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Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this stu...

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Detalles Bibliográficos
Autores principales: Liu, Jie, Fan, Shuang, Cheng, Weimin, Yang, Yang, Li, Xiaohong, Wang, Qi, Liu, Binmei, Xu, Zhuopin, Wu, Yuejin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858067/
https://www.ncbi.nlm.nih.gov/pubmed/36673386
http://dx.doi.org/10.3390/foods12020295
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author Liu, Jie
Fan, Shuang
Cheng, Weimin
Yang, Yang
Li, Xiaohong
Wang, Qi
Liu, Binmei
Xu, Zhuopin
Wu, Yuejin
author_facet Liu, Jie
Fan, Shuang
Cheng, Weimin
Yang, Yang
Li, Xiaohong
Wang, Qi
Liu, Binmei
Xu, Zhuopin
Wu, Yuejin
author_sort Liu, Jie
collection PubMed
description Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.
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spelling pubmed-98580672023-01-21 Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN Liu, Jie Fan, Shuang Cheng, Weimin Yang, Yang Li, Xiaohong Wang, Qi Liu, Binmei Xu, Zhuopin Wu, Yuejin Foods Article Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds. MDPI 2023-01-08 /pmc/articles/PMC9858067/ /pubmed/36673386 http://dx.doi.org/10.3390/foods12020295 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Jie
Fan, Shuang
Cheng, Weimin
Yang, Yang
Li, Xiaohong
Wang, Qi
Liu, Binmei
Xu, Zhuopin
Wu, Yuejin
Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title_full Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title_fullStr Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title_full_unstemmed Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title_short Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN
title_sort non-destructive discrimination of sunflower seeds with different internal mildew grades by fusion of near-infrared diffuse reflectance and transmittance spectra combined with 1d-cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858067/
https://www.ncbi.nlm.nih.gov/pubmed/36673386
http://dx.doi.org/10.3390/foods12020295
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