Jerome Workman, Jr.

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.

Astronomers have captured the first detailed optical spectrum of 3I/ATLAS, the third known interstellar object to visit our Solar System. Using the VLT’s MUSE instrument, the team finds a red, dust-dominated coma with no detectable gas emissions, offering a rare glimpse into the composition of alien comets.

This tutorial examines the development of universal spectral libraries, reviewing standardization efforts, mathematical frameworks, and practical examples across multiple spectroscopies, while emphasizing metadata harmonization, FAIR principles, and the emerging role of AI in building interoperable, machine-readable repositories. This remains an unsolved problem in spectroscopy.

New observations of interstellar comet 3I/ATLAS, the third known interstellar object ever to visit our solar system, reveal unexpected activity and composition, challenging many previous assumptions about interstellar objects.

Newly captured spectroscopic data of the third-ever known interstellar object, 3I/ATLAS, reveals a red, organic-rich surface and an enigmatic early dust coma, providing unprecedented insight into materials from beyond our solar system.

Astronomers have conducted detailed spectrophotometric observations of the mysterious interstellar comet 3I/ATLAS using the Palomar 200-inch and Apache Point telescopes. The findings reveal unexpected activity and unique spectral features, enhancing our understanding of this cosmic visitor.

The interstellar object 3I/ATLAS, discovered in July, has captivated astronomers with its unusual characteristics. While some scientists attribute its behaviors to natural cometary processes, others propose more speculative theories, including the possibility of it being an artificial probe. This article examines both mainstream and speculative interpretations of 3I/ATLAS's anomalous features. Other news articles this week will look specifically at the spectroscopic results of telescopes recently analyzing this mysterious object.

This tutorial explores the challenges posed by nonlinearities in spectroscopic calibration models, including physical origins, detection strategies, and correction approaches. Linear regression methods such as partial least squares (PLS) dominate chemometrics, but real-world data often violate linear assumptions due to Beer–Lambert law deviations, scattering, and instrumental artifacts. We examine extensions beyond linearity, including polynomial regression, kernel partial least squares (K-PLS), Gaussian process regression (GPR), and artificial neural networks (ANNs). Equations are provided in full matrix notation for clarity. Practical applications across near-infrared (NIR), mid-infrared (MIR), Raman, and atomic spectroscopies are discussed, and future research directions are outlined with emphasis on hybrid models that integrate physical and statistical knowledge.

Despite decades of major monetary investment for applied research in multiple spectroscopic sensing technologies, achieving an accurate, portable, and painless noninvasive glucose monitor remains a major unmet goal in diabetes care. This goal is extremely difficult due to persistent challenges with sensitivity, analyte specificity, accuracy, calibration stability, and biological interference.

This Chemometrics in Spectroscopy column traces the historical and technical development of these methods, emphasizing their application in calibrating spectrophotometers for prediction of measured sample chemical or physical properties and explores how AI and deep learning are reshaping the spectroscopic landscape.

A new study demonstrates that combining multi-pass FT-IR with a quantum cascade laser, two-dimensional correlation spectroscopy, and machine learning reportedly boosts the accuracy of non-invasive blood-glucose testing. The approach reports a 98.8% classification accuracy, suggesting potential for clinically viable, needle-free diabetes monitoring.

This tutorial explores the motivation, mathematical underpinnings, and practical approaches to fusing spectral data, with emphasis on early, intermediate, and late fusion strategies.

This Icons of Spectroscopy Series article features William George “Bill” Fateley, who shaped modern vibrational spectroscopy through landmark reference books and research papers, pioneering instrumentation, decades of editorial leadership, and deep commitments to students and colleagues. This article reviews his career arc, scientific contributions, and enduring legacy.

A research team in Japan has proposed a new principle, called the emission integral effect, to explain how mid-infrared passive spectroscopic imaging can detect blood glucose levels without invasive methods. Their findings suggest that dilute components like glucose may be more identifiable than concentrated ones when using this technique.

Researchers have developed a miniature non-invasive blood glucose monitoring system using near-infrared (NIR) technology. The compact, low-cost device uses infrared light to measure sugar levels through the fingertip, offering a painless alternative to traditional finger-prick tests.

Researchers from Sharif University of Technology, Tehran, present an approach using near-infrared absorbance and molar absorptivity to estimate blood glucose with a drawn blood sample—showing comparable performance to methods that apply principal components regression (PCR).

Near-infrared (NIR) spectroscopy combined with aquaphotomics shows potential for a rapid, non-invasive approach to detect subtle biochemical changes in biofluids and agricultural products. By monitoring water molecular structures through water matrix coordinates (WAMACs) and visualizing water absorption spectrum patterns (WASPs) via aquagrams, researchers can identify disease biomarkers, food contaminants, and other analytes with high accuracy. This tutorial introduces the principles, practical workflow, and applications of NIR aquaphotomics for everyday laboratory use.

This tutorial provides an in-depth discussion of methods to make machine learning (ML) models interpretable in the context of spectroscopic data analysis. As atomic and molecular spectroscopy increasingly incorporates advanced ML techniques, the black-box nature of these models can limit their utility in scientific research and practical applications. We present explainable artificial intelligence (XAI) approaches such as SHAP, LIME, and saliency maps, demonstrating how they can help identify chemically meaningful spectral features. This tutorial also explores the trade-off between model complexity and interpretability.

Researchers have demonstrated a non-invasive method using milk and near-infrared spectroscopy combined with Aquaphotomics to accurately detect Paratuberculosis in dairy cattle. The technique offers faster, more sensitive diagnosis than traditional methods.