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Updated: Sep 3, 2025
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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
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Performance Improvement of NIR Spectral Pattern Recognition from Three Compensation Models' Voting and Multi-Modal
Niangen Ye1, Sheng Zhong1, Zile Fang1
1Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
Molecules (Basel, Switzerland)
|July 27, 2022
Summary
Near-infrared (NIR) spectroscopy effectively distinguished drinking water categories using multi-modal fusion and game theory compensation models. This approach achieved 95.5% accuracy, enhancing spectral pattern recognition for water quality analysis.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Chemometrics
Background:
- Optical path length is a perturbation factor in aquaphotomics-inspired measurements.
- Near-infrared (NIR) spectroscopy is suitable for analyzing water properties.
Purpose of the Study:
- To discriminate between three categories of drinking water using NIR spectroscopy.
- To enhance discrimination accuracy through multi-modal fusion and game theory strategies.
Main Methods:
- Applied multi-measurement modal NIR spectroscopy.
- Utilized Moving Window-k Nearest Neighbor (MW-kNN) and Norris derivative filter for modeling.
- Developed two-category priority compensation and three-model voting strategies with multi-modal fusion.
- Employed Moving Window Correlation Coefficient (MWCC) and k-shortest distances for spectral analysis.
Main Results:
- Optimal MW-kNN and compensation models were determined for three measurement modals (1 mm, 4 mm, 10 mm).
- Joint models with multi-modal fusion outperformed single-modal fusion and sub-models.
- The best joint model achieved a 95.5% validation recognition accuracy by fusing long-wave (1 mm) and short-wave (10 mm) models.
Conclusions:
- Compensation models' voting and multi-modal fusion significantly improve NIR spectral pattern recognition performance.
- The proposed strategy effectively enhances the discrimination of drinking water categories.
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