Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression

• Published in: Applied sciences Vol. 10; no. 18; p. 6555
• Main Authors: Wang, Qiyang, Han, Feng, Wu, Zidan, Lan, Tianyi, Wu, Wenfu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Back propagation networks; Errors; Fatty acids; Food; Food quality; food security; Fourier transforms; free fatty acid; Grain storage; Learning algorithms; Machine learning; Moisture content; Monitoring systems; multiple-kernel learning; Neural networks; Physical properties; Regression analysis; Rice; Rice fields; Sensors; Statistical analysis; Support vector machines; support vector regression; Values; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10186555

Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach—multiple-kernel support vector regression—to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain.