Discrimination of varieties of rice using near infrared spectral by PCA and MDA model

Author

Zhou Zi-li ; Jing Chun-Feng ; Wu Di ; He Yong ; Li Xiao-Li ; Shao Yong-ni

Author_Institution

Comput. Eng. Dept., Zhejiang Inst. of Mech. & Electr. Eng., Hangzhou, China

fYear

2011

fDate

3-5 Aug. 2011

Firstpage

38

Lastpage

40

Abstract

In this research, a new method for discrimination of varieties of rice by means of near infrared spectroscopy (NIRS) was developed. First, the characteristic spectrums of rice were got through principal component analysis (PCA). The result of the analysis suggests that the reliabilities of first 4 principal components are more than 99.338%. The 2-dimontional plot was drawn with first and second principal components, which indicates that it is a good clustering analysis for classification varieties of rice. The several variables compressed by PCA were used as inputs of multiple discriminant analysis (MDA). 150 samples from three varieties were selected randomly, then they were used to build discriminated model, 30 unknown samples were predicted by this model, the recognition rate is 100%.

Keywords

agricultural engineering; agricultural products; infrared spectroscopy; pattern classification; pattern clustering; principal component analysis; MDA; PCA; clustering analysis; data classification; multiple discriminant analysis; near infrared spectroscopy; principal component analysis; reliability; rice spectrums; rice varieties discrimination; Computers; Information processing; Infrared spectra; Principal component analysis; Production; Reliability; Spectroscopy; Computer data processing; Discrimination; Multiple discriminant analysis (MDA); Near infrared spectral; Principal component analysis (PCA); Rice;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Education (ICCSE), 2011 6th International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-9717-1

Type

conf

DOI

10.1109/ICCSE.2011.6028580

Filename

6028580