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Molecules 2018, 23(6), 1352; https://doi.org/10.3390/molecules23061352

Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

1,2,†
,
1,2,†
,
1,2
,
3
,
1,2 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, Zhejiang University, Hangzhou 310058, China
3
College of Information Science and Technology, Shihezi University, Shihezi 832000, China
4
State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 22 April 2018 / Revised: 30 May 2018 / Accepted: 30 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Green Analytical Chemistry)
View Full-Text   |   Download PDF [3809 KB, uploaded 4 June 2018]   |  

Abstract

Hyperspectral images in the spectral range of 874–1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future. View Full-Text
Keywords: hyperspectral imaging technique; single grape seed; principal component analysis; support vector machine; discrimination and visualization hyperspectral imaging technique; single grape seed; principal component analysis; support vector machine; discrimination and visualization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhao, Y.; Zhang, C.; Zhu, S.; Gao, P.; Feng, L.; He, Y. Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis. Molecules 2018, 23, 1352.

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