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 real-time 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. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.