Assessment and Prediction of Free Fatty Acids Changes in Maize Based on Effective Accumulated Temperature in Large Granaries

Abstract

In large granaries, maize storage period could last 3–5 years in China. Free fatty acids (FFA) content is commonly used as a sensitivity indicator of grain quality changes during storage. Samples of the stored grain are taken manually and tested in the laboratory monthly for regular quality monitoring. Although substantial labor and time are consumed, the testing way lacks of real-time. Temperature is a main factor influencing the quality of stored grain. In this study, it was analyzed that the effective accumulated temperature (EAT) had a strong correlation coefficient with FFA. Classification and regression trees (CART) and model trees (ML) in machine learning (ML) methods were used to estimate FFA by EAT based on data collected from 11 large granaries in northeastern China. While the minimum number of samples for segmentation was set as 20, the two models both had the optimal performance. The two models were evaluated by mean absolute error (MAE), root mean square error (RMSE) and the coefficient of determination (R2). The MAE, RMSE and R2 of CART and MT are 1.296, 1.761, 0.759; 1.247, 1.821, and 0.741, respectively. As CART had the lower RMSE and the larger R2, the model performance of CART was better. The model provided a method to estimate the changes of FFA based on the exist temperature monitoring system in large granaries as a way of stored maize quality real-time monitoring.