基于高光谱图像的贝类重金属污染快速无损检测方法研究

熊建芳; 乔付; 刘忠艳; 刘瑶; 郝博麟; 姜薇; 许乐乐; 卢利琼

doi:10.13205/j.hjgc.202210019

基于高光谱图像的贝类重金属污染快速无损检测方法研究

doi: 10.13205/j.hjgc.202210019

1. 岭南师范学院计算机与智能教育学院, 广东湛江 524048;
2. 岭南师范学院电子与电气工程学院, 广东湛江 524048;
3. 岭南师范学院生命科学与技术学院, 广东湛江 524048

基金项目:

广东省教育厅普通高校特色创新项目"自然场景图像中多尺度多形态文本检测与评价方法研究"(2021KTSCX065)

国家自然科学基金青年科学基金项目"贝类重金属污染的高光谱无损检测机理、模型及综合性能评估研究"(62005109)

广东省普通高校特色创新项目"基于粗糙集的高光谱特征波段选择方法研究及其在贝类重金属无损检测中的应用"(2019KTSCX)

广东省自然科学基金面上项目"贝类毒素的高光谱快速检测机理及方法研究"(2020A515011368)

详细信息

作者简介:
熊建芳(1980-),女,讲师,主要研究方向为高光谱图像处理及数据分析。xjf181@163.com

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- 文章访问数: 109
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-23

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

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

摘要

摘要: 基于高光谱图像技术和机器学习算法,提出了一种对重金属污染蛤仔进行快速无损检测的新方法。该方法分为3步:采集蛤仔样本高光谱图像并使用3种方法进行预处理;采用线性判别分析(linear discriminant analysis,LDA)对高光谱数据降维;应用支持向量机(support vector machine, SVM)实现重金属污染蛤仔分类检测。对于以单类重金属污染样本和健康样本为样本集的二分类检测,LDA-SVM模型检测重金属污染样本的准确率可达到99.33%以上。对于以Cd、Cu、Pb、Zn 4类重金属污染样本和健康样本为样本集的五分类检测,检测准确率可达到93.33%。结果表明:LDA-SVM模型能够实现对蛤仔重金属污染快速无损检测,且该模型性能基本不受预处理方法和模型参数的影响,鲁棒性强。
- 重金属 /
- 菲律宾蛤仔 /
- 高光谱 /
- SVM /
- LDA-SVM
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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