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Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms

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Yayımlandı:Foods vol. 14, no. 14 (2025), p. 2527-2548
Yazar: Liu Quancheng
Diğer Yazarlar: Zhou, Jun, Wu Zhaoyi, Ma, Didi, Ma Yuxuan, Fan Shuxiang, Yan, Lei
Baskı/Yayın Bilgisi:
MDPI AG
Konular:
United States > US
China
Outliers (statistics)
Particle swarm optimization
Food safety
Accuracy
Comparative analysis
Classification
Quality control
Algorithms
Adaptive sampling
Data analysis
Comparative studies
Preprocessing
Genetic algorithms
Support vector machines
Seeds
Light
Optimization algorithms
Hyperspectral imaging
Ziziphus jujuba
Online Erişim:Citation/Abstract
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Özet: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.
ISSN:2304-8158
DOI:10.3390/foods14142527
Kaynak:Agriculture Science Database