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Top content published this week include a new article on bioprocess monitoring, a new “Chemometrics in Spectroscopy” column, and an overview of the latest advancements in laser-induced breakdown spectroscopy (LIBS).

A recent study presented a new system for testing metal contamination on microplastics.

A recent review article examines how AI-assisted spectroscopic tools are propelling forensic trace evidence analysis forward.

This evergreen Q&A explores the latest trends and advancements in laser-induced breakdown spectroscopy (LIBS).

A new laser-based spectroscopic method, developed by researchers at the University of Gothenburg and an EU-funded collaboration, has produced the first high-resolution nuclear-shape measurements of the radioactive actinides fermium and neptunium.

Atomic spectroscopy has quietly moved past splitting light and counting ions toward instruments that track a single nanoparticle, fuse a laser pulse with an isotope-ratio mass spectrometer, and correct their own interferences with artificial intelligence in real time. The result is a new class of ICP-MS, LIBS, and X-ray fluorescence (XRF) platforms that are turning what used to be a benchtop-only science into field-ready, self-optimizing analytical intelligence.

A recent study presented a new method for identifying textile fibers using Raman spectroscopy and deep learning.

A hybrid Uniform Design and process-data calibration strategy improves Raman-based PLS model development for real-time monitoring of ultrafiltration/diafiltration and in vitro transcription steps in biopharmaceutical manufacturing.

Carbon plays a role in building complex organic molecules, including those found in outer space.

This curated selection of articles highlights recent advancements in microplastics and nanoplastics and soil analysis.

This is Chemometrics in Spectroscopy Column Number 251. In Column Number 250, we left you with a puzzle. We had just shown that a little-known piece of two-hundred-year-old mathematics, Lagrange's method of undetermined multipliers, could force an MLR calibration to ignore one particular, carefully chosen direction of repack-induced spectral change. It worked, but it only handled one direction at a time, and we admitted we had no idea whether the trick could be extended to PCR or PLS. That bothered us. So in this installment we go looking for the more general version of the idea, and we find it sitting in some very good recent work by Neal Gallagher and Nathanial Watson on what they call clutter suppression (a framework built for a completely different problem, target detection in hyperspectral imaging, that turns out to fit ours almost perfectly). Put the two together and you get an algorithm that whitens the calibration spectra with a clutter covariance matrix estimated from repack replicates, then hands the whitened data to the same Lagrangian machine we built last time. The payoff: the correction now generalizes to several, non-proportional directions of diffuse-reflection variability at once, and, because it is just a preprocessing step, it works ahead of PLS and PCR as well as MLR, which finally answers the question we could not answer before. This approach may very well be an answer to repack variation in repeated solid or slurry sample measurements using diffuse reflection. In this column we change gears with our writing tone and format by taking a "chemometry" approach, rather than a strict tutorial one. In future columns we hope to "unpack" this information in our more typical extended and tutorial manner. Let's explore solving this repack variation problem together.

This dynamic video explores some of the latest advancements in molecular spectroscopy.

Top content published this week include a new digital e-book that explores the role of spectroscopy in cultural heritage analysis and more.