{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T15:24:25Z","timestamp":1781364265921,"version":"3.54.1"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["NRL 6.2 Program"],"award-info":[{"award-number":["NRL 6.2 Program"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides\/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field.<\/jats:p>","DOI":"10.3390\/s19102392","type":"journal-article","created":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T23:07:27Z","timestamp":1558912047000},"page":"2392","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-6164","authenticated-orcid":false,"given":"Scott N.","family":"Dean","sequence":"first","affiliation":[{"name":"National Research Council Postdoctoral Fellow, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9894-6640","authenticated-orcid":false,"given":"Lisa C.","family":"Shriver-Lake","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory, Center for Bio\/Molecular Science &amp; Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Stenger","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory, Center for Bio\/Molecular Science &amp; Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey S.","family":"Erickson","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory, Center for Bio\/Molecular Science &amp; Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel P.","family":"Golden","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory, Center for Bio\/Molecular Science &amp; Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott A.","family":"Trammell","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory, Center for Bio\/Molecular Science &amp; Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.3390\/s80314000","article-title":"Electrochemical biosensors\u2014Sensor principles and architectures","volume":"8","author":"Grieshaber","year":"2008","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9041","DOI":"10.1021\/ac9016874","article-title":"Cyclic square wave voltammetry of single and consecutive reversible electron transfer reactions","volume":"81","author":"Helfrick","year":"2009","journal-title":"Anal. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1002\/elan.201300369","article-title":"Square-wave voltammetry: A review on the recent progress","volume":"25","author":"Mirceski","year":"2013","journal-title":"Electroanalysis"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1016\/j.foodchem.2010.03.084","article-title":"Characterisation of catechins in green and black teas using square-wave voltammetry and RP-HPLC-ECD","volume":"122","author":"Novak","year":"2010","journal-title":"Food Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5867","DOI":"10.1016\/j.electacta.2008.04.006","article-title":"Evaluation of the polyphenolic content of extra virgin olive oils using an array of voltammetric sensors","volume":"53","author":"Apetrei","year":"2008","journal-title":"Electrochim Acta"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1007\/s12161-013-9649-x","article-title":"Classification of green and black teas by pca and svm analysis of cyclic voltammetric signals from metallic oxide-modified electrode","volume":"7","author":"Liu","year":"2014","journal-title":"Food Anal. Method"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.talanta.2012.12.042","article-title":"Simultaneous identification and quantification of nitro-containing explosives by advanced chemometric data treatment of cyclic voltammetry at screen-printed electrodes","volume":"107","author":"Ceto","year":"2013","journal-title":"Talanta"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Erickson, J.S., Shriver-Lake, L.C., Zabetakis, D., Stenger, D.A., and Trammell, S.A. (2017). A simple and inexpensive electrochemical assay for the identification of nitrogen containing explosives in the field. Sensors, 17.","DOI":"10.3390\/s17081769"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xi, X., Keogh, E., Shelton, C., Wei, L., and Ratanamahatana, C. (2006, January 25\u201329). Fast time series classification using numerosity reduction. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143974"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_11","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","article-title":"LSTM fully convolutional networks for time series classification","volume":"6","author":"Karim","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1002\/mrm.27166","article-title":"A convolutional neural network to filter artifacts in spectroscopic MRI","volume":"80","author":"Gurbani","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"ref_14","unstructured":"Wu, J., Zhou, B., Peck, D., Hsieh, S., Dialani, V., Mackey, L., and Patterson, G. (2018). Deepminer: Discovering interpretable representations for mammogram classification and explanation. arXiv."},{"key":"ref_15","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1093\/oxfordjournals.pan.a004868","article-title":"Logistic regression in rare events data","volume":"9","author":"King","year":"2001","journal-title":"Political Anal."},{"key":"ref_17","unstructured":"Chollet, F. (2019, March 31). Keras. Available online: https:\/\/keras.io."},{"key":"ref_18","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous systems. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v059.i10","article-title":"Tidy data","volume":"59","author":"Wickham","year":"2014","journal-title":"Journal of Statistical Software"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s10618-015-0418-x","article-title":"Using dynamic time warping distances as features for improved time series classification","volume":"30","author":"Kate","year":"2016","journal-title":"Data Min. Knowl. Disc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.talanta.2014.02.003","article-title":"Chemometrics tools used in analytical chemistry: An overview","volume":"123","author":"Kumar","year":"2014","journal-title":"Talanta"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2392\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:00Z","timestamp":1760187300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2392"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,25]]},"references-count":25,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102392"],"URL":"https:\/\/doi.org\/10.3390\/s19102392","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,25]]}}}