的霉变风险。Clarifying the mechanism of fungi growth is of great significance for maintaining the quality during grain storage.Among the factors that affect the growth of fungi spores,the most important factors are temperature,moisture content and storage time.Therefore,through this study,a multivariate linear regression model among several important factors,such as the spore number and ambient temperature,rice moisture content and storage days,were developed based on the experimental data.In order to build a more accurate model,we introduce a random forest algorithm into the fungal spore prediction during grain storage.The established regression models can be used to predict the spore number under different ambient temperature,rice moisture content and storage days during the storage process.For the random forest model,it could control the predicted value to be of the same order of magnitude as the actual value for 99%of the original data,which have a high accuracy to predict the spore number during the storage process.Furthermore,we plot the prediction surface graph to help practitioners to control the storage environment within the conditions in the low risk region.

关 键 词：稻谷储藏 真菌生长 孢子数 多元线性回归 随机森林算法 

分 类 号：X826[环境科学与工程—环境工程]