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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
[Near-infrared spectral quantitative analysis by combining classification with local PLS]
1State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|March 3, 2009
Summary
A new hybrid algorithm, H_PLS, improves near-infrared (NIR) spectral quantitative analysis accuracy. It combines classification-based and total sample models, enhancing predictions even with classification errors.
Area of Science:
- Analytical Chemistry
- Spectroscopy
Context:
- Near-infrared (NIR) spectroscopy is a rapid analytical technology with growing applications.
- Accurate quantitative analysis using NIR spectra is crucial for various industries.
- Existing methods face challenges with classification errors impacting model accuracy.
Purpose:
- To develop a robust quantitative analysis method for NIR spectroscopy.
- To mitigate the negative impact of classification failures on predictive accuracy.
- To propose a novel hybrid algorithm, H_PLS, for improved NIR spectral analysis.
Summary:
- A hybrid partial least squares (H_PLS) algorithm is introduced, integrating a classification-based local PLS (C_PLS) and a total sample local PLS (D_PLS).
- H_PLS compares predictions from C_PLS and D_PLS to determine the final property value.
- Experimental results on gasoline samples demonstrate H_PLS enhances prediction accuracy (0.9858) compared to D_PLS (0.9734) and C_PLS (0.9656), especially when classification errors occur.
Impact:
- The H_PLS algorithm offers improved accuracy and robustness in NIR spectral quantitative analysis.
- This method provides a more reliable approach for analyzing complex samples where classification might be imperfect.
- The findings contribute to advancing the precision of rapid analytical techniques in chemical analysis.
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