# A CNN-Based Method for Heavy-Metal Ion Detection
## 5 Conclusions  
We presented a CNN-based method for monitoring heavy-metal ions that can detect the type and concentration of HMIs within two minutes. The device can be used to predict the species and concentrations of a wide range of trained metals. The datasets used in HMID-NET were all experimentally derived. The main advantage of our algorithm is the use of a CNN algorithm, which avoids each calibration, shortens the detection time, improves the accuracy, and increases the reliability of the classification and prediction. The paper presented the first concept for the simultaneous type and concentration prediction of ions using CNN structures. The concept proposed in the paper can be used in a wide range of electrochemical detection fields. A detection algorithm that uses machine learning offers the possibility of the simultaneous classification of multiple ions as well as concentration prediction.  
The results, based on the three-electrode electrochemical system, demonstrated the specificity of the test results for different concentrations of solutions. The results were reproducible for the same solution at the same concentration. The sampled data underwent data extension and preprocessing operations to create a dataset containing 1200 data samples. The proposed HMID-NET algorithm was used for class determination and concentration estimation of heavy-metal cations in water. A final classification accuracy of 99.99% and an average relative error of 8.85% in terms of the concentration were obtained.  
The concept of applying convolutional neural networks to electrochemistry is also applicable to other analytical systems. However, the disadvantage of the data-driven approach is the need to reproduce the conditions of the test phase in the training phase. There is a lack of contemporary datasets for heavy-metal detection. The shortage of experimental data prevents the current application of our method to mixed solutions of heavy-metal ions. In future work, we will apply HMID-NET to the detection of mixed solutions and investigate how to obtain the species and concentrations of all the ions contained in the solution simultaneously.  
Conceptualization, J.L., Y.Z. and J.Z.; methodology, J.L., Y.Z. and J.Z.; software, F.C., J.Z., R.Z., Z.Z. and X.Y.; validation, F.C., R.Z., Z.J. and F.T.; formal analysis, J.Z. and F.C.; investigation, J.Z. and F.C.; resources, J.L.; data curation, F.C.; writing--original draft preparation, J.Z. and F.C.; writing--review and editing, J.L. and Y.Z.; visualization, F.C., R.Z., Z.Z. and X.Y.; supervision, J.L. and Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Project of the Hainan Province, grant number ZDYF2019024, and the Hainan Provincial Natural Science Foundation of China, grant number 620 QN236.  
Not applicable.  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
The datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.