Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Quantitative spectroscopy relies on accurate regression models for data analysis.
• Current methods for spectroscopic model development can be complex and labor-intensive.
• Automation in spectroscopic model development is crucial for efficiency and broader application.

Purpose of the Study:

• To introduce a novel, automated modeling procedure for quantitative spectroscopy.
• To develop a robust method applicable to various spectroscopic data types.
• To achieve regression model quality comparable to or exceeding current techniques.

Main Methods:

• Introduced Iterative Regression of Corrective Baselines (IRCB) for model development.
• Employed a matrix transformation to linearize spectroscopic data (X to X_).
• Utilized ordinary least-squares regression for feature ranking and selection from the transformed matrix.

Main Results:

• IRCB demonstrated effectiveness on Fourier transform infrared (FTIR) spectroscopy data.
• The method was successfully applied to near-infrared spectroscopy (NIR) soil composition challenge data.
• IRCB showed comparable or superior performance against benchmark results for Raman spectroscopy data.

Conclusions:

• IRCB offers a powerful and automated approach for quantitative spectroscopic model development.
• The method is versatile, applicable across different spectroscopic techniques and data complexities.
• IRCB simplifies the modeling process without requiring additional data preprocessing.