Will Wetzel

Top articles published this week include a feature article about big pharma’s investments in U.S.-based manufacturing, an article about the 2025 Emerging Leader in Molecular Spectroscopy Lingyan Shi, and some news items detailing the winners of the Coblentz Society’s student awards.

In a recent announcement, the Coblentz Society, an organization committed to promoting and nurturing young scientists to pursue vibrational spectroscopy, announced that Steven Quarin, a student at the University of Cincinnati, is this year’s recipient of the William G. Fateley Student Award.

A new review published in Trends in Food Science & Technology highlights how advanced spectroscopy, multidimensional chromatography, artificial intelligence (AI), and novel sensors are improving food safety by enhancing sensitivity, speed, and sustainability in contaminant detection.

A recent study presented a simple correction method that significantly improved the accuracy of Transmission Raman Spectroscopy by mitigating spectral distortions caused by tablet thickness, porosity, and compaction force.

The Coblentz Society, a nonprofit organization dedicated to fostering the application and understanding of vibrational spectroscopy, announced the winners of its 2025 student awards.

Researchers at INIAV in Portugal have demonstrated that near-infrared (NIR) spectroscopy combined with chemometric algorithms offers a rapid, non-destructive, and accurate method for detecting harmful fumonisins in maize, enhancing food safety monitoring.

Researchers at China Agricultural University developed a rapid and accurate spectroscopic method using NIR and FT-IR combined with PLS regression to measure protein content in rice noodles, enhancing quality control for the popular river snail rice noodle (luosifen) industry.

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.