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Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman
Biao Lu1, Shilong Wu2, Deliang Liu1
1School of Information and Engineering, Suzhou University, Suzhou 234000, China.
Molecules (Basel, Switzerland)
|April 13, 2024
Summary
Raman spectroscopy combined with machine learning accurately analyzes methanol-gasoline blends. This method precisely classifies blend types and quantifies methanol content, offering a reliable approach for fuel quality assessment.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Machine Learning
Background:
- Methanol-gasoline blends are promoted as eco-friendly biofuels.
- Methanol content critically impacts blend quality and combustion.
- Accurate analysis methods are essential for quality control.
Purpose of the Study:
- To perform qualitative and quantitative analysis of methanol-gasoline blends.
- To evaluate Raman spectroscopy coupled with machine learning for this purpose.
- To develop and compare advanced analytical models.
Main Methods:
- Configured methanol-gasoline blends with varying concentrations.
- Employed Raman spectroscopy for data acquisition.
- Utilized partial least squares discriminant analysis (PLS-DA) for classification.
- Developed a consensus model integrating self-organizing mapping (SOM) neural networks for quantitative prediction.
Main Results:
- PLS-DA achieved nearly 100% accuracy in classifying blend categories.
- The unsupervised consensus model significantly outperformed PLS and UVE-PLS for quantitative analysis.
- Correlation coefficients for methanol content prediction consistently exceeded 0.98.
Conclusions:
- Raman spectroscopy is a suitable technique for both qualitative and quantitative analysis of methanol-gasoline blends.
- The developed consensus model offers superior accuracy in predicting methanol content.
- Raman spectroscopy is poised for increased application in fuel composition analysis.
Keywords:
Raman spectroscopyconsensus modelmethanol–gasoline blendspartial least squares discriminant analysis (PLS-DA)self-organizing mapping (SOM)More Related Videos
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