Jerome Workman, Jr. is an Executive Editor for Spectroscopy. Direct correspondence about this article to jworkman@mjhlifesciences.com
Researchers at Wroclaw University of Science and Technology and Université catholique de Louvain have demonstrated how diffuse reflectance spectroscopy (DRS) in the 900 nm to 1100 nm range can non-destructively assess powder blend homogeneity in metal additive manufacturing. Their findings suggest that DRS offers a fast, reliable method for ensuring uniformity in aluminum alloy powders used in powder bed fusion 3D printing.
Researchers at Sun Yat-sen University have applied visible diffuse reflectance spectroscopy to reveal how iron oxide minerals, particularly hematite, define the iconic red hues of China's Danxia landforms.
Researchers from Northwestern University, University of Cádiz, and University of Arizona have developed new formulae for analyzing optical thin films that outperform traditional models by accounting for complex geometries and absorbing substrates. These advances offer more precise ultraviolet-visible-near-infrared (UV-vis-NIR) spectroscopic analysis of film materials used in critical modern technologies.
In this Icons of Spectroscopy article, Executive Editor Jerome Workman Jr. delves into the life and impact of Bruce Kowalski, an analytical chemist whose major contributions to chemometrics helped establish the field of applying advanced quantitative and qualitative mathematics to extract meaningful chemical information from complex datasets. Kowalski’s visionary approach to chemical data analysis, education, and software development has transformed the landscape of modern analytical chemistry for academia and industry.
Spanish scientists have combined Raman imaging with powerful chemometric tools to identify and map organic molecules in a Martian meteorite, offering key insights for future Mars sample return missions and the search for past life on the Red Planet.
A team of scientists in Poland has unveiled the first detailed structural and magnetic analysis of the Ribbeck meteorite, a recently recovered space rock classified as an aubrite. Using Raman spectroscopy, X-ray diffraction, and advanced magnetic testing, researchers revealed the meteorite's unique mineralogy and its connection to deep space conditions.
Researchers in Rome used advanced spectroscopic techniques to probe the mineralogy of the CM2 carbonaceous chondrite NWA 12184. This revealed the effects of space weathering and provided insights into C-type asteroid evolution.
Researchers have developed a powerful deep learning model that automates the identification of minerals using Raman spectroscopy, offering faster, more accurate results even in complex geological samples. By integrating attention mechanisms and explainable AI tools, the system boosts trust and performance in field-based mineral analysis.
A new review by researchers from IIT Delhi and the University of Queensland highlights how Terahertz (THz) and low-wavenumber Raman (THz-Raman) spectroscopy are advancing quality control and efficiency in pharmaceuticals, agriculture, and mineral industries. These powerful non-invasive tools enable detailed multi-parameter sensing, offering deeper insight at the molecular level.
A team of international researchers has developed a faster, more accurate method to analyze soil carbon fractions using mid-infrared spectroscopy and deep learning. Their approach preserves the chemical balance of soil organic carbon components, paving the way for improved climate models and sustainable land management.
Scientists at the University of Barcelona have developed a fast and reliable way to identify natural, treated, and synthetic rubies and sapphires using Fourier-transform infrared (FT-IR) spectroscopy. Their study reveals unique spectral fingerprints for different gemstone types, offering a powerful tool in the fight against gem fraud.
A historical and technical overview from the Gemological Institute of America (GIA) explores how advanced scientific instruments—particularly spectroscopic methods—have transformed gem identification. From refractometers to modern spectrophotometers, this deep dive highlights the evolving challenges and solutions in gem testing.
New research from the Gemological Institute of America highlights the essential role of infrared spectroscopy in identifying gemstones, detecting treatments, and distinguishing natural from synthetic gems. The technique’s precision and non-destructive nature have made it an indispensable tool in modern gemology.
In this “Icons of Spectroscopy” column, Spectroscopy Executive Editor Jerome Workman, Jr. explores the life and contributions of Albert Abraham Michelson, a graduate of the United States Naval Academy, who showed early brilliance in physics and mathematics, eventually resulting in a teaching position at the Academy and the Nobel Prize in Physics in 1907.
Researchers have developed a wireless, wearable brain-monitoring device using functional near-infrared spectroscopy (fNIRS) to detect cognitive fatigue in real time. The miniaturized system enables mobile brain activity tracking, with potential applications in driving, military, and high-stress work environments.
A newly published review in the journal Advanced Materials explores how intelligent wearable sensors, powered by smart materials and machine learning, are changing healthcare into a decentralized, personalized, and predictive modeling system. An international team of researchers highlights emerging technologies that promise earlier diagnosis, improved therapy, and continuous health monitoring—anytime, anywhere.
A new comprehensive review explores how wearable plasmonic sensors using surface-enhanced Raman spectroscopy (SERS) are changing the landscape for non-invasive health monitoring. By combining nanotechnology, AI, and real-time spectroscopy analysis to detect critical biomarkers in human sweat, this integration of nanomaterials, flexible electronics, and AI is changing how we monitor health and disease in real-time.
In this interview, S. Michael Angel, a consultant on the SuperCam team, explains how the instrument analyzes Martian rocks and what it reveals about Mars’ geology and potential for past life.
A study from Chinese researchers demonstrates how combining satellite imagery, land use data, and machine learning can improve pollution monitoring in fast-changing urban rivers. The study focuses on non-optically active pollutants in the Weihe River Basin and showcases promising results for remote, data-driven water quality assessments.
New research highlights how remote satellite sensing technologies are changing the way scientists monitor inland water quality, offering powerful tools for tracking pollutants, analyzing ecological health, and supporting environmental policies across the globe.
Modern remote sensing technologies have evolved from coarse-resolution multispectral sensors like MODIS and MERIS to high-resolution, multi-band systems such as Sentinel-2 MSI, Landsat OLI, and UAV-mounted spectrometers. These advancements provide greater spectral and spatial detail, enabling precise monitoring of environmental, agricultural, and land-use dynamics.
A new dual-spectroscopy approach reveals real-time pollution threats in indoor workspaces. Chinese researchers have pioneered the use of laser-induced breakdown spectroscopy (LIBS) and aerosol mass spectrometry to uncover and monitor harmful heavy metal and dust emissions from soldering and welding in real-time. These complementary tools offer a fast, accurate means to evaluate air quality threats in industrial and indoor environments—where people spend most of their time.
A global research team has detailed how smart sensors, artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies are transforming the detection and management of environmental pollutants. Their comprehensive review highlights how spectroscopy and sensor networks are now key tools in real-time pollution tracking.
Scientists at Oak Ridge National Laboratory have demonstrated that a fast, laser-based mass spectrometry method—LA-ICP-TOF-MS—can accurately detect and identify airborne environmental particles, including toxic metal particles like ruthenium, without the need for complex sample preparation. The work offers a breakthrough in rapid, high-resolution analysis of environmental pollutants.
In this two-part "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared spectroscopy, has transformed analysis technology. In this Part II article of a two-part series, we summarize Norris’ foundational publications in NIR, his patents, achievements, and legacy.
A new deep learning-enhanced spectroscopic platform—SERSome—developed by researchers in China and Finland, identifies medicinal and edible homologs (MEHs) with 98% accuracy. This innovation could revolutionize safety and quality control in the growing MEH market.
Researchers are using AI-enabled Raman spectroscopy to enhance the development, administration, and response prediction of cancer immunotherapies. This innovative, label-free method provides detailed insights into tumor-immune microenvironments, aiming to optimize personalized immunotherapy and other treatment strategies and improve patient outcomes.
In this "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared (NIR) spectroscopy has transformed analysis technology. This Part I article of a two-part series introduces Norris’ contributions to NIR.
A leading-edge review led by researchers at Oak Ridge National Laboratory and MIT explores how artificial intelligence is revolutionizing the study of molecular vibrations and phonon dynamics. From infrared and Raman spectroscopy to neutron and X-ray scattering, AI is transforming how scientists interpret vibrational spectra and predict material behaviors.
