Jerome Workman, Jr.

Between 2024 and 2026, environmental applications of vibrational spectroscopy advanced rapidly through innovations in multimodal instrumentation (combining 2 or more distinct measurement techniques), spectral data fusion, portable sensing technologies, and the integration of chemometrics and machine learning (ML). Near-infrared (NIR), Fourier transform infrared (FTIR), and Raman spectroscopy were increasingly deployed to address pressing environmental challenges such as microplastics contamination, soil organic matter quantification, indoor air quality monitoring, and pesticide residue detection in food and ecological systems. This article reviews 10 influential peer-reviewed papers published during this period, providing expanded narrative discussions of their technical contributions and explaining why each paper represents a significant impact on the field.

Artificial intelligence and machine learning are rapidly reshaping how analytical data are modeled, interpreted, and deployed, but the conceptual foundation is already familiar to practitioners of chemometrics. Latent variables, calibration models, variance–bias tradeoffs, and multivariate optimization did not originate with neural networks; they have been central to spectroscopic data analysis for decades. This expanded glossary provides a rigorous, side-by-side translation between modern artificial intelligence (AI) terminology and established chemometric concepts. This glossary is intended to demystify AI terminology, while preserving statistical clarity. It is designed to help analytical scientists, spectroscopists, and chemometricians engage with modern data-driven methods without abandoning physical interpretability or statistical discipline.

From a chemometric standpoint, artificial intelligence (AI) in spectroscopy is best understood as an extension of established multivariate methods rather than as a replacement. Most AI approaches closely parallel familiar tools such as regression, classification, and principal component analysis, but offer greater flexibility to handle nonlinear behavior, interacting physical and chemical effects, and large, heterogeneous datasets. By learning directly from raw spectra, AI methods can reduce reliance on manual preprocessing while still indicating which spectral regions influence predictions. In this sense, AI represents a developmental layer of chemometrics that enables classical concepts to operate effectively in modern spectroscopic systems. Overall, AI is best viewed as the next developmental layer of chemometrics, not as a competing discipline. As with all current AI programs, domain knowledge of analytical chemistry is essential for AI’s effective application. Knowing the boundaries of what is plausible in any chemical or modeling system allows fine-tuning of the models towards useful and reliable analytical results.

At Pittcon, generative artificial intelligence will be presented at the James L Waters Symposium on Monday, March 9, 2:30 PM to 4:40 PM in Room 221A. Generative artificial intelligence has transitioned from a conceptual novelty to a practical approach for innovation in spectroscopic data analysis. During 2025, a small set of highly influential publications crystallized this transformation by demonstrating how generative models can synthesize realistic spectra, solve inverse spectral problems, accelerate materials discovery, and automate molecular structural elucidation. This article reviews six pivotal contributions published in 2025 that collectively define the state of generative artificial intelligence in spectroscopy. These works establish theoretical foundations, survey emerging methods, introduce physics-informed generative architectures, and demonstrate transformative applications across vibrational, electronic, and magnetic resonance spectroscopies.

During 2025, near-infrared (NIR) spectroscopy has accelerated its transition from a mature analytical technique into a digitally enabled cornerstone of biopharmaceutical manufacturing and quality control. Advances in miniaturized instrumentation, process analytical technology (PAT), chemometrics, artificial intelligence (AI), and real-time process control technologies have driven NIR spectroscopy into new roles spanning upstream fermentation, downstream processing, raw material characterization, and continuous manufacturing. This article reviews and contextualizes ten influential peer-reviewed publications from 2025 that collectively define the current state and near-term trajectory of NIR spectroscopy in biopharmaceutical analysis.

Fourier transform infrared (FT-IR) spectroscopy has undergone a notable evolution in biopharmaceutical analysis over the past three years. Advances in crystal engineering, process analytical technology (PAT), chemometrics, machine learning (ML), and hyphenated analytical platforms have significantly expanded FT-IR’s analytical scope. This article reviews ten of the most influential publications from 2023–2026 that exemplify FT-IR’s growing role across the biopharmaceutical lifecycle, from drug substance design and formulation to manufacturing, quality control, and clinical bioanalysis.

Between 2023 and 2026, Raman spectroscopy transitioned from a supportive analytical technique to a central enabling technology in biopharmaceutical analysis and manufacturing. Advances in artificial intelligence (AI), machine learning (ML), automation, and surface-enhanced Raman spectroscopy (SERS) have expanded Raman’s role from nutrient monitoring to real-time prediction of critical quality attributes (CQAs), inline control of complex bioprocesses, and non-destructive analysis of finished drug products. This article reviews ten of the most influential publications from this period, highlighting how they collectively reshaped expectations for Raman spectroscopy as a process analytical technology (PAT) and a quality-by-design (QbD) tool in modern biopharmaceutical development.

This year’s Emerging Leader in Atomic Spectroscopy Award recipient is Sarah Theiner, whose research is focused on the application of atomic spectroscopy techniques—laser ablation inductively coupled plasma–mass spectrometry (LA-ICP-MS) and single-cell ICP-MS—to expand these analytical techniques as tools for biological and clinical imaging and drug-distribution studies.

Over the past two years, near infrared spectroscopy (NIRS) and related NIR techniques have seen rapid adoption in biomedical research. These developments span non invasive diagnostics, functional monitoring, machine learning integration, point of care probes, and applications in complex clinical settings such as liver fibrosis, viral detection, neonatal care, brain injury, and neurodegenerative disorders. This article synthesizes 10 key publications, highlighting trends, methodologies, and clinical potential.

Over the past three to four years, Fourier Transform Infrared (FT-IR) spectroscopy has emerged as one of the most rapidly expanding vibrational techniques in biomedical research. Driven by advances in attenuated total reflectance (ATR), live-cell measurements, chemometrics, and machine learning (ML), FT-IR has moved beyond descriptive biochemical profiling toward predictive diagnostics and translational clinical science. This article highlights and critically summarizes the top 10 most influential peer-reviewed articles published recently on FT-IR applications in tissues, cells, hair, blood, saliva, urine, and exercise physiology, emphasizing analytical innovation, clinical relevance, and future impact.

In the past few years, Raman spectroscopy and its technological modifications—such as surface-enhanced Raman spectroscopy (SERS), coherent Raman scattering (CRS), and multimodal platforms—have transitioned from proof-of-concept demonstrations to impactful tools in biomedical research. These advances span therapeutic monitoring, chemical biology imaging, deep-tissue diagnostics, precision oncology, and multimodal analytics. This article synthesizes the most influential reviews in these areas, highlighting emerging trends, limitations, and future directions.

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.

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.