Classification of Apple Varieties Using FT-NIR Spectroscopy and Possibilistic Learning Vector Quantization

Bin Wu; Xiang Li; Sheng Wei Qiu; Xiao Hong Wu; Min Li

doi:10.4028/www.scientific.net/AMM.644-650.1405

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Classification of Apple Varieties Using FT-NIR...

Classification of Apple Varieties Using FT-NIR Spectroscopy and Possibilistic Learning Vector Quantization

Abstract:

Red Fuji, Huaniu and Gala were classified by Fourier transform near infrared (FT-NIR) spectroscopy and possibilistic learning vector quantization (PLVQ) which was proposed to solve the noise sensitivity problem of fuzzy learning vector quantization (PLVQ). Firstly, apple NIR spectra were measured by FT-NIR spectrophotometer. Secondly, principal component analysis (PCA) was used to compress the dimensionality of NIR spectra which was high dimensional. Thirdly, fuzzy c-means (FCM) clustering was run to termination to obtain the cluster vectors for PLVQ. Finally, PLVQ was performed to classify the data. Experimental results showed that this classification method was fast, nondestructive and effective for classifying the variety of apples.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

1405-1408

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.1405

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Online since:

September 2014

Authors:

Bin Wu*, Xiang Li, Sheng Wei Qiu, Xiao Hong Wu, Min Li

Keywords:

Apple, Classification, Near Infrared Spectroscopy, PCA, PLVQ

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