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.

Researchers at the National Research Council (CNR) in Rome have developed a compact spectroscopic sensor and machine learning system that can accurately recognize beverages in smart cups or glasses.

This tutorial investigates the persistent issue of sample heterogeneity—chemical and physical—during spectroscopic analysis. Focus will be placed on understanding how spatial variation, surface texture, and particle interactions influence spectral features. Imaging spectroscopy, localized sampling strategies, and adaptive averaging algorithms will be reviewed as tools to manage this problem, as one of the remaining unsolved problems in spectroscopy.

This tutorial guides spectroscopy practitioners through the integration of Raman spectroscopy and machine learning (ML) techniques for detecting microplastics (MPs) in aquatic and environmental samples.

A recent study presented an AI-enhanced NIRS-XRF fusion spectroscopy method that significantly improves coal classification and quality prediction for coking enterprises.

A new review in Digital Discovery by Yongqiang Cheng of MIT and Oak Ridge National Laboratory highlights how AI-driven methods are changing how we study atomic vibrations.

This Icons of Spectroscopy Series article features E. Bright Wilson, a pioneer of chemical physics. Wilson’s contributions to infrared, Raman, and microwave spectroscopy provided the theoretical and practical foundation for analyzing molecular structure and dynamics. As a revered professor at Harvard and coauthor of landmark texts, he mentored nearly 150 students and researchers, leaving a lasting legacy of scientific excellence and integrity.

In this column, we will describe what is known about the structures of these materials and how Raman spectroscopy can characterize them.

ATR FT-IR shows polyethylene and polypropylene particles were common in facial scrubs and creams.