Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms
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| Publicat a: | Foods vol. 14, no. 14 (2025), p. 2527-2548 |
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| Autor principal: | |
| Altres autors: | , , , , , |
| Publicat: |
MDPI AG
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3233194942 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2304-8158 | ||
| 024 | 7 | |a 10.3390/foods14142527 |2 doi | |
| 035 | |a 3233194942 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231462 |2 nlm | ||
| 100 | 1 | |a Liu Quancheng |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 245 | 1 | |a Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a United States--US | |
| 651 | 4 | |a China | |
| 653 | |a Outliers (statistics) | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Food safety | ||
| 653 | |a Accuracy | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Classification | ||
| 653 | |a Quality control | ||
| 653 | |a Algorithms | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Data analysis | ||
| 653 | |a Comparative studies | ||
| 653 | |a Preprocessing | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Support vector machines | ||
| 653 | |a Seeds | ||
| 653 | |a Light | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Hyperspectral imaging | ||
| 653 | |a Ziziphus jujuba | ||
| 700 | 1 | |a Zhou, Jun |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 700 | 1 | |a Wu Zhaoyi |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 700 | 1 | |a Ma, Didi |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 700 | 1 | |a Ma Yuxuan |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 700 | 1 | |a Fan Shuxiang |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 700 | 1 | |a Yan, Lei |u School of Technology, Beijing Forestry University, Beijing 100083, China; liuqc@bjfu.edu.cn (Q.L.); wojiaozhoujun198@163.com (J.Z.); zhuixunWZY666@bjfu.edu.cn (Z.W.); Mdd2194155477@bjfu.edu.cn (D.M.); m19213026571@163.com (Y.M.) | |
| 773 | 0 | |t Foods |g vol. 14, no. 14 (2025), p. 2527-2548 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233194942/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233194942/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233194942/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |