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	Distinguishing different cultivar kiwifruits by near-infrared spectroscopy Based on pattern recognition methods
	Liu, Bin1,2; Tang, Mingjie2,3; Lu, Liming1; Lin, Yaling1; Cai, Jianrong2; Ma, Teng1
	2015
摘要	An approach that used to distinguish four kinds of different cultivar kiwifruits by near-infrared spectroscopy was proposed in this paper. Original spectra of kiwifruit in wavelength range of 10000-4000/cm were acquired and pre-processed by standard normal vitiate transformation (SNV), and principle component analysis (PCA) was employed to extract useful information. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were attempted comparatively in this work. Some parameters of the model were optimized by cross-validation in building model. Experimental results showed that the performance of support vector machine model is the best in contrast to K-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with identification rates of 98.5% in calibration set and 97.5% in prediction set respectively. Based on the results, it was concluded that the near-infrared spectroscopy combined with supervised pattern recognition has significant potential in kiwifruit species detection.
发表期刊	International Agricultural Engineering Journal
ISSN	08582114
卷号	24期号:3页码:61-67
通讯作者	Cai, Jianrong
收录类别	EI
语种	英语