Classification of Apple Using near Infrared Spectroscopy Based on Fuzzy Discriminant Analysis

Abstract:

Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy.

• Periodical: Advanced Materials Research (Volume 710)
• Pages: 524-528
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.710.524
• Online since: June 2013
• Authors: Xiao Hong Wu, Xing Xing Wan, Bin Wu, Fan Wu
• Keywords: Classification of Apple, Foley-Sammon Discriminant Analysis, Fuzzy Discriminant Analysis, Linear Discriminant Analysis, Near Infrared Spectroscopy (NIRS), Quadratic Discriminant Analysis

References

[1] C. Sârbu, R.D. Nascu-Briciu, A. Kot-Wasik, S. Gorinstein, A. Wasik, J. Namiesnik: Food Chemistry, Vol. 130 (2012), pp.994-1002.

DOI: 10.1016/j.foodchem.2011.07.120

[2] T. Pholpho, S. Pathaveerat, P. Sirisomboon: Journal of Food Engineering, Vol. 104 (2011), pp.169-172.

DOI: 10.1016/j.jfoodeng.2010.12.011

[3] H. Cen, Y. He, M. Huang: European Food Research and Technology, 2007, Vol. 225 (2007): pp.699-705.

[4] W. Luo, S. Huan, H. Fu, et al.: Food Chemistry Vol. 128 (2011), p.555–561.

[5] J. Zhao, H. Hu, X. Zou: Transactions of the CSAE, Vol. 23 (2007), pp.149-152.

[6] R.A. Fisher: Annals of Eugenics, Vol. 7 (1936), pp.179-188.

[7] X.H. Wu and J.J. Zhou: Pattern Recognition, Vol. 39 (2006), pp.2236-2239.

[8] C.F. Lin, S.D. Wang: Pattern Recognition Letters, Vol. 25 (2004), pp.1647-1656.

[9] J.C. Bezdek: Pattern recognition with fuzzy objective function algorithms (Plenum Press, New York 1981).

[10] W. Wang and J. Yang: Acta Automatic Sinica, Vol. 34 (2008), pp.400-407.

[11] D.H. Foley, J.W. Sammon: IEEE Transaction on Computers, Vol. 24 (1975), pp.281-289.

[12] J. Duchene, S. Leclercq: IEEE Transaction on Pattern Analysis and Machine Learning, Vol. 10 (1988), pp.978-983.