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A recap of a few interviews Spectroscopy editors have conducted with key opinion leaders at the forefront of technique innovation in 2025. These experts share their views on technological breakthroughs, analytical challenges, and the trends poised to redefine how we interrogate matter at every scale.

2025 Technology Trends in Artificial Intelligence for Spectroscopy © nuddss -chronicles-stock.adobe.com

Artificial intelligence is transforming vibrational spectroscopy by automating calibration, feature extraction, and interpretation across Raman, infrared, near-infrared (NIR), and hyperspectral imaging (HSI) systems. This review of articles highlighted in Spectroscopy during 2025 captures several major developments, spanning data fusion, spectral imaging, and industrial and biomedical applications.

In this part of our ongoing review of the infrared spectra of carbonyl-containing functional groups, we will study the spectra of esters and carbonates. Esters are ubiquitous in our food and medicines, and polymeric carbonates form an important part of the materials around us. As always, concepts will be illustrated with reference spectra.

Spectroscopy is rapidly evolving, and professionals who build expertise in AI-driven analytics, automation, and high-demand sectors like pharma, biotech, and materials science will be best positioned to advance their careers despite industry-wide talent and budget challenges.

2025 was a turning point for vibrational spectroscopy © somchai20162516

In 2025, the vibrational-spectroscopy community saw a convergence of deep learning, advanced simulation, and portable instrumentation that materially changed how spectra are interpreted and applied. Breakthroughs in spectrum-to-structure models, machine learning (ML)-accelerated molecular dynamics, and field-deployable classic Raman, near-infrared (NIR), and surface-enhanced Raman spectroscopy (SERS) sensors pushed vibrational techniques from complex laboratory characterization toward automated structure elucidation, rapid analysis, and real-world sample sensing (1–6,9). This summary article highlights key 2025 contributions and their implications for the year of discovery.

A recent study provides a detailed introduction to uniform manifold approximation and projection (UMAP) for analyzing LA-ICP-TOF-MS data. By converting high-dimensional MSI data into two-dimensional spaces, UMAP facilitates automated visualization to identify spectral clusters. Spectroscopy spoke to the paper’s lead author, Katharina Kronenberg of the University of Graz, about her group’s work.