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A recent Tutorial Tuesday clip explored modern oil refining operations and the role spectroscopy plays in these operations.

An upcoming oral session at the American Chemical Society (ACS) Fall 2026 meeting will explore how women in STEM can pursue entrepreneurship opportunities.

Pittcon award winners unpack biopharma column tech and AI-driven chromatography, sharing career insights and practical method-development lessons.

A new review in Food Physics finds that combining near-infrared spectroscopy with machine learning is transforming food quality testing across sectors like meat, dairy, and produce.

The American Chemical Society (ACS) has several keynotes planned for their upcoming Fall 2026 conference.

Researchers at the University of Agriculture Faisalabad showed that Raman spectroscopy combined with chemometric modeling (PCA and PLSR) can accurately and non-destructively quantify rosuvastatin content in pharmaceutical tablets.

Curriculum design is important in preparing students for the workforce. This “Pathways in Spectroscopy” clip explores the areas of spectroscopy that need to be represented more in university curriculums.

Top articles published this week include a review article on X-ray techniques and a new “Pathways in Spectroscopy” episode that discusses an approach to teaching students about spectroscopy.

In this short feature article, we explore the topics covered at the recent International Symposium on Molecular Spectroscopy (ISMS).

A European study of nearly 9,000 cropland soil samples found that combining visible–near-infrared (vis-NIR) spectroscopy with climate and topographic data only improves soil carbon and nitrogen prediction accuracy at spectral resolutions of 10 nanometers or finer.

Spectroscopy is playing a key role in analyzing materials in lithium-ion batteries.

The upcoming 2026 Vibrational Spectroscopy Seminar will focus on time-resolved spectroscopy and vibrational imaging, with structured mentorship component for doctoral and postdoctoral attendees.

A new artificial intelligence (AI) interpretability method developed at Zhejiang Police College was designed to improve transparency in Raman spectroscopy classification by segmenting spectral data into continuous regions and quantifying each region's contribution to model decisions.