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TwinSpectra-Spectroscopic scans of refuse-derived fuel particles
Jonas Fischer1, Mina Bikhit2, Łukasz Wrześniowski2
1Ruhr-Universität Bochum, Universitätsstraße 150, D-44780 Bochum, Germany.
Data in Brief
|June 15, 2026
Summary
TwinSpectra introduces spectroscopic data from refuse-derived fuel particles in cement manufacturing. This dataset supports machine learning for sustainable cement production and advanced spectroscopic data analysis.
Area of Science:
- Materials Science
- Spectroscopy
- Chemical Engineering
Background:
- Cement manufacturing generates refuse-derived fuel (RDF) requiring characterization.
- Spectroscopic analysis offers a non-destructive method for material identification.
- Existing datasets may lack the dual-spectroradiometer approach for comprehensive analysis.
Purpose of the Study:
- To present the novel TwinSpectra dataset, comprising dual-spectroradiometer measurements of RDF particles.
- To establish a foundation for developing machine learning models for RDF analysis in cement production.
- To facilitate research into high-dimensional, sequential spectroscopic data processing.
Main Methods:
- Acquisition of material reflectance spectra using two distinct spectroradiometers.
- Sampling of 120 RDF particles across six material groups.
- Rigorous data preprocessing, cleansing, and calibration procedures.
- Assignment of all 1438 readouts to their respective material classes.
Main Results:
- Compilation of two preprocessed datasets (718 and 720 measurements).
- A total of 1438 spectroscopic readouts from 120 RDF particle samples.
- Successful classification of all measurements into six distinct material groups.
Conclusions:
- The TwinSpectra dataset provides a robust resource for advancing RDF analysis in cement manufacturing.
- The data is suitable for developing and validating machine learning algorithms for process automation.
- This work contributes to the field of high-dimensional spectroscopic data analysis.
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