# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## 1 Introduction  
Food security has been a research hotspot all over the world due to its impact on the environment, economy, and society [1, 2]. Approximately a third of food produced in the world is wasted every year [3]. As a result of poor grain storage management, the grain's quality and nutritional value deteriorate rapidly during storage [4]. Ensuring that the process of grain storage is of high quality is particularly important. The free fatty acid (FFA) content, a sensitivity indicator of the quality changes, is often employed as a measure of deterioration of stored grain [5, 6]. In the process of grain storage, an increase of FFA content in stored grain has been observed during storage, which is attributed to the role of lipase hydrolysis [5, 7, 8]. This can impact the physical properties of rice in terms of its textural, flavor, composition, and eating quality [9, 10, 11]. Generally, FFA increases with storage period and the quality of stored grain reduces concomitantly [4, 12, 13]. Grain will deteriorate and become inedible when its FFA content reaches a certain standard value [7, 14]. It is reasonable to use the fatty acid value as a quality index during wheat flour storage. Thus, it is of significance to study the changes in FFA accumulation, as they are directly related to the quality loss of stored grain.  
In order to detect FFAs in food, researchers have developed a variety of accurate and reliable methods, such as classic titration with Fourier transform infrared spectroscopy [15] and gas chromatography [16]. In addition, a new method based on a homemade olfactory visualization sensor has been proposed to realize the quantitative determination of FFAs during rice storage [7]. The detectability of FFAs allows us to analyze changes of food quality. There have been many studies involving various grains, such as rice [6; 17] and wheat [18], that have investigated the changing regularity of FFA during storage. Some mathematical equations [19; 20] were developed to predict the FFA content during storage. These models estimate the FFA content of grain by establishing the relationship between FFA values and the storage period. Therefore, it is necessary to develop an efficient and rapid method to achieve an accurate prediction of FFA during storage.  
In fact, the FFA content during grain storage is affected by many factors. Therefore, it is not possible to use the relationship between storage period and FFA content to predict the FFA value. Machine learning (ML) methods can help solve problems involving multiple independent variables, whether responses are linear or not. So far, the development of ML has provided a new idea for grain storage security, and has been used in many applications, such as FFA determination [21], grain storage loss analysis [22], and stored grain insect detection [23]. The successful application of ML in grain storage is of great significance to grain storage safety.  
In ML, artificial neural networks (ANN) have been widely used for a variety of tasks [24; 25]. However, ANN suffers from its weak generalization ability and over-fitting. On the contrary, support vector regression (SVR) has a better generalization ability and exhibits better prediction accuracy due to its implementation of the structural risk minimization principle which considers both the training error and the capacity of the regression model [26]. However, the kernel function and hyperparameters of SVR have profound impacts on the results, and it is time-consuming to determine the kernel and its hyperparameters [27]. Several researchers have proposed multiple kernel learning (MKL) to deal with these problems [28; 29]. In this paper, we propose a model by means of multiple kernel support vector regression to predict the FFA content in stored grain during storage.