A pre-training enhanced deep learning framework for robust sparse unmixing in chemical imaging
Chemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variab...
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| Vydané v: | Analytica chimica acta Ročník 1374; s. 344524 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
08.11.2025
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| ISSN: | 0003-2670, 1873-4324, 1873-4324 |
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| Abstract | Chemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variability. While deep learning models have shown promise in jointly estimating endmember spectra and abundances, the increased complexity and parameters make them highly sensitive to initialization, often resulting in unstable performance. Therefore, developing accurate and robust unmixing algorithms is critical for reliable chemical imaging analysis.
We propose a versatile pre-training framework to enhance sparse unmixing (P4SU) for chemical imaging. P4SU leverages simulated spectra generated from spectral libraries to pre-train deep learning models, followed by fine-tuning on target chemical imaging data. To accommodate a range of mixing scenarios, we equip the framework with both linear and nonlinear decoder options. Evaluated across three chemically diverse datasets—pigment mixtures, sugar solutions, and pharmaceutical tablets—P4SU demonstrates superior accuracy and stability compared to conventional methods (e.g., SUnSAL, CLSUnSAL) and non-pre-trained models. Significantly, pre-trained models reduce the root mean square error (RMSE) by 15–32 % compared to non-pre-trained ones and lower the standard deviation of results by 90–98 % on the pigment dataset.
The method's robustness to spectral variability and noise demonstrates its potential for rapid, reliable chemical analysis in quality control and material identification. Implemented as an open-source Python toolkit (available at https://github.com/Ryan21wy/P4SU), P4SU offers a practical solution to streamline analytical workflows in chemical imaging analysis.
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•A pretraining framework tailored for sparse unmixing (P4SU) was proposed for robust and accurate chemical imaging unmixing.•Leveraging simulated spectra, P4SU overcomes deep learning models' sensitivity to initial parameter settings.•Combined with linear and nonlinear decoders, pretrained models exhibit improvements across diverse unmixing scenarios.•P4SU provides a versatile, open-source solution for rapid chemical analysis in quality control and heritage conservation. |
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| AbstractList | Chemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variability. While deep learning models have shown promise in jointly estimating endmember spectra and abundances, the increased complexity and parameters make them highly sensitive to initialization, often resulting in unstable performance. Therefore, developing accurate and robust unmixing algorithms is critical for reliable chemical imaging analysis.
We propose a versatile pre-training framework to enhance sparse unmixing (P4SU) for chemical imaging. P4SU leverages simulated spectra generated from spectral libraries to pre-train deep learning models, followed by fine-tuning on target chemical imaging data. To accommodate a range of mixing scenarios, we equip the framework with both linear and nonlinear decoder options. Evaluated across three chemically diverse datasets—pigment mixtures, sugar solutions, and pharmaceutical tablets—P4SU demonstrates superior accuracy and stability compared to conventional methods (e.g., SUnSAL, CLSUnSAL) and non-pre-trained models. Significantly, pre-trained models reduce the root mean square error (RMSE) by 15–32 % compared to non-pre-trained ones and lower the standard deviation of results by 90–98 % on the pigment dataset.
The method's robustness to spectral variability and noise demonstrates its potential for rapid, reliable chemical analysis in quality control and material identification. Implemented as an open-source Python toolkit (available at https://github.com/Ryan21wy/P4SU), P4SU offers a practical solution to streamline analytical workflows in chemical imaging analysis.
[Display omitted]
•A pretraining framework tailored for sparse unmixing (P4SU) was proposed for robust and accurate chemical imaging unmixing.•Leveraging simulated spectra, P4SU overcomes deep learning models' sensitivity to initial parameter settings.•Combined with linear and nonlinear decoders, pretrained models exhibit improvements across diverse unmixing scenarios.•P4SU provides a versatile, open-source solution for rapid chemical analysis in quality control and heritage conservation. Chemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variability. While deep learning models have shown promise in jointly estimating endmember spectra and abundances, the increased complexity and parameters make them highly sensitive to initialization, often resulting in unstable performance. Therefore, developing accurate and robust unmixing algorithms is critical for reliable chemical imaging analysis. We propose a versatile pre-training framework to enhance sparse unmixing (P4SU) for chemical imaging. P4SU leverages simulated spectra generated from spectral libraries to pre-train deep learning models, followed by fine-tuning on target chemical imaging data. To accommodate a range of mixing scenarios, we equip the framework with both linear and nonlinear decoder options. Evaluated across three chemically diverse datasets-pigment mixtures, sugar solutions, and pharmaceutical tablets-P4SU demonstrates superior accuracy and stability compared to conventional methods (e.g., SUnSAL, CLSUnSAL) and non-pre-trained models. Significantly, pre-trained models reduce the root mean square error (RMSE) by 15-32 % compared to non-pre-trained ones and lower the standard deviation of results by 90-98 % on the pigment dataset. The method's robustness to spectral variability and noise demonstrates its potential for rapid, reliable chemical analysis in quality control and material identification. Implemented as an open-source Python toolkit (available at https://github.com/Ryan21wy/P4SU), P4SU offers a practical solution to streamline analytical workflows in chemical imaging analysis. Chemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variability. While deep learning models have shown promise in jointly estimating endmember spectra and abundances, the increased complexity and parameters make them highly sensitive to initialization, often resulting in unstable performance. Therefore, developing accurate and robust unmixing algorithms is critical for reliable chemical imaging analysis.BACKGROUNDChemical imaging techniques, such as hyperspectral and Raman imaging, provide spatially resolved spectral insights for non-destructive analysis of complex mixtures. However, accurately decomposing mixed pixel spectra into constituent components remains challenging due to spectral overlaps and variability. While deep learning models have shown promise in jointly estimating endmember spectra and abundances, the increased complexity and parameters make them highly sensitive to initialization, often resulting in unstable performance. Therefore, developing accurate and robust unmixing algorithms is critical for reliable chemical imaging analysis.We propose a versatile pre-training framework to enhance sparse unmixing (P4SU) for chemical imaging. P4SU leverages simulated spectra generated from spectral libraries to pre-train deep learning models, followed by fine-tuning on target chemical imaging data. To accommodate a range of mixing scenarios, we equip the framework with both linear and nonlinear decoder options. Evaluated across three chemically diverse datasets-pigment mixtures, sugar solutions, and pharmaceutical tablets-P4SU demonstrates superior accuracy and stability compared to conventional methods (e.g., SUnSAL, CLSUnSAL) and non-pre-trained models. Significantly, pre-trained models reduce the root mean square error (RMSE) by 15-32 % compared to non-pre-trained ones and lower the standard deviation of results by 90-98 % on the pigment dataset.RESULTSWe propose a versatile pre-training framework to enhance sparse unmixing (P4SU) for chemical imaging. P4SU leverages simulated spectra generated from spectral libraries to pre-train deep learning models, followed by fine-tuning on target chemical imaging data. To accommodate a range of mixing scenarios, we equip the framework with both linear and nonlinear decoder options. Evaluated across three chemically diverse datasets-pigment mixtures, sugar solutions, and pharmaceutical tablets-P4SU demonstrates superior accuracy and stability compared to conventional methods (e.g., SUnSAL, CLSUnSAL) and non-pre-trained models. Significantly, pre-trained models reduce the root mean square error (RMSE) by 15-32 % compared to non-pre-trained ones and lower the standard deviation of results by 90-98 % on the pigment dataset.The method's robustness to spectral variability and noise demonstrates its potential for rapid, reliable chemical analysis in quality control and material identification. Implemented as an open-source Python toolkit (available at https://github.com/Ryan21wy/P4SU), P4SU offers a practical solution to streamline analytical workflows in chemical imaging analysis.SIGNIFICANCEThe method's robustness to spectral variability and noise demonstrates its potential for rapid, reliable chemical analysis in quality control and material identification. Implemented as an open-source Python toolkit (available at https://github.com/Ryan21wy/P4SU), P4SU offers a practical solution to streamline analytical workflows in chemical imaging analysis. |
| ArticleNumber | 344524 |
| Author | Xie, Ting Wang, Yue Sun, Jinyu Zhang, Zhimin Lu, Hongmei Zhang, Hailiang Tan, Lin Liu, Anqi |
| Author_xml | – sequence: 1 givenname: Yue surname: Wang fullname: Wang, Yue – sequence: 2 givenname: Anqi surname: Liu fullname: Liu, Anqi – sequence: 3 givenname: Lin surname: Tan fullname: Tan, Lin – sequence: 4 givenname: Jinyu surname: Sun fullname: Sun, Jinyu – sequence: 5 givenname: Hailiang surname: Zhang fullname: Zhang, Hailiang – sequence: 6 givenname: Ting surname: Xie fullname: Xie, Ting – sequence: 7 givenname: Zhimin orcidid: 0000-0002-4167-4234 surname: Zhang fullname: Zhang, Zhimin email: zmzhang@csu.edu.cn – sequence: 8 givenname: Hongmei surname: Lu fullname: Lu, Hongmei email: hongmeilu@csu.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40983411$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning Pre-training Sparse unmixing Raman imaging Hyperspectral imaging |
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