How to Read and Interpret FTIR Spectra for Materials: A Master Dataset with Step-by-Step Guided PeakCorrelation Analysis, Representative Examples, and a Foundation for Future Artificial Intelligence (AI)-Assisted Analysis
Keywords:
Artificial intelligence, Chemistry, Dataset, FTIR, Materials, OrganicAbstract
Fourier Transform Infrared (FTIR) spectroscopy is widely
used for materials characterization; however, spectrum
interpretation often relies on isolated peak identification,
which can lead to ambiguity, especially for complex
materials. This study presents a master FTIR dataset
combined with a step-by-step guided peak-correlation
workflow to support systematic and reproducible FTIR
interpretation. The dataset organizes FTIR information into
five spectral regions and emphasizes correlated peak
families rather than individual bands. Representative
examples covering simple compounds, organic compounds,
polymers, and halogenated materials demonstrate the
applicability of the approach across materials science and
chemical engineering fields. Beyond manual interpretation,
the structured dataset and workflow are designed to be
machine-readable and extensible, enabling future
integration with data-driven methods such as artificial
intelligence (AI) and machine learning (ML) for automated
spectral analysis. This work provides a practical reference for
FTIR interpretation, education, and the development of
intelligent materials characterization systems
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