Computationally efficient curve-fitting procedure for large two-dimensional experimental infrared spectroscopic...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemical Engineering

Background:

• Quantitative analysis of large spectral datasets is challenging.
• Time-series spectroscopy generates complex two-dimensional (2D) data.
• Efficient data processing is crucial for real-time monitoring.

Purpose of the Study:

• To develop an efficient curve-fitting procedure for quantitative analysis of 2D time-series spectral data.
• To enhance computational efficiency in spectral data modeling.
• To apply the method to real-world chemical reaction monitoring.

Main Methods:

• Utilized a least-squares approach to minimize differences between experimental and fitted spectra.
• Employed the Pearson VII model as the band-shape function for infrared absorbance spectra.
• Developed an iterative curve-fitting strategy using initial guesses from neighboring spectra for enhanced efficiency.

Main Results:

• Successfully modeled a 2D experimental spectroscopic time-series array from a rhodium-catalyzed hydroformylation reaction.
• Demonstrated significant increases in computational efficiency through iterative parameter optimization.
• The method rapidly and efficiently modeled the entire 2D spectral array.

Conclusions:

• The developed curve-fitting procedure is highly effective for massive time-series spectroscopic data.
• This method shows promise for on-line process monitoring in specialty chemical and pharmaceutical syntheses.
• Efficient quantitative information extraction from complex spectral data is achievable.