Top articles published this week include a video highlighting some of the icons of spectroscopy and a news article about using machine learning to quantify uncertainty in spectroscopic analyses.

This explainer video describes the role that reflection and emission spectroscopy play in characterizing rocky exoplanets.

In this edition of “Inside the Laboratory,” George Shields, a professor of chemistry at Furman University and the founder and director of the Molecular Education and Research Consortium in Undergraduate Computational ChemistRY (MERCURY), discusses the goal of MERCURY and some of its most recent projects

Analytica USA, debuting September 10–12 in Columbus, Ohio, combines a trade exhibition with a scientific conference for analytical science.

A new study demonstrates how a machine learning technique, quantile regression forest, can provide both accurate predictions and sample-specific uncertainty estimates from infrared spectroscopic data. The work was applied to soil and agricultural samples, highlighting its value for chemometric modeling.

In a recent press release, Horiba, an analytical and measurement technology company, announced the release of its Aqualog-Next A-TEEM Spectrometer.

This explainer video highlights how energy-dispersive inelastic X-ray scattering (EDIXS) can be used to discriminate between different stamps.

A recent study conducted by researchers from Northwestern Polytechnical University explored how to improve laser-induced breakdown spectroscopy (LIBS) for analyzing complex mineral samples

A new study demonstrates that infrared spectroscopy combined with chemometric modeling offers a fast, cost-effective way to classify plant-based milk alternatives and detect compositional variability, particularly in almond beverages.

This Icons of Spectroscopy Series article features Infrared pioneer Earle Keith Plyler (1897–1976), who transformed molecular spectroscopy—building precision techniques, reference data, and instruments that set enduring methods and standards at the National Bureau of Standards (NBS, now NIST). As a teacher and mentor, he established a generation of leaders in molecular spectroscopy.

The Winter Conference on Plasma Spectrochemistry will convene in Tucson, Arizona, from January 11–17, 2026.

The Icons of Spectroscopy series shines a spotlight on the pioneers whose work laid the foundation for modern analytical science.

In a recent review article, a team of researchers from Shanghai Jiao Tong University explored how to improve the monitoring of drugs and metabolites in biomedical research and clinical settings.

This tutorial contrasts classical analytical error propagation with modern Bayesian and resampling approaches, including bootstrapping and jackknifing. Uncertainty estimation in multivariate calibration remains an unsolved problem in spectroscopy, as traditional, Bayesian, and resampling approaches yield differing error bars for chemometric models like PLS and PCR, highlighting the need for deeper theoretical and practical solutions.

Learn how to troubleshoot FT-IR spectroscopy. From noisy spectra to ATR errors, discover four common FT-IR problems and simple fixes.

Top articles published this week include a video about the structural complexity of polyethylene, a news story about using near-infrared (NIR) and X-ray fluorescence (XRF) to classify coal types, and a look at microplastic analysis.

This explainer video highlights how spectroscopic sensors can help improve health monitoring applications.

Researchers from Brazil have developed an improved method combining infrared and Raman spectroscopic techniques to better identify and characterize microplastics. This integrated approach enhances accuracy in distinguishing various polymer types and provides refined spectral analysis crucial for environmental studies.

This explainer video highlights how spectroscopy is being integrated with artificial intelligence to improve detection accuracy of microplastics.

Why polyethylene’s IR spectrum splits—unveiling crystallinity, side chains, and polymer structure in HDPE, LDPE, and LLDPE.

A recent study demonstrated that UV–visible (UV-vis) spectroscopy combined with machine learning (ML) can provide a fast, cost-effective, and automated method for detecting biological contamination in microalgae cultures.

Raman spectroscopy, combined with computational modeling and machine learning, shows strong potential for distinguishing PFAS compounds, offering a promising new framework for environmental monitoring and contamination analysis.

Spectroscopy is increasingly being used in cultural heritage studies. We discuss spectroscopy's evolution in this field.

A new study investigates how colorants embedded in microplastics (MPs) interfere with Raman spectroscopy, one of the key tools used to identify microplastic particles. The research details how fluorescence from these additives complicates spectral analysis, underscoring challenges in environmental microplastic detection.

A new review article highlights how Explainable Artificial Intelligence (XAI) can enhance transparency, trust, and innovation in agricultural spectroscopy, paving the way for smarter and more sustainable food quality assessment.

A new study highlights terahertz (THz) metamaterials as a promising non-invasive, highly sensitive technology for improving food safety testing in agriculture.

Researchers have developed a novel approach to quantify microplastics in water environments by combining Raman spectroscopy with convolutional neural networks (CNN). This integrated method enhances the accuracy and speed of microplastic identification, offering a promising tool for environmental monitoring.

A recent study found that coffee, red wine, and Coca-Cola significantly reduce the hardness and alter the chemical structure of dental resin composites.