{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:12:29Z","timestamp":1776784349858,"version":"3.51.2"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CONICYT","award":["ACM170008"],"award-info":[{"award-number":["ACM170008"]}]},{"name":"CONICYT","award":["3170897"],"award-info":[{"award-number":["3170897"]}]},{"name":"CONICYT","award":["16M10029"],"award-info":[{"award-number":["16M10029"]}]},{"name":"CONICYT Fondecyt\/Postdoctorate","award":["ACM170008"],"award-info":[{"award-number":["ACM170008"]}]},{"name":"CONICYT Fondecyt\/Postdoctorate","award":["3170897"],"award-info":[{"award-number":["3170897"]}]},{"name":"CONICYT Fondecyt\/Postdoctorate","award":["16M10029"],"award-info":[{"award-number":["16M10029"]}]},{"name":"FONDEF IT","award":["ACM170008"],"award-info":[{"award-number":["ACM170008"]}]},{"name":"FONDEF IT","award":["3170897"],"award-info":[{"award-number":["3170897"]}]},{"name":"FONDEF IT","award":["16M10029"],"award-info":[{"award-number":["16M10029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra from the combustion of high-grade sulfide minerals. A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. The optimized deep learning model outperformed conventional machine learning methods, highlighting the effectiveness of deep learning for spectral analysis in high-temperature environments. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability and identify key spectral regions contributing to classification decisions. The results demonstrated that the model successfully distinguished spectral features associated with different mineral species, offering insights into combustion dynamics. These findings support the potential integration of deep learning for real-time spectral monitoring in industrial flash smelting operations, thereby enabling more precise process control and decision-making.<\/jats:p>","DOI":"10.3390\/bdcc9050130","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:44:58Z","timestamp":1747212298000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0304-4343","authenticated-orcid":false,"given":"Carlos","family":"Toro","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Universidad Andres Bello, Autopista Concepci\u00f3n-Talcahuano CCP-THNO 7100, Talcahuano 4260000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2049-8451","authenticated-orcid":false,"given":"Walter","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Metallurgical Engineering Department, University of Concepci\u00f3n, Concepci\u00f3n 4070386, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4743-3562","authenticated-orcid":false,"given":"Gonzalo","family":"Reyes","sequence":"additional","affiliation":[{"name":"Metallurgical Engineering Department, University of Concepci\u00f3n, Concepci\u00f3n 4070386, Chile"}]},{"given":"Miguel","family":"Pe\u00f1a","sequence":"additional","affiliation":[{"name":"Metallurgical Engineering Department, University of Concepci\u00f3n, Concepci\u00f3n 4070386, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4414-8843","authenticated-orcid":false,"given":"Nicol\u00e1s","family":"Caselli","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad Andres Bello, Autopista Concepci\u00f3n-Talcahuano CCP-THNO 7100, Talcahuano 4260000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8318-4201","authenticated-orcid":false,"given":"Carla","family":"Taramasco","sequence":"additional","affiliation":[{"name":"Department of Engineering Sciences, Universidad Andres Bello, Vi\u00f1a del Mar 2531015, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5591-3518","authenticated-orcid":false,"given":"Pablo","family":"Orme\u00f1o-Arriagada","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Negocios y Ciencias Agroambientales, Universidad de Vi\u00f1a del Mar, Vi\u00f1a del Mar 2520000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5993-5933","authenticated-orcid":false,"given":"Eduardo","family":"Balladares","sequence":"additional","affiliation":[{"name":"Metallurgical Engineering Department, University of Concepci\u00f3n, Concepci\u00f3n 4070386, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","unstructured":"Schlesinger, M.E., Sole, K.C., Davenport, W.G.I., and Flores, G.R.F.A. (2021). Extractive Metallurgy of Copper, Elsevier."},{"key":"ref_2","unstructured":"Davenport, W.G., Jones, D.M., King, M.J., and Partelpoeg, E.H. (2004). Flash Smelting: Analysis, Control and Optimization, Wiley-Tms. [2nd Revised ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bacedoni, M., Moreno-Ventas, I., and R\u00edos, G. (2020). Copper Flash Smelting Process Balance Modeling. Metals, 10.","DOI":"10.3390\/met10091229"},{"key":"ref_4","unstructured":"Kemori, N., Ojima, Y., and Kondo, Y. (1988). Variation of the Composition and Size of Copper Concentrate Particles in the Reaction Shaft. Proceedings of the Flash Reaction Processes, University of Utah."},{"key":"ref_5","first-page":"C79","article-title":"Oxidation of Chalcopyrite in Simulated Suspension Smelting Conditions","volume":"100","author":"Jokilaakso","year":"1991","journal-title":"Trans. Instit. Min. Metall.(Sect. C Mineral Process. Extr. Metall.)"},{"key":"ref_6","unstructured":"Ahokainen, T., Jokilaakso, A., Vaarno, J., and Jarvi, J. (1997, January 3\u20134). Modelling Chalcopyrite Combustion Together with Fluid Flow Simulation. Proceedings of the International Conference on CFD in the Mineral & Metal Processing and Power Generation, Melbourne, Australia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s11663-018-1183-1","article-title":"Evolution of Size and Chemical Composition of Copper Concentrate Particles Oxidized Under Simulated Flash Smelting Conditions","volume":"49","author":"Parra","year":"2018","journal-title":"Met. Mater. Trans. B"},{"key":"ref_8","first-page":"C1","article-title":"Combustion of Pyrite Concentrate under Simulated Flash-Smelting Conditions","volume":"90","author":"Jorgensen","year":"1981","journal-title":"Trans. Inst. Mining Metall."},{"key":"ref_9","first-page":"37","article-title":"Single-Particle Combustion of Chalcopyrite","volume":"288","author":"Jorgensen","year":"1983","journal-title":"Proc. Australas. Inst. Min. Metall."},{"key":"ref_10","unstructured":"Jorgensen, F.R.A., Moyle, F.J., and Wadsley, M.W. (1989). Structural Changes Associated with the Ignition of Pyrite and Chalcopyrite during Flash Smelting. Process Mineralogy IX: Applications to Mineral Beneficiation, Metallurgy, Gold, Diamonds, Ceramics, Environment and Health, Minerals, Metals & Materials Society."},{"key":"ref_11","unstructured":"Kemori, N., Akada, A., and Kondo, Y. (1991). Development of a Concentrate Burner for Industrial Oxygen Flash Smelting. Copper 91, Pyrometallurgy of Copper, Pergamon Press."},{"key":"ref_12","first-page":"631","article-title":"Operation Optimization of Concentrate Burner in Copper Flash Smelting Furnace","volume":"14","author":"Chen","year":"2004","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_13","unstructured":"Jorgensen, F.R.A., Campbell, A., Taylor, R., and Washington, B. (2005, January 7\u20138). Sampling and Measurements in the Reaction Shaft at Olympic Dam. Proceedings of the First Extractive Metallurgy Operators\u2019 Conference, Melbourne, Australia."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1179\/cmq.2004.43.4.561","article-title":"Dynamic Simulation of a Flash Furnace","volume":"43","author":"Parada","year":"2004","journal-title":"Can. Metall. Q."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1016\/j.apm.2006.03.017","article-title":"CFD Modelling of the Flow and Reactions in the Olympic Dam Flash Furnace Smelter Reaction Shaft","volume":"30","author":"Solnordal","year":"2006","journal-title":"Appl. Math. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1088\/0022-3735\/18\/6\/006","article-title":"Two-Colour Pyrometer Measurement of the Temperature of Individual Combusting Particles","volume":"18","author":"Jorgensen","year":"1985","journal-title":"J. Phys. E Sci. Instrum."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/BF02654095","article-title":"Two-Wavelength Pyrometry Study of the Combustion of Sulfide Minerals: Part I. Apparatus and General Observations","volume":"26","author":"Tuffrey","year":"1995","journal-title":"Metall. Materi. Trans. B"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/BF02915042","article-title":"Kinetics of the Flash Converting of MK (Chalcocite) Concentrate","volume":"27","author":"Morgan","year":"1996","journal-title":"Met. Mater. Trans B"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11663-005-0021-4","article-title":"Pyrometric temperature and size measurements of chalcopyrite particles during flash oxidation in a laminar flow reactor","volume":"36","author":"Laurila","year":"2005","journal-title":"Metall. Materi. Trans. B"},{"key":"ref_20","unstructured":"Reyes, G. (2021). Desarrollo y Aplicaci\u00f3n de un Sensor Optoelectr\u00f3nico Para el Monitoreo de la Operaci\u00f3n de un Horno de Fusi\u00f3n Flash de Concentrados de Cobre. [Ph.D. Thesis, Universidad de Concepci\u00f3n]. Available online: https:\/\/repositorio.udec.cl\/items\/189dfe19-e58e-4aae-a714-e032da37799f."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"D\u00edaz, W., Toro, C., Balladares, E., Parra, V., Coelho, P., Reyes, G., and Parra, R. (2019). Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend. Metals, 9.","DOI":"10.3390\/met9091017"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Toro, C., Torres, S., Parra, V., Fuentes, R., Castillo, R., D\u00edaz, W., Reyes, G., Balladares, E., and Parra, R. (2020). On the Detection of Spectral Emissions of Iron Oxides in Combustion Experiments of Pyrite Concentrates. Sensors, 20.","DOI":"10.3390\/s20051284"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1039\/D0AN01483D","article-title":"Machine learning for recognizing minerals from multispectral data","volume":"146","author":"Jahoda","year":"2021","journal-title":"Analyst"},{"key":"ref_24","first-page":"1979","article-title":"NIR-Spectroscopy and Machine Learning Models to Pre-concentrate Copper Hosted Within Sedimentary Rocks","volume":"41","author":"Elbasbas","year":"2024","journal-title":"Min. Metall. Explor."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116903","DOI":"10.1016\/j.geoderma.2024.116903","article-title":"On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning","volume":"446","author":"Kok","year":"2024","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15807","DOI":"10.1038\/s41598-025-92686-2","article-title":"Interpretable Machine Learning Models Classify Minerals, Spectroscopy","volume":"15","author":"Smith","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19330","DOI":"10.1021\/acs.analchem.4c03107","article-title":"Mineralogical Analysis of Solid-Sample Flame Emission Spectra by Machine Learning","volume":"96","author":"Bernicky","year":"2024","journal-title":"Anal. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105202","DOI":"10.1016\/j.chemolab.2024.105202","article-title":"A 1D-CNN model for the early detection of citrus Huanglongbing disease in the sieve plate of phloem tissue using micro-FTIR","volume":"252","author":"Yang","year":"2024","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109122","DOI":"10.1016\/j.compag.2024.109122","article-title":"Variety classification and identification of jujube based on near-infrared spectroscopy and 1D-CNN","volume":"223","author":"Li","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s11063-021-10652-1","article-title":"One-Dimensional Deep Convolutional Neural Network for Mineral Classification from Raman Spectroscopy","volume":"54","author":"Sang","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102499","DOI":"10.1016\/j.apmt.2024.102499","article-title":"Deep learning assisted Raman spectroscopy for rapid identification of 2D materials","volume":"41","author":"Qi","year":"2024","journal-title":"Appl. Mater. Today"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s00424-024-02997-y","article-title":"Explainable artificial intelligence for spectroscopy data: A review","volume":"477","author":"Contreras","year":"2024","journal-title":"Pflug. Arch. Eur J Physiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118254","DOI":"10.1016\/j.trac.2025.118254","article-title":"Interpretability in near-infrared (NIR) spectroscopy: Current pathways to the long-standing challenge","volume":"189","author":"Grabska","year":"2025","journal-title":"TrAC-Trend. Anal. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"125242","DOI":"10.1016\/j.saa.2024.125242","article-title":"Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks","volume":"326","author":"Xia","year":"2025","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9959","DOI":"10.1021\/acs.analchem.3c01101","article-title":"1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis","volume":"95","author":"Shi","year":"2023","journal-title":"Anal. Chem."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105777","DOI":"10.1016\/j.infrared.2025.105777","article-title":"Using a one-dimensional convolutional neural network on FTIR spectroscopy to measure the thickness of composite plastic films","volume":"147","author":"Wang","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szeghalmy, S., and Fazekas, A. (2023). A Comparative Study of the use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors, 23.","DOI":"10.3390\/s23042333"},{"key":"ref_39","first-page":"1","article-title":"Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization","volume":"18","author":"Li","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zeng, F., Peng, W., Kang, G., Feng, Z., and Yue, X. (2021, January 29\u201331). Spectral Data Classification by One-Dimensional Convolutional Neural Networks. Proceedings of the 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), Austin, TX, USA.","DOI":"10.1109\/IPCCC51483.2021.9679444"},{"key":"ref_41","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_42","unstructured":"Toro, C. (2025). Dataset of Spectral Emission of Mineral Species during Combustion. Figshare Dataset."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., and Driscoll, R.L. (2017). USGS Spectral Library Version 7 Data: U.S. Geological Survey Data Release.","DOI":"10.3133\/ds1035"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kiranyas, S., Abdeljaber, O., Avci, O., and Gabbouj, M. (2019, January 12\u201317). 1-D Convolutional Neural Networks for Signal Processing Applications. Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682194"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gole, J.L. (1992). Toward the Modeling of the Oxidation of Small Metal and Metalloid Molecules. Gas Phase Metal Reactions, Elsevier.","DOI":"10.1016\/B978-0-444-89070-2.50027-X"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/5\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:39Z","timestamp":1760031159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/5\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["bdcc9050130"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9050130","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}