A CNN-Based Method for Heavy-Metal Ion Detection
Data processing is an essential component of heavy-metal ion detection. Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4520 |
| _version_ | 1797608354703474688 |
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| author | Jian Zhang Feng Chen Ruiyu Zou Jianjun Liao Yonghui Zhang Zeyu Zhu Xinyue Yan Zhiwen Jiang Fangzhou Tan |
| author_facet | Jian Zhang Feng Chen Ruiyu Zou Jianjun Liao Yonghui Zhang Zeyu Zhu Xinyue Yan Zhiwen Jiang Fangzhou Tan |
| author_sort | Jian Zhang |
| collection | DOAJ |
| description | Data processing is an essential component of heavy-metal ion detection. Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implications. This paper proposes a CNN-based heavy-metal ion detection system, which can automatically, accurately, and efficiently detect the type and concentration of heavy-metal ions. First, we used square-wave voltammetry to collect data from heavy-metal ion solutions. For this purpose, a portable electrochemical constant potential instrument was designed for data acquisition. Next, a dataset of 1200 samples was created after data preprocessing and data expansion. Finally, we designed a CNN-based detection network, called HMID-NET. HMID-NET consists of a backbone and two branch networks that simultaneously detect the type and concentration of the ions in the solution. The results of the assay on 12 sets of solutions with different ionic species and concentrations showed that the proposed HMID-NET algorithm ultimately obtained a classification accuracy of 99.99% and a mean relative error of 8.85% in terms of the concentration. |
| first_indexed | 2024-03-11T05:42:17Z |
| format | Article |
| id | doaj.art-d52208b755e149fe849ff3381b9c4ddb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:17Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d52208b755e149fe849ff3381b9c4ddb2023-11-17T16:21:31ZengMDPI AGApplied Sciences2076-34172023-04-01137452010.3390/app13074520A CNN-Based Method for Heavy-Metal Ion DetectionJian Zhang0Feng Chen1Ruiyu Zou2Jianjun Liao3Yonghui Zhang4Zeyu Zhu5Xinyue Yan6Zhiwen Jiang7Fangzhou Tan8School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Applied Science and Technology, Hainan University, Haikou 570228, ChinaData processing is an essential component of heavy-metal ion detection. Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implications. This paper proposes a CNN-based heavy-metal ion detection system, which can automatically, accurately, and efficiently detect the type and concentration of heavy-metal ions. First, we used square-wave voltammetry to collect data from heavy-metal ion solutions. For this purpose, a portable electrochemical constant potential instrument was designed for data acquisition. Next, a dataset of 1200 samples was created after data preprocessing and data expansion. Finally, we designed a CNN-based detection network, called HMID-NET. HMID-NET consists of a backbone and two branch networks that simultaneously detect the type and concentration of the ions in the solution. The results of the assay on 12 sets of solutions with different ionic species and concentrations showed that the proposed HMID-NET algorithm ultimately obtained a classification accuracy of 99.99% and a mean relative error of 8.85% in terms of the concentration.https://www.mdpi.com/2076-3417/13/7/4520heavy-metal ion detectionconvolutional neural networkselectrochemical potentiostat |
| spellingShingle | Jian Zhang Feng Chen Ruiyu Zou Jianjun Liao Yonghui Zhang Zeyu Zhu Xinyue Yan Zhiwen Jiang Fangzhou Tan A CNN-Based Method for Heavy-Metal Ion Detection Applied Sciences heavy-metal ion detection convolutional neural networks electrochemical potentiostat |
| title | A CNN-Based Method for Heavy-Metal Ion Detection |
| title_full | A CNN-Based Method for Heavy-Metal Ion Detection |
| title_fullStr | A CNN-Based Method for Heavy-Metal Ion Detection |
| title_full_unstemmed | A CNN-Based Method for Heavy-Metal Ion Detection |
| title_short | A CNN-Based Method for Heavy-Metal Ion Detection |
| title_sort | cnn based method for heavy metal ion detection |
| topic | heavy-metal ion detection convolutional neural networks electrochemical potentiostat |
| url | https://www.mdpi.com/2076-3417/13/7/4520 |
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