Startseite Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering
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Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering

  • Xiaohong Wu EMAIL logo , Bin Wu , Jun Sun und Min Li
Veröffentlicht/Copyright: 5. Dezember 2014
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Abstract

Discrimination of apple varieties plays an important role in apple post-harvest commercial processing. A fast allied fuzzy c-means (FAFCM) clustering algorithm was proposed to classify the apple varieties using near-infrared reflectance (NIR) spectroscopy technology and orthogonal linear discriminant analysis (OLDA) which was used as feature extraction and dimensionality reduction method. Our classification method: the high-dimensional NIR data were reduced to three-dimensional data by OLDA at first, and the FAFCM clustering algorithm was implemented to classify the reduced data. Furthermore, the principal component analysis (PCA) and linear discriminant analysis (LDA) combined with k-nearest neighbor classifier (KNNC), fuzzy c-means (FCM) clustering and unsupervised possibilistic clustering algorithm (UPCA), formed the other four classification methods to classify apple samples in comparison with our proposed method. The experimental results showed that FAFCM achieved the best performance of classification.

Acknowledgments

This research was financially supported by the priority academic program development of Jiangsu Higher Education Institutions, National Science Foundation of China (No. 31471413), Nature Science Foundation of Anhui provincial colleges (No. KJ2012Z302), Anhui provincial college foundation for young talent (No. 2012SQRL251), the key project of Education Department of Sichuan Province (No. 12ZA070) and China Postdoctoral Science Foundation funded project (No. 20090460078).

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Published Online: 2014-12-5
Published in Print: 2015-2-1

©2015 by De Gruyter

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