东北地区稻谷储藏期间脂肪酸含量的预测模型

粮食储备是保障国家粮食安全的重要物质基础,谷物中脂肪酸含量是粮食储藏过程中品质变化的敏感性指标。为了实现绿色储粮安全管理,该文采用多元线性回归(multiple linear regression,MLR)、人工神经网络(artificial neural network, ANN)、支持向量回归(support vector regression, SVR)、最小二乘支持向量回归(least square support vector regression,LSSVR)等机器学习算法模型,对东北地区稻谷储藏过程中的脂肪酸含量(以KOH计)进行预测。通过主成分分析(principal component analysis,PCA)方法筛选稻谷关键储藏参数,得到4个影响稻谷脂肪酸含量的关键因子,分别为稻谷入仓水分、入仓脂肪酸含量、储藏有效积温、检测粮温。然后,将得到的关键因子进行归一化处理,再分别输入到MLR、ANN、SVR、LSSVR模型,采用决定系数R2、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方根误差(RMSE)等评价指标对不同模型的预测性能进行对比,探讨稻谷脂肪酸含量预测的最优模型算法。研究结果表明,LSSVR模型的决定系数R2、MAE、MAPE、RMSE分别为0.911、0.275 mg/100 g、1.604%、0.348 mg/100 g,预测效果略优于MLR,明显优于ANN和SVR,LSSVR和MLR模型可作为稻谷储藏期间脂肪酸含量预测的方法。该研究实现了稻谷脂肪酸含量的预测,为科学储粮、安全绿色储粮提供参考。

Models for predicting the fatty acid contents of rice during storage in the northeast China

Reserving grains is important to safeguard food supply and a major prerequisite in national security. Grain quality changes during the reserving storage period, and an important grain quality indictor is fatty acid content. Understanding realtime change in fatty acid content of the grains is thus significant for safe storage of grains. Taking rice storage as an example,we present and compare four models for predicting change in its fatty acid content: multiple linear regression(MLR) model,artificial neural network(ANN) model, support vector regression(SVR) model, and least square support vector regression(LSSVR) model. Comparison of the four models was based on their coefficient of determination(R2), mean absolute error(MAE), mean absolute relative error and root mean square error(RMSE), in reproducing the observed change of fatty acid content. A total of 201 rice storage data were collected from 35 granaries at 5 grain depots from three provinces in the northeastern China. Each data set included inception of the rice storage, initial moisture, initial fatty acid content, moisture,effective accumulated temperature, duration of the storage, grain temperature, granary temperature, time at which the measurements were taken, and fatty acid content. The correlation between the fatty acid content and other parameters was analyzed using the Pearson correlation coefficient. Because of possible correlation between parameters, we reduced the number of the parameters using the principal components analysis by keeping only the key independent parameters, which were initial moisture, initial fatty acid content, effective accumulated temperature and grain temperature. These four parameters were normalized first, and we then randomly selected 80% of the data to train the models, with the remaining 20%to test the models. The particle swarm optimization(PSO) algorithm was used to optimize the parameters of the SVR and LSSVR models prior to the simulation. The model testing results showed that the coefficient of determinant, MAE, MAPE and RMSE of the LSSVR model was 0.911, 0.275 mg/100 g, 1.604% and 0.348 mg/100 g, respectively, significantly better than those of the ANN and SVR models and slightly better than that of the MLR model. A number of testing indicators revealed that the LSSVR and MLR models were most accurate while the SVR model was least accurate for predicting the fatty acid content in the rice. It can be concluded that the LSSVR and MLR models were accurate and reliable, and can be used to estimate change in fatty acid content of the rice using other easy-to-measure parameters. It has implications for estimating other quality indicators of grains in reserving storages.