Rapid Classification of Pork NIR Speca Using PCA and FLVQ

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

doi:10.4028/www.scientific.net/AMM.602-605.3867

Paper Titles

The Bi-Dimensional Bedrosian’s Principle for Image Decomposition
p.3854

Research on Verification Method of OpenGL Simulated Image Based on Camera Calibration
p.3859

Space-Ground Synchronization TT&C Resource Distribution
p.3863

Rapid Classification of Pork NIR Speca Using PCA and FLVQ
p.3867

Research of Software Defect Prediction Based on CART
p.3871

Prony Algorithm's Application on Extracting the Parameters of the Power System Transient Fault
p.3877

Traffic Volume Prediction Using Improved Kalman Filter
p.3881

Methods for Digital to Analog Conversion of Arts Image Fusion Based on Multimedia Technology
p.3886

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Rapid Classification of Pork NIR Speca Using PCA...

Rapid Classification of Pork NIR Speca Using PCA and FLVQ

Abstract:

Pork storage time is relevant to its freshness which influences pork quality. To achieve the rapid and effective discrimination of pork storage time, near infrared spectroscopy was used to collect the near infrared reflectance (NIR) spectra of pork in different storage time. The high-dimensional NIR spectra was firstly compressed by principal component analysis (PCA) and then classified by fuzzy learning vector quantization (FLVQ). PCA plus FLVQ is a completely unsupervised learning algorithm which finds hidden patterns in unlabeled data. Experimental results showed that PCA plus FLVQ could classify pork NIR spectra effectively.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

3867-3870

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.3867

Citation:

Cite this paper

Online since:

August 2014

Authors:

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

Keywords:

Classification, FLVQ, Near Infrared Spectroscopy (NIRS), PCA, Pork

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] D.I. Ellis, D. Broadhurst, D.B. Kell, J.J. Rowland and R. Goodacre: Applied and Environmental Microbiology, Vol. 68 (2002), pp.2822-2828.

Google Scholar

[2] O. Papadopoulou, E.Z. Panagou, C.C. Tassou and G.J.E. Nychas: Food Research International, Vol. 44 (2011), pp.3264-3271.

DOI: 10.1016/j.foodres.2011.09.012

Google Scholar

[3] S.A. Ammor, A. Argyri and G.J.E. Nychas: Meat Science, Vol. 81 (2009), pp.507-514.

Google Scholar

[4] J. Cai, Q. Chen, X. Wan and J. Zhao: Food Chemistry, Vol. 126 (2011), pp.1354-1360.

Google Scholar

[5] Q. Chen, J. Cai, X. Wan and J. Zhao: LWT - Food Science and Technology, Vol. 44 (2011), p.2053-(2058).

Google Scholar

[6] E.C.K. Tsao, J.C. Bezdek and N.R. Pal: Pattern Recognition, Vol. 27 (1994), pp.757-764.

Google Scholar

[7] J.C. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York (1981).

Google Scholar

[8] J.C. Bezdek, E.C.K. Tsao and N.R. Pal, in: Proceedings of the First IEEE Conference on Fuzzy Systems, SanDiego, USA (1992).

Google Scholar

[9] C.W.D. deAlmeida, R.C.R. deSouza and A.B. Candeias: Neurocomputing, Vol. 99 (2013), pp.65-75.

Google Scholar

[10] I.T. Jolliffe: Principal Component Analysis, 2nd edition, Springer, New York (2002).

Google Scholar