Cargando…

Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality...

Descripción completa

Detalles Bibliográficos
Autores principales: Zou, Xiuguo, Wang, Chenyang, Luo, Manman, Ren, Qiaomu, Liu, Yingying, Zhang, Shikai, Bai, Yungang, Meng, Jiawei, Zhang, Wentian, Su, Steven W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025600/
https://www.ncbi.nlm.nih.gov/pubmed/35458982
http://dx.doi.org/10.3390/s22082997
_version_ 1784690912577716224
author Zou, Xiuguo
Wang, Chenyang
Luo, Manman
Ren, Qiaomu
Liu, Yingying
Zhang, Shikai
Bai, Yungang
Meng, Jiawei
Zhang, Wentian
Su, Steven W.
author_facet Zou, Xiuguo
Wang, Chenyang
Luo, Manman
Ren, Qiaomu
Liu, Yingying
Zhang, Shikai
Bai, Yungang
Meng, Jiawei
Zhang, Wentian
Su, Steven W.
author_sort Zou, Xiuguo
collection PubMed
description Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
format Online
Article
Text
id pubmed-9025600
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90256002022-04-23 Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine Zou, Xiuguo Wang, Chenyang Luo, Manman Ren, Qiaomu Liu, Yingying Zhang, Shikai Bai, Yungang Meng, Jiawei Zhang, Wentian Su, Steven W. Sensors (Basel) Article Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage. MDPI 2022-04-14 /pmc/articles/PMC9025600/ /pubmed/35458982 http://dx.doi.org/10.3390/s22082997 Text en © 2022 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
Zou, Xiuguo
Wang, Chenyang
Luo, Manman
Ren, Qiaomu
Liu, Yingying
Zhang, Shikai
Bai, Yungang
Meng, Jiawei
Zhang, Wentian
Su, Steven W.
Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title_full Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title_fullStr Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title_full_unstemmed Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title_short Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine
title_sort design of electronic nose detection system for apple quality grading based on computational fluid dynamics simulation and k-nearest neighbor support vector machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025600/
https://www.ncbi.nlm.nih.gov/pubmed/35458982
http://dx.doi.org/10.3390/s22082997
work_keys_str_mv AT zouxiuguo designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT wangchenyang designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT luomanman designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT renqiaomu designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT liuyingying designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT zhangshikai designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT baiyungang designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT mengjiawei designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT zhangwentian designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine
AT sustevenw designofelectronicnosedetectionsystemforapplequalitygradingbasedoncomputationalfluiddynamicssimulationandknearestneighborsupportvectormachine