# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## 4 Discussion  
In China, grains are usually stored for 3-5 years after harvest. During the period of storage, the quality of the grains will decline over time. Grain quality is affected by many physical and biological factors during storage [33; 55]. An increase of FFA content in stored grain has been observed during storage, which is related to the temperature distribution--the higher the storage temperature, the faster the fatty acid value increases [5; 56]. Figure 6 shows the temperature distribution of a cross section of the stored grain on 26 July 2018 in Section 2.4. It can be clearly seen that a \"cold core\" similar to that in Figure 3 is formed inside the stored grain pile, which leads to significant differences in FFA content. In view of the effect of temperature on FFA accumulation, we included the effective accumulated temperature of grain storage as the input feature of ML, for the reason that the effect of temperature on FFA accumulation can be considered as reducing the error of the prediction results. The theory of accumulated temperature has been widely used in many fields [57; 58; 59]. The value of threshold temperature has a crucial significance [58; 60], so we take the developmental zero temperature of the biological entities including insects and molds inside the stored grain as the threshold temperature in this paper.  
In fact, we obtained little data of the FFA content in stored grain because of the sparsity of sample locations, which are not representative of all stored grain in a granary. In such cases, temperature sensors arranged inside the grain pile are invaluable assets for the estimation of grain quality. Generally, the number of temperature sensors is much larger than that of sample locations. For example, a total of 417 sensors were arranged (one temperature and humidity sensor, and 416 temperature sensors); however, there were only 72 locations when sampling. Therefore, a wider range of grain quality monitoring can be achieved through the temperature sensors, and continuous monitoring can be conducted. Sampling consumes labor, and the subsequent quality determination still requires a lot of manpower and material resources.  
Figure 6: Temperature distribution of stored grain as a cross section on 26 July 2018. The upper left corner is the schematic diagram of the selected cross section.  
Prior to this, researchers have established mathematical regression models between FFA and storage time for FFA estimation during grain storage [19; 20], but these models are not very applicable once storage conditions change. To solve the problem associated with traditional mathematical methods not being able to fully reveal the essential characteristics of the grain during storage, our aim was to train the storage data set using ML to predict the FFA in stored grain. This work, of course, depends on the data set collected, that is, if the data set is larger, we typically obtain a better model in the case of selecting input features appropriately; then, we can obtain a good prediction effect on any given storage factors. The results from this paper demonstrate the applicability of the MKSVR model in realizing the real-time monitoring of grain quality changes, which can reduce any economic losses caused by grain quality reductions.