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.

Click here to access the Spectroscopy July/August 2025 North America PDF in an interactive format.

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.

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.

Top articles published this week include a tutorial about calibration transfer techniques and inter-instrument variability, a couple news articles about quantifying microplastics, and a feature on the “pressure to publish.”

Researchers from the U.S. Horticultural Research Laboratory’s Agricultural Research Service present a preliminary characterization of the citrus peel materials responsible for elevated high performance liquid chromatography-ultraviolet (HPLC-UV) chromatogram baselines from citrus peel extracts through the use of Fourier-transform infrared (FTIR) and proton-nuclear magnetic resonance (1H-NMR) spectroscopy.

Chinese Academy of Sciences researchers combine spectroscopic methods with deep learning to classify microplastics at near-perfect accuracy.

A new study uses spectroscopic tools to analyze the spread and transformation of microplastics across water, soil, and air systems. Researchers also examined the limitations of global policies in addressing this multidimensional pollutant.

Researchers from Zhejiang University highlight how combining machine learning with spectroscopic imaging can transform biomedical research by enabling more precise, interpretable, and efficient analysis of complex molecular data.

Jiangnan University researchers map the evolution, challenges, and future of spectroscopy in preserving humanity’s shared legacy.

The findings suggest that bottling processes and PET containers are major contributors to beverage contamination, raising concerns about food safety and environmental pollution.

A new study by researchers from Spain and Brazil demonstrates that combining near- and mid-infrared spectroscopy with advanced statistical analysis can identify how growing site, harvest season, and clonal variation influence yerba mate’s chemical composition.

Researchers at the National Institute of Technology Rourkela have developed a highly accurate machine learning-assisted FT-IR spectroscopy method to detect and quantify sawdust adulteration in coriander powder, offering a fast and scalable solution to enhance food safety and authenticity.

A new study using infrared spectroscopy reveals that commercial beet sugar contains microplastic particles, raising concerns over food processing and packaging practices. Scientists identified various plastic types in sugar samples, including polyethylene and PET.

A recent study reveals that microplastics, primarily blue polyolefin fibers, are widespread throughout the western Arctic Ocean’s water column.

A research team from Putian University has developed a dual surface-enhanced Raman spectroscopy (SERS) and Fourier transform infrared spectroscopy (FT-IR) approach to reveal detailed molecular changes in E. coli exposed to different antibiotics.

Researchers at Santiago de Compostela University (Santiago, Spain) find ultraviolet–visible (UV–vis) spectroscopy can detect and quantify post-COVID condition with high accuracy, paving the way for real-time clinical use.

A new study reveals that anthocyanin-rich fruit extracts degrade rapidly under sunlight but remain most stable in cold, dark storage.

Inter-instrument variability is a major obstacle in multivariate spectroscopic analysis, affecting the reliability and portability of calibration models. This tutorial addresses the theoretical and practical challenges of model transfer across instruments. It covers spectral variability sources—such as wavelength shifts, resolution differences, and line shape variations—and presents key standardization techniques including direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). We discuss the underlying mathematics of these approaches using matrix notation and highlight limitations that must be considered for reliable universal calibration.