Will Wetzel

Researchers 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.

A new study demonstrates that paper bagging significantly enhances Fuji apple quality and appearance. Hyperspectral imaging combined with machine learning offers a powerful, non-destructive method for evaluating fruit grown under different cultivation conditions.

In a recent study published in the journal Beverages, a team of researchers from the National Institute for Research and Development of Isotopic and Molecular Technologies and Babeș-Bolyai University explored a new way to improve wine authentication

A recent study published in the journal Food Chemistry explored Brazil’s cachaça industry, focusing on a new analytical method that can confirm the geographic origin of cachaças from the Brejo Paraibano region in Brazil.

A new study highlights how chemometrics-powered spectroscopic techniques offer a fast, non-destructive, and cost-effective method for detecting phenolics and vitamins in foods.

Researchers in China propose novel postharvest processing mode using vis-NIR spectroscopy and deep learning to accurately measure pomelo sweetness.

Researchers 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.

Top articles published this week include an interview with 2025 Clara Craver Award recipient Prashant Jain, an Icons of Spectroscopy column on Bruce R. Kowalski, and an interview with Pooja Sheevam about quantifying mineral basalts in the Hawaiian PTA-2 drill core.

In the final part of this video interview with Pooja Sheevam, she discusses the importance of her study in understanding the mineralogical and geochemical processes in Hawaii.

Researchers 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.

Explore advancements in infrared spectroscopy and insights from Brian Smith, a key columnist, on the future of this evolving field.

In Part IV of our conversation with Pooja Sheevam, she discusses how scanning electron microscopy with an energy-dispersive X-ray spectroscopy (SEM-EDS) and bulk X-ray fluorescence (XRF) were used to better understand fluid-rock interactions.

Researchers use EC-SERS to reveal the first detailed structural study of hU-II peptide in aqueous solution, paving the way for new drug development.

A recent review in the Journal of Pharmaceutical Analysis highlights how AI, particularly deep learning, is revolutionizing Raman spectroscopy by enhancing its accuracy, efficiency, and applications in drug development, quality control, and clinical diagnostics.

In Part III, our discussion with Pooja Sheevam focused on the use of long-wave IR (LWIR) spectroscopy in analyzing basaltic rocks.

A recent study examined the inner structure of calcium silicate hydrate, a principal binding agent in concrete.

Shanghai researchers develop high-accuracy machine learning system to identify colorless microplastics across varied environments.

A recent study explored new rapid screening alternatives to traditional methods for detecting pork adulteration in meatballs, aiding halal food authentication efforts.

Top articles published this week include an interview with Pooja Sheevam about her study analyzing Hawaii’s PTA-2 drill core, several news stories on recent meteorite studies, and a news article on using Raman spectroscopy and artificial intelligence (AI) to detect adulteration in maple syrup.

In Part I of our video interview with Pooja Sheevam, she discusses why she and her team used both LWIR and SWIR spectroscopy in analyzing Hawaii's PTA-2 drill core, and how the two techniques complemented each other in the study.

A new study published in Marine Pollution Bulletin reveals significant microplastic contamination at 5000-meter depths in the Central Indian Ocean Basin, highlighting the widespread reach of plastic pollution and the urgent need for regulatory action.

Researchers in China have pioneered a rapid, green, and non-destructive detection system using NIR spectroscopy and machine learning to ensure yak milk powder quality.

A new study published in the Journal of Food Composition and Analysis by researchers at the University of Sharjah reveals that while most cat foods sold in Sharjah meet international safety standards, some contain elevated metal levels, prompting calls for stricter regulation and quality control to protect pet health.

Researchers from Tohoku University, Shibaura Institute of Technology, and Shizuoka University unveil advanced sorting system using NIR, THz, and machine learning for improved recycling outcomes.

Researchers at McGill University have developed a fast, eco-friendly method using portable Raman spectroscopy and deep learning to accurately assess the antioxidant content of maple syrup on-site.

Top articles published this week include an interview with Ayanjeet Ghosh and Rohit Bhargava on imaging for biomedical applications, a preview of the 78th International Symposium on Molecular Spectroscopy, and a news article on using artificial intelligence (AI) to study minerals.

Researchers at Guangdong University of Technology have developed a fast, non-destructive Raman spectroscopy method to accurately detect active ingredients in complex drug formulations.