What question did this study set out to answer?

To quantify the effects of functional groups on carbonyl peak shifts in infrared spectra using machine learning methods.

Machine Learning-Enabled Quantification of Characteristic Carbonyl Peak Shifts in Infrared Spectra: An Instrumental Analysis Laboratory Experiment for Upper-Division Undergraduates

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To quantify the effects of functional groups on carbonyl peak shifts in infrared spectra using machine learning methods.
Developed a spectral database from IR spectroscopy experiments.
Constructed machine learning models to predict peak shifts.
Validated model predictions with experimental data.
Conducted SHAP analysis for interpretability of results.
Successfully quantified the influence of various functional groups on carbonyl peak shifts.
Students demonstrated improved understanding of spectra-structure correlations through the integration of machine learning.

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Educational study combines machine learning with infrared spectroscopy, enhancing understanding of spectral shifts in undergraduates.

Zhou et al. (2026) studied this question.

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