Systematic Feature and Method Selection for Two-Phase Flow Classification Using Multi-Wavelength NIR Sensors

Systematic Feature and Method Selection for Two-Phase Flow Classification Using Multi-Wavelength NIR Sensors | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 September 2025 V1 Latest version Share on Systematic Feature and Method Selection for Two-Phase Flow Classification Using Multi-Wavelength NIR Sensors Authors : Thiago Martins 0000-0002-3655-2777 [email protected] , João P Bedretchuk , Victor N Kürschner , Raphael D Comesanha , Anderson Wedderhoff Spengler , Jorge Luiz , and Kleber Vieira De Paiva Authors Info & Affiliations https://doi.org/10.22541/au.175873362.20709872/v1 Published IEEE Access Version of record Peer review timeline 210 views 125 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurate classification of two-phase gas-liquid flows is critical for the optimization and safety of industrial processes. This work presents the Multi-Feature Multi-Domain (MFMD) methodology, a systematic framework for flow pattern classification in horizontal air-water flows. Five representative patterns-bubble, plug, slug, stratified, and wave-were identified using collimated, non-intrusive nearinfrared (NIR) optical sensors. The approach relies on the fusion of data from three discrete laser wavelengths (850 nm, 980 nm, and 1310 nm), selected for their distinct absorption characteristics in water and sensitivity to interfacial, hybrid, and volumetric features. Features were extracted from the time (Probability Density Function, PDF), frequency (Power Spectral Density, PSD), and time-frequency (Discrete Wavelet Transform, DWT) domains. We compared an interpretable feature selection strategy, Recursive Feature Elimination (RFE), with two transformation methods: supervised Linear Discriminant Analysis (LDA) and unsupervised Principal Component Analysis (PCA). Classification was performed using Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). Results show that combining sensors at the suggested wavelengths is crucial for high performance. The RFE approach achieved 100% accuracy under the evaluated conditions when merging features from all data sources. Moreover, efficient models were obtained by fusing signals across wavelengths regardless of the reduction method. This work culminates in the Optimal Feature and Method Selection (OFMS) guide, which synthesizes the findings into a practical tool for selecting effective strategies while balancing maximum accuracy and model parsimony. Supplementary Material File (ieee_access___artigo_mfmd___ofms_multi_wavelengths (2).pdf) Download 4.11 MB Information & Authors Information Version history V1 Version 1 24 September 2025 Peer review timeline Published IEEE Access Version of Record 1 Jan 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords feature selection flow pattern identification index terms dimensionality reduction machine learning multi-domain non-intrusive sensing Authors Affiliations Thiago Martins 0000-0002-3655-2777 [email protected] Electrical Engineering Department, Federal University of Santa Catarina View all articles by this author João P Bedretchuk Electrical Engineering Department, Federal University of Santa Catarina View all articles by this author Victor N Kürschner Electrical Engineering Department, Federal University of Santa Catarina View all articles by this author Raphael D Comesanha Electrical Engineering Department, Federal University of Santa Catarina View all articles by this author Anderson Wedderhoff Spengler Electrical Engineering Department, Federal University of Santa Catarina View all articles by this author Jorge Luiz Mechanical Engineering Department, Federal University of Santa Catarina View all articles by this author Kleber Vieira De Paiva Mobility Engineering Department, Federal University of Santa Catarina View all articles by this author Metrics & Citations Metrics Article Usage 210 views 125 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Thiago Martins, João P Bedretchuk, Victor N Kürschner, et al. Systematic Feature and Method Selection for Two-Phase Flow Classification Using Multi-Wavelength NIR Sensors. Authorea . 24 September 2025. DOI: https://doi.org/10.22541/au.175873362.20709872/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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