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Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278444/ https://www.ncbi.nlm.nih.gov/pubmed/30412997 http://dx.doi.org/10.3390/molecules23112907 |
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author | Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Gao, Pan He, Yong |
author_facet | Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Gao, Pan He, Yong |
author_sort | Feng, Lei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6278444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62784442018-12-13 Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Gao, Pan He, Yong Molecules Article 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. MDPI 2018-11-08 /pmc/articles/PMC6278444/ /pubmed/30412997 http://dx.doi.org/10.3390/molecules23112907 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Gao, Pan He, Yong Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title | Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title_full | Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title_fullStr | Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title_full_unstemmed | Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title_short | Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging |
title_sort | variety identification of raisins using near-infrared hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278444/ https://www.ncbi.nlm.nih.gov/pubmed/30412997 http://dx.doi.org/10.3390/molecules23112907 |
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