A Soft-sensing Method for Corn Composition Content Using NIRS and LS-SVR

摘要：

A soft-sensing method for oil, protein and starch content in the corn is developed using near-infrared reflectance spectroscopy(NIRS) and least square support vector regression(LS-SVR) techniques, and the feasibility of using different NIR spectrometers for analysis is also examined. Firstly, 90 corn samples are scanned using NIR spectrometers. Then, the original NIRS are processed with multiplicative scatter correction(MSC), Savitzky-Golay second derivative analysis and principal component analysis(PCA). Finally, the soft-sensing model for corn composition content is built using LS-SVR algorithm. The research results show that correlation coefficient (Rc) of NIRS calibrated and actual oil, protein and starch content measured by chemical method are 0.947, 0.969 and 0.948 respectively. It is proved that soft-sensing method has strong robustness for agricultural products.

关键词： Near-infrared reflectance spectroscopy (NIRS) Least square support vector regression(LS-SVR) Corn Oil Protein Starch

作者: Xiaohong Wang

作者单位: Key Laboratory of Numerical Control of Jiangxi Province, Jiujiang University, Jiujiang 332005, China School of Electronic and Information Engineering; Dalian University of Technology, Dalian, 116024, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2014-2019

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）