Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

Xiao Hong Wu; Tong Xiang Cai; Bin Wu; Jun Sun

doi:10.4028/www.scientific.net/AMR.710.768

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 710Research on the Variety Discrimination of Apple...

Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

Abstract:

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

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Periodical:

Advanced Materials Research (Volume 710)

Pages:

768-771

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.710.768

Citation:

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Online since:

June 2013

Authors:

Xiao Hong Wu, Tong Xiang Cai, Bin Wu, Jun Sun

Keywords:

Apple, Hybrid Possibilistic Clustering, NIR, Principal Component Analysis (PCA)

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