Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms
Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intellige...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/14/2527 |
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| author | Quancheng Liu Jun Zhou Zhaoyi Wu Didi Ma Yuxuan Ma Shuxiang Fan Lei Yan |
| author_facet | Quancheng Liu Jun Zhou Zhaoyi Wu Didi Ma Yuxuan Ma Shuxiang Fan Lei Yan |
| author_sort | Quancheng Liu |
| collection | DOAJ |
| description | Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification. |
| format | Article |
| id | doaj-art-7a857a9f3af74cea907d276fd8a7f268 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-7a857a9f3af74cea907d276fd8a7f2682025-08-20T03:58:30ZengMDPI AGFoods2304-81582025-07-011414252710.3390/foods14142527Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization AlgorithmsQuancheng Liu0Jun Zhou1Zhaoyi Wu2Didi Ma3Yuxuan Ma4Shuxiang Fan5Lei Yan6School of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaAccurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification.https://www.mdpi.com/2304-8158/14/14/2527dried jujubeshyperspectral imagingmachine learningintelligent optimization algorithmsvariety classificationGWO |
| spellingShingle | Quancheng Liu Jun Zhou Zhaoyi Wu Didi Ma Yuxuan Ma Shuxiang Fan Lei Yan Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms Foods dried jujubes hyperspectral imaging machine learning intelligent optimization algorithms variety classification GWO |
| title | Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms |
| title_full | Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms |
| title_fullStr | Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms |
| title_full_unstemmed | Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms |
| title_short | Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms |
| title_sort | classification prediction of jujube variety based on hyperspectral imaging a comparative study of intelligent optimization algorithms |
| topic | dried jujubes hyperspectral imaging machine learning intelligent optimization algorithms variety classification GWO |
| url | https://www.mdpi.com/2304-8158/14/14/2527 |
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