A soft-sensing method for corn composition content using NIRS and LS-SVR

Author

Wang, Xiaohong

Author_Institution

Key Lab. of Numerical Control of Jiangxi Province, Jiujiang Univ., Jiujiang, China

fYear

2009

fDate

17-19 June 2009

Firstpage

2014

Lastpage

2019

Abstract

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.

Keywords

crops; infrared spectroscopy; least squares approximations; principal component analysis; proteins; regression analysis; support vector machines; LS-SVR; NIR spectrometers; NIRS; Savitzky-Golay second derivative analysis; agricultural products; corn composition content; correlation coefficient; least square support vector regression techniques; multiplicative scatter correction; near-infrared reflectance spectroscopy; oil content; principal component analysis; protein content; robustness; soft-sensing method; starch content; Agricultural products; Chemicals; Least squares methods; Petroleum; Principal component analysis; Protein engineering; Reflectivity; Robustness; Scattering; Spectroscopy; Corn; Least square support vector regression (LS-SVR); Near-infrared reflectance spectroscopy (NIRS); Oil; Protein; Starch;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5191976

Filename

5191976