2025/04/24 by Dirk Lellinger, James Thomson, Nicolás Coca-López +7 · 1 voice
Chemistry · Biochemistry, Genetics and Molecular Biology · Environmental Science · #Spectroscopy and Chemometric Analyses #Spectroscopy Techniques in Biomedical and Chemical Research #Water Quality Monitoring and Analysis
paper · doi:10.1177/00037028251330654
openalex publication_date 2025/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Raman spectroscopy is a powerful characterization technique with increasing applications that would greatly benefit from data harmonization. Several standards deal with calibration in Raman spectroscopy, but no detailed procedure covers the complete calibration of an instrument, including both spectral axes, from reference material spectra generation to data processing. Moreover, the type of reference materials, the quality of the recorded spectra and the choice of the fitting functions are critical for obtaining precise and reliable reference data for calibration. This report describes the challenges and importance of peak fitting for Raman signal calibration based on an interlaboratory study with 10 different instruments. Spectra of neon emission, silicon, calcite, and polystyrene were fitted using common peak shapes, observing that Gaussian, Pearson IV, Voigt, and Voigt shapes are preferred for these materials, respectively. An analysis of the effect on the fitting of the signal-to-noise ratio (S/N) recommends a minimum value of 100 for a Raman peak if it should be used to calibrate a Raman instrument. Some factors that might affect the peak shape of the Raman signal, such as the physical and chemical properties of the sample, the nature of the electronic transitions, the instrument response and the spectral resolution are discussed. The results highlight the role of peak fitting analysis in improving the quality and reliability of Raman spectra calibration and, thus, enhancing data transfer and comparability, especially for handheld and portable Raman analyzers, as well as applications based on quantification, multivariate data analysis, and other complex processing steps.