Will Wetzel

This study presents a new system that enables the precise detection of glucose, choline, and lactate without traditional labels or antibodies.

A recent study unveiled a new adaptive Raman spectroscopy and transformer-based model for fast, high-accuracy microbial classification.

Top articles published this week include an interview about drug detection techniques with Robert Ewing of the Pacific Northwest National Laboratory, a feature about how funding cuts are impacting analytical chemists, and a compilation of articles about how Raman spectroscopy is being used in cancer diagnostics.

A recent study presented a new, highly sensitive and eco-friendly fluorescent sensor, SU-1, which is capable of detecting ultra-low levels of cyanide in water and living cells.

Researchers at China’s National Key Laboratory have identified 170 nickel autoionization states using resonance ionization mass spectrometry, significantly advancing the spectral database critical for laser isotope separation and atomic spectroscopy.

Researchers from Nanjing University of Information Science & Technology have introduced a breakthrough AI-enhanced multimodal strategy for real-time detection of polyamide microplastics contaminated with heavy metals.

A new review article highlights the role Fourier transform infrared (FT-IR) spectroscopy plays in characterizing nanomaterials and polymers.

Researchers from China Agricultural University introduce PeaNet, promising rapid, accurate, and nondestructive protein analysis.

Researchers at the University of Belgrade have demonstrated that combining Raman and FT-IR spectroscopy with machine learning algorithms offers a highly accurate, non-destructive method for identifying seed varieties in lettuce, paprika, and tomato.

A compilation of articles that explore the role of Raman spectroscopy in cancer research is presented.

Researchers from Guangdong Polytechnic Normal University highlight how combining Raman spectroscopy with machine learning enables rapid, non-destructive, and highly accurate analysis of fruit quality, offering transformative potential for food safety and agricultural diagnostics.

Published in Food Chemistry, researchers from Jiangsu University of Science and Technology and Jimei University use near-infrared (NIR) spectroscopy and machine learning to tackle food adulteration and enhance quality control.

Top articles published this week include an interview series with Robert Ewing of the Pacific Northwest National Laboratory, a news article on using infrared (IR) cameras to see invisible methane leaks, and an article about the role of vibrational spectroscopy in analyzing plant-based food products.

In Part II of our three-part interview with Robert Ewing, he reviews the results of the Nogales border test.

Researchers at Heilongjiang University have developed a rapid and accurate method for detecting sweeteners in food using Raman spectroscopy combined with a Random Forest machine learning algorithm, offering a powerful tool for improving food safety.

Researchers from institutions in Brazil harness near-infrared spectroscopy and machine learning to determine cocoa content with precision.

Researchers from Jiangsu University and Jimei University developed an advanced FT-NIR-based method for food safety monitoring, achieving over 97% accuracy in identifying multiple oil-based contaminants in peanut oil.

In the first part of a three-part interview, Robert Ewing discusses the core technology behind the VaporID system, explains how the system differs from current IMS systems, and describes the challenges the team faced in miniaturizing the VaporID device into a portable, microwave-sized system.

The U.S. Department of Energy’s Pacific Northwest National Laboratory’s (PNNL) VaporID, which is a newly developed portable air sampling system incorporating a miniaturized mass spectrometer (MS), can detect trace levels of fentanyl, methamphetamine, cocaine, and even explosives like TNT with great accuracy.

A new review highlights how vibrational spectroscopy techniques like FTIR, NIR, and Raman offer rapid, non-destructive tools for accurately analyzing plant-based protein content and structure.

Researchers at Xi’an Jiaotong University have demonstrated that ATR-FTIR spectroscopy, combined with histological analysis and machine learning, can accurately distinguish between drowning and strangulation in forensic cases.

A new study published in Geoderma demonstrates that combining soil spectroscopy with radar-derived vegetation indices and environmental data significantly improves the accuracy of soil organic carbon predictions in Brazil’s semi-arid regions.

A new study published in the Journal of South American Earth Sciences reveals how microbial activity, low pH conditions, and sediment chemistry in Brazil’s São Carlos Shale uniquely preserved diverse Upper Cretaceous fossils, offering fresh insights into the paleoenvironment of the Bauru Basin.

In a press release, CRAIC Technologies announced the launch of its novel maceral identification solution that is designed to improve coal analysis. This new system contains high-speed imaging, servo-driven scanning, and intelligent software that work together to generate more accurate maceral analysis.

A recent study presented a dual-method approach combining confocal micro-Raman spectroscopy and Nile Red-assisted fluorescence microscopy to enhance the accuracy and throughput of microplastics detection in environmental samples.

The Society for Applied Spectroscopy (SAS) recently announced the 2025 Fellows Award recipients. Here's a rundown of who was selected and their contributions to the field of spectroscopy.

Machine learning models and spectral analysis provide a scalable alternative to conventional trace metal detection.

A new review article highlights how researchers in Moscow are integrating machine learning with optical spectroscopy techniques to enhance real-time diagnosis and surgical precision in central nervous system tumor treatment.

A recent study showcases a cost-effective, ecofriendly UV spectrophotometric method enhanced with dimension reduction algorithms to accurately quantify veterinary drugs dexamethasone and prednisolone, offering a sustainable alternative to traditional analysis techniques.