RAPID AND NON-DESTRUCTIVE DETECTION FOR SHELLFISH CONTAMINATED BY HEAVY METAL BASED ON HYPERSPECTRAL IMAGES

XIONG Jianfang; QIAO Fu; LIU Zhongyan; LIU Yao; HAO Bolin; JIANG Wei; XU Lele; LU Liqiong

doi:10.13205/j.hjgc.202210019

Volume 40 Issue 10

Oct. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(10): 141-149

XIONG Jianfang, QIAO Fu, LIU Zhongyan, LIU Yao, HAO Bolin, JIANG Wei, XU Lele, LU Liqiong. RAPID AND NON-DESTRUCTIVE DETECTION FOR SHELLFISH CONTAMINATED BY HEAVY METAL BASED ON HYPERSPECTRAL IMAGES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(10): 141-149. doi: 10.13205/j.hjgc.202210019

Citation:

XIONG Jianfang, QIAO Fu, LIU Zhongyan, LIU Yao, HAO Bolin, JIANG Wei, XU Lele, LU Liqiong. RAPID AND NON-DESTRUCTIVE DETECTION FOR SHELLFISH CONTAMINATED BY HEAVY METAL BASED ON HYPERSPECTRAL IMAGES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(10): 141-149. doi: 10.13205/j.hjgc.202210019

Citation:

XIONG Jianfang, QIAO Fu, LIU Zhongyan, LIU Yao, HAO Bolin, JIANG Wei, XU Lele, LU Liqiong. RAPID AND NON-DESTRUCTIVE DETECTION FOR SHELLFISH CONTAMINATED BY HEAVY METAL BASED ON HYPERSPECTRAL IMAGES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(10): 141-149. doi: 10.13205/j.hjgc.202210019

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RAPID AND NON-DESTRUCTIVE DETECTION FOR SHELLFISH CONTAMINATED BY HEAVY METAL BASED ON HYPERSPECTRAL IMAGES

doi: 10.13205/j.hjgc.202210019

1. School of Computer and Intelligent Education, Lingnan Normal University, Zhanjiang 524048, China;
2. School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China;
3. School of Life Sciences and Technology, Lingnan Normal University, Zhanjiang 524048, China

Received Date: 2022-01-23

Abstract

Abstract

A new method for rapid and non-destructive detection of Ruditapes Philippinarum contaminated by heavy metals has been proposed in this paper, which is based on hyperspectral image technology and machine learning algorithm. Firstly, the hyperspectral images of Ruditapes Philippinarum were collected and preprocessed by three preprocessing methods. Then the dimension of hyperspectral image data was reduced by Linear Discriminant Analysis (LDA). Finally, heavy metal-contaminated Ruditapes Philippinarum was detected by the Support Vector Machine (SVM). For binary classification of single heavy metal-contaminated samples and healthy samples, the accuracy of LDA-SVM model for detecting heavy metal-contaminated samples was higher than 99.33%. For multi-classification of Cd, Cu, Pb and Zn-contaminated samples and healthy samples, the accuracy was more than 93.33%. The experimental results showed that LDA-SVM model could realize rapid and non-destructive detection of Ruditapes Philippinarum contaminated by heavy metals. Moreover, the performance of LDA-SVM model was not affected by preprocessing methods and model parameters, and the model had strong robustness.
- heavy metal,
- Ruditapes Philippinarum,
- hyperspectral,
- SVM,
- LDA-SVM

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References(29)

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