Top articles published this week include a preview of our upcoming content series for National Space Day, a news story about air quality monitoring, and an announcement from Metrohm about their new Midwest office.
This week, Spectroscopy published articles highlighting recent studies in several application areas in analytical spectroscopy including environmental analysis and space exploration. Key techniques highlighted in these articles include infrared (IR) spectroscopy, Raman spectroscopy, and laser-induced breakdown spectroscopy (LIBS). Happy reading!
Celebrate National Space Day with Spectroscopy
Spectroscopy is partnering with the Society for Applied Spectroscopy (SAS) to celebrate National Space Day on May 2 with a special online content series. This upcoming content series will highlight spectroscopy's crucial role in space exploration, including its use in instruments aboard the Hubble and James Webb telescopes, as well as the Mars rovers Perseverance and Curiosity. Spectroscopy enables remote analysis of celestial bodies, helping scientists uncover the chemical composition and atmospheric conditions of planets and stars (1). Techniques like Raman, IR, and mass spectrometry (MS) have advanced the search for life on Mars by identifying key elements and water-formed minerals, driving future discoveries in planetary science (1).
LIBS Illuminates the Hidden Health Risks of Indoor Welding and Soldering
As indoor soldering and welding become more common, scientists in China have developed a rapid air quality monitoring system using LIBS and single-particle aerosol mass spectrometry (SPAMS). These tools detect harmful airborne pollutants like lead, tin, carbon emissions, and fine particulate matter (PM2.5, PM10) in real time (2). The studies show that rising soldering temperatures increase toxic emissions, with PM2.5 posing serious health risks because of prolonged exposure (2). Combining LIBS and SPAMS with machine learning (ML) enables detailed pollutant profiling and real-time alerts, offering a faster, non-destructive alternative to traditional air monitoring for safer indoor environments.
Metrohm Announces Grand Opening of Regional Office in Chicago
Metrohm USA has opened a new regional office and laboratory in Lombard, Illinois, to enhance support and service for its Midwest customers. This strategic expansion aims to improve accessibility for industries such as pharmaceuticals, food and beverage, and environmental testing. The facility features spaces for training, product demos, and industry events, reinforcing Metrohm’s commitment to personalized, collaborative customer support (3). Located near Chicago’s major transportation hubs, the office offers greater convenience and responsiveness. The ribbon-cutting ceremony is scheduled for April 30 (3). This move underscores Metrohm’s mission to provide innovative, high-quality chemical analysis solutions alongside a human-centered customer experience.
Smarter Sensors, Cleaner Earth Using AI and IoT for Pollution Monitoring
A recent review article published in Frontiers in Environmental Science highlighted how smart technologies like artificial intelligence (AI), Internet of Things (IoT), machine learning (ML), and spectroscopy are improving pollution monitoring. Led by an international team, the study explores how low-cost sensors and AI algorithms enable real-time detection and prediction of air, soil, and water pollutants (4). Spectroscopic techniques, such as vis-NIR and surface-enhanced Raman spectroscopy (SERS), play a key role in identifying contaminants, while integrated sensor networks enhance environmental data accuracy (4). Despite challenges like data sharing and model transparency, this review underscores the growing potential of digital tools in advancing sustainable pollution control.
High-Speed Laser MS for Precise, Prep-Free Environmental Particle Tracking
Researchers at Oak Ridge National Laboratory recently demonstrated the effectiveness of laser ablation-inductively coupled plasma-time-of-flight mass spectrometry (LA-ICP-TOF-MS) for rapid, direct analysis of airborne pollutants. Unlike traditional methods, this technique requires no chemical digestion, offering accurate, high-throughput elemental mapping in under 30 minutes per sample (5). The study, involving intentional ruthenium particle release, showed that LA-ICP-TOF-MS detects and differentiates target and background particles with high precision (5). Compared to quadrupole systems, TOF-MS offers better isotopic accuracy and broader element detection (5). Validated by SEM-EDS, the method holds promise for real-time environmental and public health monitoring of toxic airborne contaminants.
Hyperspectral Imaging for Walnut Quality Assessment and Shelf-Life Classification
June 12th 2025Researchers from Hebei University and Hebei University of Engineering have developed a hyperspectral imaging method combined with data fusion and machine learning to accurately and non-destructively assess walnut quality and classify storage periods.
AI-Powered Near-Infrared Imaging Remotely Identifies Explosives
June 11th 2025Chinese researchers have developed a powerful new method using near-infrared (NIR) hyperspectral imaging combined with a convolutional neural network (CNN) to identify hazardous explosive materials, like trinitrotoluene (TNT) and ammonium nitrate, from a distance, even when concealed by clothing or packaging.
New NIR/Raman Remote Imaging Reveals Hidden Salt Damage in Historic Fort
June 10th 2025Researchers have developed an analytical method combining remote near-infrared and Raman spectroscopy with machine learning to noninvasively map moisture and salt damage in historic buildings, offering critical insight into ongoing structural deterioration.
New Machine Learning Model Distinguishes Recycled PET with 10% Accuracy Threshold
June 9th 2025Researchers from Jinan University and Guangzhou Customs Technology Center have developed a cost-effective UV-vis spectroscopy and machine learning method to accurately identify recycled PET content as low as 10%, advancing sustainable packaging and circular economy efforts.
Harnessing Near-Infrared Spectroscopy and Machine Learning to Detect Microplastics in Chicken Feed
June 5th 2025Researchers from Tianjin Agricultural University, Nankai University, and Zhejiang A&F University have developed a highly accurate method using near-infrared spectroscopy and machine learning to rapidly detect and classify microplastics in chicken feed.