{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:58:16Z","timestamp":1768679896550,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006360","name":"Bundesministerium f\u00fcr Wirtschaft und Energie","doi-asserted-by":"publisher","award":["ZF4152305DB8"],"award-info":[{"award-number":["ZF4152305DB8"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006360","name":"Bundesministerium f\u00fcr Wirtschaft und Energie","doi-asserted-by":"publisher","award":["KK5155003DB1"],"award-info":[{"award-number":["KK5155003DB1"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Molding sand mixtures used in the foundry industry consist of various sands (quartz sands, chromite sands, etc.) and additives such as bentonite. The optimum control of the processes involved in using the mixtures and in their regeneration after the casting requires an efficient in-line monitoring method that is not available today. We are investigating whether such a method can be based on electrical impedance spectroscopy (EIS). To establish a database, we have characterized various sand mixtures by EIS in the frequency range from 0.5 kHz to 1 MHz under laboratory conditions. Attempts at classifying the different molding sand mixtures by support vector machines (SVM) show encouraging results. Already high assignment accuracies (above 90%) could even be improved with suitable feature selection (sequential feature selection). At the same time, the standard uncertainty of the SVM results is low, i.e., data assigned to a class by the presented SVMs have a high probability of being assigned correctly. The application of EIS with subsequent evaluation by machine learning (machine-learning-enhanced EIS, MLEIS) in the field of bulk material monitoring in the foundry industry appears possible.<\/jats:p>","DOI":"10.3390\/s24062013","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T11:37:22Z","timestamp":1711021042000},"page":"2013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2637-4591","authenticated-orcid":false,"given":"Luca","family":"Bifano","sequence":"first","affiliation":[{"name":"Chair of Measurement and Control Systems, Faculty of Engineering Science, University of Bayreuth, 95440 Bayreuth, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5771-2277","authenticated-orcid":false,"given":"Xiaohu","family":"Ma","sequence":"additional","affiliation":[{"name":"Chair of Measurement and Control Systems, Faculty of Engineering Science, University of Bayreuth, 95440 Bayreuth, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2000-4730","authenticated-orcid":false,"given":"Gerhard","family":"Fischerauer","sequence":"additional","affiliation":[{"name":"Chair of Measurement and Control Systems, Faculty of Engineering Science, University of Bayreuth, 95440 Bayreuth, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kanoun, O. (2018). Impedance Spectroscopy, De Gruyter. [1st ed.].","DOI":"10.1515\/9783110558920"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10510","DOI":"10.1038\/s41598-019-46974-3","article-title":"Machine learning approaches for automated lesion detection in microwave breast imaging clinical data","volume":"9","author":"Rana","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5979","DOI":"10.1109\/JSEN.2019.2911718","article-title":"Development of a portable electrochemical impedance spectroscopy system for bio-detection","volume":"19","author":"Jiang","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TIM.2014.2371191","article-title":"Low-cost impedance spectroscopy system based on a logarithmic amplifier","volume":"64","author":"Grassini","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.measurement.2016.07.014","article-title":"A simple Arduino-based EIS system for in situ corrosion monitoring of metallic works of art","volume":"114","author":"Grassini","year":"2018","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ruiz-Vargas, A., Arkwright, J.W., and Ivorra, A. (2016, January 4\u20138). A portable bioimpedance measurement system based on Red Pitaya for monitoring and detecting abnormalities in the gastrointestinal tract. Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2016.7843433"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"065701","DOI":"10.1088\/0957-0233\/24\/6\/065701","article-title":"A high-speed bioelectrical impedance spectroscopy system based on the digital auto-balancing bridge method","volume":"24","author":"Li","year":"2013","journal-title":"Meas. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/TBCAS.2016.2592511","article-title":"Novel 10-bit impedance-to-digital converter for electrochemical impedance spectroscopy measurements","volume":"11","author":"Chen","year":"2017","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10776","DOI":"10.1109\/TPEL.2021.3063506","article-title":"A novel on-board electrochemical impedance spectroscopy system for real-time battery impedance estimation","volume":"36","author":"Koseoglou","year":"2021","journal-title":"IEEE Trans. Power Electr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4863","DOI":"10.1109\/JSEN.2023.3236375","article-title":"Approaches to detect microplastics in water using electrical impedance measurements and support vector machines","volume":"23","author":"Meiler","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ma, X., Bifano, L., and Fischerauer, G. (2023). Evaluation of electrical impedance spectra by long short-term memory to estimate nitrate concentrations in soil. Sensors, 23.","DOI":"10.3390\/s23042172"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22858","DOI":"10.1109\/JSEN.2021.3108779","article-title":"Minimally invasive sensors for transurethral impedance spectroscopy","volume":"21","author":"Veil","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_13","first-page":"2505","article-title":"Extraction method of cell\u2019s complex permittivity in cell solutions from measured impedance by GHz electrical impedance spectroscopy","volume":"21","author":"An","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/JSEN.2011.2167227","article-title":"Differentiation between normal and cancerous cells at the single cell level using 3-D electrode electrical impedance spectroscopy","volume":"12","author":"Kang","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8251","DOI":"10.1109\/JSEN.2017.2710146","article-title":"Electrical impedance spectro-tomography based on dielectric relaxation model","volume":"17","author":"Baidillah","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_16","unstructured":"Tilch, W., Polzin, H., and Franke, M. (2019). Praxishandbuch Bentonitgebundener Formstoff (Practical Manual for Bentonite-Bound Molding Material; in German), Schiele & Sch\u00f6n. [2nd ed.]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"287","DOI":"10.5194\/jsss-11-287-2022","article-title":"In situ monitoring of used-sand regeneration in foundries by impedance spectroscopy","volume":"11","author":"Bifano","year":"2022","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jcis.2013.08.025","article-title":"Low-frequency dielectric properties of three bentonites at different absorbed water states","volume":"411","author":"Kaden","year":"2013","journal-title":"J. Colloid Interface Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124155","DOI":"10.1016\/j.jhydrol.2019.124155","article-title":"Impact of soil salinity, texture and measurement frequency on the relations between soil moisture and 20 MHz\u20133 GHz dielectric permittivity spectrum for soils of medium texture","volume":"579","author":"Lewandowski","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.jhydrol.2019.04.066","article-title":"Verification of soil salinity index model based on 0.02\u20133 GHz complex dielectric permittivity spectrum measurements","volume":"574","author":"Szerement","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2001JB000691","article-title":"A method for measuring the solid particle permittivity or electrical conductivity of rocks, sediments, and granular materials","volume":"108","author":"Robinson","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1515\/teme-2019-0121","article-title":"Investigation of complex permittivity spectra of foundry sands","volume":"87","author":"Bifano","year":"2020","journal-title":"Tech. Mess."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/jsss-10-43-2021","article-title":"Characterization of sand and sand\u2013binder systems from the foundry industry with electrical impedance spectroscopy","volume":"10","author":"Bifano","year":"2021","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1007\/s00521-016-2694-9","article-title":"Fast-forward solver for inhomogeneous media using machine learning methods: Artificial neural network, support vector machine and fuzzy logic","volume":"29","author":"Abdolrazzaghi","year":"2016","journal-title":"Neural. Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Mu\u00f1oz, F., and Rodrigo-Mor, A. (2020). Partial discharges and noise discrimination using magnetic antennas, the cross wavelet transform and support vector machines. Sensors, 20.","DOI":"10.3390\/s20113180"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1142\/S0218001403002484","article-title":"Support vector identification of seismic electric signals","volume":"17","author":"Ifantis","year":"2003","journal-title":"Int. J. Pattern Recogn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6184","DOI":"10.1109\/TPAMI.2021.3085969","article-title":"Fast support vector classification for large-scale problems","volume":"44","author":"Cernadas","year":"2022","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5129","DOI":"10.1109\/TNNLS.2020.3027062","article-title":"An improved nonparallel support vector machine","volume":"32","author":"Liu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3566","DOI":"10.1109\/TNNLS.2020.3015442","article-title":"A semiproximal support vector machine approach for binary multiple instance learning","volume":"32","author":"Avolio","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"151","DOI":"10.23919\/JSEE.2021.000014","article-title":"A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking","volume":"32","author":"Lei","year":"2021","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7253","DOI":"10.1109\/TPAMI.2021.3092177","article-title":"Support vector machine classifier via L-0\/1 soft-margin loss","volume":"44","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2590","DOI":"10.1109\/TIM.2015.2418684","article-title":"An adaptive support vector machine-based workpiece surface classification system using high-definition metrology","volume":"64","author":"Du","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Aziz, S., Khan, M.U., Choudhry, Z.A., Aymin, A., and Usman, A. (2019, January 17\u201319). ECG-based biometric authentication using empirical mode decomposition and support vector machines. Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2019.8936174"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1049\/iet-smt.2013.0087","article-title":"Pilot study electrical impedance based tissue classification using support vector machine classifier","volume":"8","author":"Grewal","year":"2014","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1007\/s00226-018-1023-0","article-title":"Moisture content recognition for wood chips in pile using supervised classification","volume":"52","author":"Oussar","year":"2018","journal-title":"Wood Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1007\/s10762-016-0317-2","article-title":"Characterization and classification of coals and rocks using terahertz time-domain spectroscopy","volume":"38","author":"Wang","year":"2016","journal-title":"J. Infrared Millim. Terahertz Waves"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1515\/teme-2022-0042","article-title":"Uncertainty-aware automated machine learning toolbox","volume":"90","author":"Dorst","year":"2022","journal-title":"Tech. Mess."},{"key":"ref_38","unstructured":"The MathWorks, Inc. (2024, February 26). Statistics and Machine Learning Toolbox: 12.1 (R2021a). Available online: https:\/\/de.mathworks.com\/help\/stats\/."},{"key":"ref_39","unstructured":"Jcgm, J.C.G.M. (2008). Evaluation of Measurement Data\u2014Guide to the Expression of Uncertainty in Measurement, JCGM Publications. Guides in Metrology."},{"key":"ref_40","first-page":"132","article-title":"Sequential feature selection for classification","volume":"Volume 7106","author":"Wang","year":"2011","journal-title":"Advances in Artificial Intelligence"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/2013\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:17:31Z","timestamp":1760105851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/2013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,21]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24062013"],"URL":"https:\/\/doi.org\/10.3390\/s24062013","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,21]]}}}