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Molecules 2018, 23(11), 2907; https://doi.org/10.3390/molecules23112907

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

1,2
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1,2
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1,2
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1,2
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3,* and 1,2,4,*
1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
3
College of Information Science and Technology, Shihezi University, Shihezi 832000, China
4
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Christian Huck and Krzysztof B. Bec
Received: 12 October 2018 / Revised: 6 November 2018 / Accepted: 7 November 2018 / Published: 8 November 2018
Full-Text   |   PDF [2754 KB, uploaded 8 November 2018]   |  

Abstract

Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties. View Full-Text
Keywords: near-infrared hyperspectral imaging; raisins; support vector machine; pixel-wise; object-wise near-infrared hyperspectral imaging; raisins; support vector machine; pixel-wise; object-wise
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Feng, L.; Zhu, S.; Zhang, C.; Bao, Y.; Gao, P.; He, Y. Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging. Molecules 2018, 23, 2907.

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