Baseline matching preprocessing of temperature perturbation infrared spectra

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Sequential infrared (IR) spectral data often suffer from baseline drift and sloping baselines.
• These baseline issues can obscure subtle spectral features and hinder accurate quantitative analysis.
• Existing methods may not adequately address drift in large spectral datasets.

Purpose of the Study:

• To describe and validate an infrared spectrum baseline matching procedure.
• To compensate for measurement drift and eliminate sloping baselines in sequentially acquired spectra.
• To demonstrate the procedure's utility in revealing subtle spectral changes not apparent in overlaid spectra.

Main Methods:

• Development of a baseline matching procedure based on theoretical principles.
• Implementation of the procedure using three distinct infrared spectrum datasets.
• Acquisition of sequential spectra including no sample, polymer powder, and polymer film.
• Application of the method to datasets with 100+ spectra, including temperature-dependent measurements.

Main Results:

• The procedure effectively compensates for short- and long-term measurement drifts.
• Baseline matching revealed subtle temperature-dependent changes in a poly(styrene) film.
• These changes were not evident when comparing overlaid spectra.
• Instrument reproducibility was characterized using polymer powder and film datasets.

Conclusions:

• The described baseline matching procedure is a robust method for preprocessing sequential IR spectra.
• It significantly improves the detection of subtle spectral variations, particularly in temperature-dependent studies.
• The technique is easily implementable for large spectral datasets using macro programming.