Jerome Workman, Jr.

A rule proposed by the White House Office of Management and Budget (OMB) would give political appointees final authority over discretionary grant decisions ahead of peer reviewers, allow agencies to terminate active grants without a formal right of appeal, and restrict international collaboration and publication funding across federal science agencies. For the optical, molecular, vibrational, and atomic spectroscopy community, the proposal could affect the grants, journal support, and student and postdoctoral positions that sustain the field.

A review of selected X-ray analysis work published on Spectroscopy Online from 2022–2026.

This review highlights major advances in infrared (IR) and near-infrared (NIR) spectroscopy from 2022–2026, including instrument miniaturization, hyperspectral imaging, machine learning, and artificial intelligence. These developments are expanding the use of vibrational spectroscopy beyond the laboratory into portable and field-deployable analytical systems. Environmental applications such as microplastic detection, biosolids analysis, soil characterization, and contaminant monitoring are emerging as key growth areas.

A comprehensive introduction to LIBs coverage published in Spectroscopy Online from 2022–2025.

The following articles are the 10 most accessed digital object identifier (DOI) manuscripts for Spectroscopy and LCGC International during the month of May 2026.

Today we celebrate World Oceans Day, where we highlight how spectroscopy is helping us advance ocean analysis and preserve this fragile ecosystem.

This feature article traces C. V. Raman’s life, scientific evolution, and foundational contributions to quantum spectroscopy, emphasizing the intellectual path that led to one of the most important discoveries in optical science. Each year his discovery is celebrated as National Science Day (India) on February 28 to honor Sir C.V. Raman's discovery of the Raman Effect.

In their milestone 250th column, Howard Mark and Jerome Workman, Jr. describe a mathematically rigorous algorithm that minimizes or eliminates sampling repack variation in near-infrared spectroscopy. The method separates systematic spectral changes caused by sample rearrangement from true compositional information, enabling more robust calibration models and significantly improving analytical repeatability for powdered and heterogeneous solid samples.

Over the past two years, Spectroscopy magazine has extensively documented and analyzed the growing role of artificial intelligence in spectroscopy through articles, interviews, podcasts, and technical features, highlighting both its hype and its potential as a transformative advancement in data processing and analytical science.

This first installment of Research Profiles in Spectroscopy Series features The University of California, Santa Barbara (UCSB) Petrochronology Research Group and its advances in laser ablation ICP-MS, isotope geochemistry, and petrochronology. Led by John Cottle, Andrew Kylander-Clark, and Morgan Adamson, the group has developed innovative spectroscopic methods that combine high-resolution isotopic dating with trace-element analysis to better understand petrochronology processes, including mountain building, crustal evolution, and complex geological processes.

This new feature in Spectroscopy introduces a structured, application-focused series that curates and examines the most influential research papers in molecular and atomic spectroscopy. Each installment presents a focused “Top 10” collection of seminal publications within a specific analytical domain, spanning techniques such as ultraviolet–visible, infrared, Raman, near-infrared, and atomic spectroscopy. Across biomedical, biopharmaceutical, environmental, and forensic applications, the selected papers illustrate how spectroscopic methods are applied to real-world analytical challenges. Emphasis is placed on the integration of spectral data with chemometric approaches to enable robust calibration, accurate prediction, and meaningful interpretation. Together, these curated collections provide practitioners with a concise, application-oriented perspective on impactful developments in spectroscopy. This article brings together the first nine “Top 10” collections in the series, offering a cross-disciplinary view of influential work shaping the field.

Spectroscopy associate editorial director Jerome Workman Jr. highlights the current trends and ongoing research in space exploration.

The following articles are the 10 most accessed digital object identifier (DOI) manuscripts for Spectroscopy and LCGC International during March, 2026.

Spectroscopy is rapidly evolving into an integrated, intelligent ecosystem where advances in instrumentation, detectors, and optics—combined with chemometrics and artificial intelligence (AI)—are enabling higher sensitivity, miniaturization, multimodal analysis, and real-time decision-making across techniques ranging from ultraviolet–visible (UV–vis), infrared (IR), and Raman to inductively coupled plasma mass spectrometry (ICP-MS), laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF). Together, these developments are driving automation, predictive modeling, and the emergence of autonomous analytical laboratories with increasingly connected, cloud-enabled workflows.

Over the past two years, molecular spectroscopy has undergone a marked transformation from a predominantly laboratory-based analytical approach into a field-deployable, data-rich forensic toolkit. This evolution has been driven by three converging trends: (i) advances in vibrational spectroscopic instrumentation (Fourier transform infrared [FT-IR], Raman, and near-infrared [NIR], (ii) the integration of chemometrics and machine learning for extracting actionable information from complex spectra, and (iii) the emergence of portable and miniaturized devices suitable for in situ analysis. The ten papers reviewed here collectively demonstrate how spectroscopy is now addressing some of the most persistent challenges in forensic science—such as time since deposition (TSD), post-mortem interval (PMI), trace evidence discrimination, and rapid drug identification—while maintaining evidentiary integrity through non-destructive analysis. Importantly, these works also reflect a shift toward interpretability, validation, and legal defensibility, which are essential for courtroom acceptance.

This article is derived from an invited talk given at the Pittcon Conference and Expo in San Antonio, Texas on Monday, March 9, exploring how generative artificial intelligence may transform the daily practice of analytical chemistry. It was presented in The James L. Waters Symposium.

At Pittcon 2026 in San Antonio, Texas, the LCGC International Awards Session was held on Tuesday, March 10, from 1:30 PM to 4:40 PM. This session, presided by Jerome Workman, Jr., celebrated two distinguished scientists whose work has significantly influenced modern separation science. This annual session honors both a lifetime of achievement and the promise of emerging leadership in chromatography. In its nineteenth year, the program recognized Jack Henion with the LCGC Lifetime Achievement Award and Bob W. J. Pirok with the LCGC Emerging Leader in Chromatography Award.

The Pittcon (Pittsburgh) Conference and Expo in San Antonio featured a forward-looking symposium exploring how generative artificial intelligence (AI) may transform the daily practice of analytical chemistry. The James L. Waters Symposium, “Generative AI in the Analytical Chemist’s Toolbox for Chemical Measurements”, took place on Monday, March 9, 2026 (2:30–4:40 p.m.) in Room 221A. The session was presided over by Daniel W. Armstrong of The University of Texas at Arlington, who introduced the topic by emphasizing the rapidly expanding knowledge base required of modern analytical chemists. In addition to chemistry, today’s analytical scientist must command elements of physics, advanced mathematics, data science, and, increasingly, AI. The symposium focused on the practical integration of generative AI tools into chemical measurement science. Speakers discussed how AI can assist analytical chemists with tasks such as algorithm generation, signal processing, literature synthesis, and data interpretation. Importantly, the session emphasized responsible implementation, highlighting the need for rigorous validation, high-quality data sets, and integration into existing laboratory workflows.

At the Pittcon Conference and Expo in Saan Antonio, Texas, on Monday, March 9, 2026 (8:30–11:00 AM, Room 304C), the session “Spectroscopy and Sustainability: A Perfect Match” explored how modern spectroscopic technologies are helping laboratories and industries operate more efficiently while reducing environmental impact. Chaired by John Wasylyk and sponsored by the Society for Applied Spectroscopy, the session brought together 6 presentations covering applications from pharmaceutical process monitoring and biomedical diagnostics to chemical manufacturing, defense, and remote sensing. Throughout the morning, a consistent theme emerged: spectroscopy’s speed, nondestructive nature, and rich chemical information make it inherently aligned with the goals of sustainability.

The 2024-2026 period has been marked by rapid methodological innovation and critical reassessment of established atomic spectrometric techniques in environmental analysis. Advances in inductively coupled plasma–tandem mass spectrometry (ICP-MS/MS) reaction-cell chemistry, matrix-effect correction in X-ray fluorescence (XRF), microwave-sustained plasma sources, and green preconcentration strategies have expanded analytical capabilities for soils, waters, sediments, plants, and atmospheric particulates. Simultaneously, comparative evaluations of inductively coupled plasma–mass spectrometry (ICP-MS), inductively coupled plasma–optical emission spectrometry (ICP-OES), and XRF have sharpened our understanding of detection limits, bias, and field applicability. This brief review highlights 10 of the most influential publications shaping environmental applications of XRF, ICP-MS, and ICP-OES during 2024–2026. Each paper is discussed with emphasis on its technical contributions and broader impact on environmental monitoring, regulatory science, and instrumental development.

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.