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

## TL;DR

This paper introduces a new model called IRCB for quantitative spectroscopy that simplifies and automates the development of regression models for complex spectral data.

## Contribution

The novel IRCB method enables automated regression modeling for spectroscopy without preprocessing, using matrix transformation and feature selection.

## Key findings

- IRCB produces regression models with quality comparable to or better than existing methods for various spectroscopic data.
- The method was successfully applied to FTIR, NIR, and Raman spectroscopy in case studies.
- The workflow is demonstrated to be effective for both synthetic and real-world spectral datasets.

## Abstract

In this work, a new model with broad utility for quantitative
spectroscopy
development is reported. A primary objective of this work is to create
a novel modeling procedure that may allow for higher automation of
the model development process. The fundamental concept is simple yet
powerful even for complex spectra and is employed with no additional
preprocessing. This approach is applicable for several types of spectroscopic
data to develop regression models that have similar or greater quality
than the current methods. The key modeling steps are a matrix transformation
and subsequent feature selection process that are collectively referred
to as iterative regression of corrective baselines (IRCB). The transformed
matrix (Xtransform) is a linearized
form of the original X data set. Features from Xtransform that are predictive
of Y can be ranked and selected by ordinary least-squares
regression. The best features (rows of Xtransform) are linear depictions of Y that can be utilized to develop regression models with several machine
learning models. The IRCB workflow is first detailed by using a case
study of Fourier transform infrared (FTIR) spectroscopy for prepared
solutions of a three-component mixture. Next, IRCB is applied and
compared to benchmark results for the 2006 “Chimiométrie”
near-infrared spectroscopy (NIR) soil composition challenge and Raman
measurements of a simulated nuclear waste slurry.