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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hypers...
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/PMC6321087/ https://www.ncbi.nlm.nih.gov/pubmed/30477266 http://dx.doi.org/10.3390/molecules23123078 |
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author | Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Feng, Xuping He, Yong |
author_facet | Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Feng, Xuping He, Yong |
author_sort | Feng, Lei |
collection | PubMed |
description | Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree. |
format | Online Article Text |
id | pubmed-6321087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63210872019-01-14 Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging Feng, Lei Zhu, Susu Zhang, Chu Bao, Yidan Feng, Xuping He, Yong Molecules Article Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree. MDPI 2018-11-25 /pmc/articles/PMC6321087/ /pubmed/30477266 http://dx.doi.org/10.3390/molecules23123078 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 Feng, Xuping He, Yong Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title | Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title_full | Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title_fullStr | Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title_full_unstemmed | Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title_short | Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging |
title_sort | identification of maize kernel vigor under different accelerated aging times using hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321087/ https://www.ncbi.nlm.nih.gov/pubmed/30477266 http://dx.doi.org/10.3390/molecules23123078 |
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