Maximum likelihood estimation based regression for multivariate calibration.

Area of Science:

• Chemometrics
• Spectroscopy
• Data analysis

Background:

• Multivariate calibration is crucial for quantitative analysis in various scientific fields.
• Traditional methods like least-squares can be sensitive to noise and outliers in spectral data.
• Partial Least Squares (PLS) is a common but sometimes suboptimal approach for complex spectral datasets.

Purpose of the Study:

• To introduce a novel Maximum Likelihood Estimation based Regression (MLER) model for enhanced multivariate calibration.
• To demonstrate the superior robustness and accuracy of MLER compared to existing methods.
• To provide an efficient computational approach for solving the MLER model.

Main Methods:

• Development of a Maximum Likelihood Estimation (MLE) based regression model (MLER).
• Implementation of an iteratively reweighted least squares technique for efficient MLER model solving.
• Validation using three real-world near-infrared (NIR) spectra datasets.

Main Results:

• The MLER model demonstrates significantly higher accuracy and robustness against noise and outliers.
• MLER effectively establishes precise spectra-concentrate relationships.
• Experimental results show MLER outperforms state-of-the-art Partial Least Squares (PLS) methods on NIR data.

Conclusions:

• The proposed MLER model offers a more effective and accurate solution for multivariate calibration, particularly for spectral data.
• MLER provides a robust alternative to traditional least-squares and PLS methods.
• The efficient solution technique enables practical application of MLER in chemometrics and related fields.