Jerome Workman, Jr.

For Pittcon 2026, the James L. Waters Symposium, scheduled for Monday, March 9, from 2:30 to 4:40 p.m. in Room 221A, turns its focus on Generative artificial intelligence (AI) systems in analytical chemistry, which are increasingly being used for analytical data interpretation, algorithm development, experimental planning, and scientific communication. This article introduces the general concepts of generative AI and its use in spectroscopy.

The 2026 James L. Waters Annual Symposium at Pittcon will focus on the integration of generative AI into analytical chemistry, examining how large language models and AI tools can support method development, data analysis, and chemical measurement while maintaining scientific rigor, validation, and interpretability. Continuing its decades-long tradition of connecting historical perspective with emerging technologies, the symposium will feature presentations from leading chemists and spectroscopists, highlighting both the opportunities and challenges of responsibly incorporating AI into chemical measurement science.

In this video segment, Spiros Pergantis of the University of Crete discusses the supportive and tightly-knit nature of the spectroscopy community, which was evident at the Winter Conference on Plasma Spectrochemistry.

In this video segment, Anika Retzmann of the University of Calgary discusses how analyzing elemental and stable metal isotopic compositions of biological systems help answer fundamentally different research questions across fields such as biomedicine and environmental science.

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 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.

Vibrational spectroscopy is undergoing a major transformation driven by advances in new AI and machine learning, portable instrumentation, nanofabrication, hyperspectral imaging, and robust chemometrics. These developments are enabling more sensitive measurements, field-deployable analysis, multimodal data fusion, and automated spectral interpretation suitable for real-world industrial and clinical use. As these technologies converge, the field is positioned for a renaissance that may redefine how spectroscopy is practiced by 2030.

A new review by researchers from the University of Waterloo, Sanofi, and McGill University highlights how vibrational and fluorescence spectroscopy are reshaping real-time monitoring of pharmaceutical bioprocesses. The authors detail recent advances in UV-Vis, NIR-MIR, Raman, and fluorescence sensing, supported by modern chemometrics and AI tools.

A new perspective article by Anna de Juan and Rodrigo Rocha de Oliveira highlights how hyperspectral imaging (HSI), paired with advanced chemometrics, is redefining process analytical technology (PAT) by coupling chemical specificity with full-field spatial resolution. Their work outlines how HSI surpasses classical spectroscopic PAT tools and enables quantitative, qualitative, and mechanistic insight into chemical processes in real time.

A research team has developed the first short synthetic peptide-based biosensor for real-time tracking of the disease-related protease matrix metalloproteinase-9 (MMP-9), using multi-parametric surface plasmon resonance spectroscopy (MP-SPR).

This mini-tutorial explores how data preprocessing (DP) transforms raw FT-IR ATR spectra into meaningful, reliable inputs for chemometric modeling. Readers will learn about key DP methods: normalization, scatter correction, centering, scaling, and baseline correction, and how proper selection of these techniques improves accuracy, reproducibility, and interpretability in infrared spectroscopic analysis.

Fourier transform infrared (FT-IR) spectroscopy is a versatile, non-destructive analytical tool used to characterize molecular structures, monitor chemical reactions, and quantify analytes in diverse materials. This mini-tutorial reviews fundamental principles, key operational modes, and practical examples across environmental, biomedical, and industrial applications. Readers will review and learn how to optimize FT-IR methods, interpret spectra, and avoid common pitfalls in data collection and processing.

This video in the Icons of Spectroscopy series highlights the life and scientific achievements of Albert A. Michelson, the first American Nobel Laureate in the sciences. It traces his journey from his early years in the American West and his education at the U.S. Naval Academy to his groundbreaking experiments measuring the speed of light. We explore his invention of the Michelson interferometer, its role in the famous Michelson–Morley experiment, and its lasting influence on modern optical and spectroscopic methods, including astronomy.

The miniaturization of spectroscopic instruments has reached a remarkable milestone: wearable vibrational spectroscopy. Techniques such as Raman, surface-enhanced Raman scattering (SERS), infrared (IR), and functional near-infrared (fNIRS) spectroscopy are no longer confined to the laboratory bench—they now fit on our bodies, into household devices, and onto industrial equipment. These wearable devices promise continuous, real-time monitoring, offering molecular-level insights for personal health, household management, clinical care, and industrial applications.

Phil C. Williams (1933–2025) was an internationally recognized pioneer in near-infrared spectroscopy whose visionary work transformed grain analysis from chemical assays to rapid, environmentally responsible spectroscopic methods. His lifelong commitment to scientific rigor, mentorship, and practical innovation has left an enduring legacy that continues to shape industrial spectroscopy for grain analysis that impacts the global economy.

This second part of the Recent Research in Chemometrics and AI for Spectroscopy article surveys current and emerging applications of artificial intelligence (AI) in spectroscopy, highlighting explainable AI (XAI), deep learning, and generative AI frameworks.

This compilation of articles shows how spectroscopy is being used to ensure honey authentication.

This first article in a two-part series introduces the foundations and terminology of AI as applied to chemometrics, defines key algorithmic approaches, and explores their growing role in spectral data analysis, model quantitative calibration, classification, and interpretability.

Using optical and near-infrared spectroscopy, researchers have identified crystalline water ice as the likely driver of explosive outbursts on comet 12P/Pons-Brooks. Their findings link this dramatic cometary activity to a process once observed in comets 17P/Holmes and 332P/Ikeya–Murakami.

A new international review highlights how hyperspectral imaging (HSI) is revolutionizing diverse fields—from counterfeit detection and agriculture to cancer diagnostics—by capturing unprecedented spectral detail invisible to traditional cameras. The study identifies major advances, challenges, and the growing role of artificial intelligence in real-time HSI applications.

Researchers have developed a new method combining unmanned aerial vehicle (UAV) hyperspectral imaging with satellite data to monitor chlorophyll-a (Chla) and total nitrogen (TN) concentrations in coastal wetland waters. Their approach enhances the precision and scalability of water quality assessments, providing a model for managing eutrophication in fragile ecosystems.

Researchers at the European Space Research and Technology Centre (ESTEC) have developed a new framework for onboard hyperspectral image processing that uses deep learning to analyze massive volumes of spectral data in real time. Their review highlights lightweight neural networks, generative models, and hardware accelerators as key technologies shaping the next generation of spaceborne Earth observation.

A sweeping review, “Surface-Enhanced Raman Spectroscopy: A Half-Century Historical Perspective” traces the evolution of SERS from its foundational experiments in the 1970s through the nanoscience era and modern high-spatial-resolution techniques.

Here are ten main unsolved problems in vibrational and atomic spectroscopy, each accompanied by a tutorial-style synopsis suitable for advanced practitioners or graduate-level students. Each of these tutorials, spanning advanced spectroscopy modeling, chemometrics, machine learning (ML) interpretability, and standardization, consists of a descriptive article. Each piece is well-referenced (with detailed matrix equations, radiative transfer models, chemometric derivations, and so forth), and includes the following. • Special focus on each topic—including mathematical derivations in matrix notation. • Conservative, verifiable content anchored to established reference sources. • Appropriate tutorial article structure: Title, Summary, Abstract, Introduction, Theory with equations, Examples, Discussion & Future Research, and References.

This curated collection of recent Spectroscopy magazine mini-tutorials highlights the latest analytical and data-driven innovations in vibrational spectroscopy. Covering NIR, Raman, O-PTIR, and related optical methods, the series emphasizes practical workflows, emerging machine learning integrations, and advanced chemometric techniques for real-world laboratory applications—from food and environmental monitoring to biomedical analysis and nanoscale imaging.

A new perspective from researchers at the Karlsruhe Institute of Technology explores the evolving relationship between human expertise and artificial intelligence in polymer chemistry.

James Dudley Winefordner was a preeminent figure in analytical chemistry whose contributions to spectrochemical method development, instrumentation, and mentoring shaped generations of scientists.

Scientists have developed IR-Bot, an autonomous robotic platform that combines infrared spectroscopy, machine learning, and quantum chemistry to perform real-time analysis of chemical mixtures. The system promises to transform autonomous experimentation by delivering rapid, accurate feedback to guide chemical reactions without human oversight.

This tutorial explains how baseline drift and multiplicative scatter distort spectroscopic data, reviews correction techniques such as MSC, SNV, EMSC, wavelet-based detrending, and AsLS baseline estimation with matrix-based derivations, and explores emerging data-driven scatter modeling strategies and future research directions.

A recent review by Jhonatan Contreras and Thomas Bocklitz from Friedrich Schiller University Jena and the Leibniz Institute of Photonic Technology delves into the emerging field of explainable artificial intelligence (XAI) in spectroscopy.