Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM

Huang, Lin; Chen, Yangfan; Wang, Jianbo; Cheng, Zhandong; Tao, Ling; Zhou, Huamao; Xu, Jianguo; Yao, Mingyin; Liu, Muhua; Chen, Tianbing

doi:10.1039/d2ay01874h

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…The accuracy of the training and test sets of the (SG-FD)-CARS-SVM model for sweet corn variety identification based on hyperspectral imaging was 94.07% and 94.86%, respectively (Zhou et al, 2020). The classification accuracy of the MSC-CARS-BAS-WOA-SVR model was the highest based on near-infrared spectroscopy (Xu et al, 2023). Nondestructive detection of external defects of Kubota based on hyperspectral detection technology, the classification reached an accuracy of 96.77% for the prediction set of the CARS-GS-SVM model (Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Hyperspectral imaging techniques, which integrate spectral and image information, have been widely used in variety identification studies of rice, soybean, and wheat (Wu et al., 2021 ). An SVM parameter optimization model based on an improved genetic algorithm (IGA) enhanced the classification effect of Gannan navel oranges with an accuracy of 98.00% (Huang et al., 2023 ). Based on deep learning combined with hyperspectral imaging technology for the identification of Fritillaria thunbergerii varieties, the CNN model had the highest recognition accuracy (Kabir et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral imaging combined with GA‐SVM for maize variety identification

Zhang,

Wang,

Zhang

et al. 2024

Food Science & Nutrition

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The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high‐quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43–1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky–Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full‐band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS‐SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC‐(CARS‐SPA)‐GA‐SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS‐SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA‐SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging combined with GA‐SVM for maize variety identification

Zhang,

Wang,

Zhang

et al. 2024

Food Science & Nutrition

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“…Myneni Sukesh Babudem et al [13] have classified LIBS spectral data using principal component analysis (PCA) and artificial neural network (ANN) analysis to classify specimens that have not aged, have undergone gamma irradiation, and have undergone water aging. Lin Huang et al [14] used LIBS combined with IGA-SVM to classify navel oranges, with a classification accuracy of up to 98%, providing important guidance for the ra pid online screening of heavy metals in agricultural products using LIBS. Yang et al [15] combined three types of machine learning in LIBS for qualitative analysis of different types of iron ores, with classification accuracy of 83.0%, 80.7%, and 90.3%, respectively, achieving fast and accurate classification of iron ores.…”

mentioning

confidence: 99%