Researchers from Shanghai Jiao Tong University are harnessing artificial intelligence to elevate surface-enhanced Raman spectroscopy (SERS) for highly sensitive, multiplexed biomedical analysis, enabling faster diagnostics, imaging, and personalized treatments.

A recent study presented a dual-method approach combining confocal micro-Raman spectroscopy and Nile Red-assisted fluorescence microscopy to enhance the accuracy and throughput of microplastics detection in environmental samples.

The Society for Applied Spectroscopy (SAS) recently announced the 2025 Fellows Award recipients. Here's a rundown of who was selected and their contributions to the field of spectroscopy.

A new review led by researchers from MIT and Oak Ridge National Laboratory outlines how artificial intelligence (AI) is transforming the study of molecular vibrations and phonons, making spectroscopic analysis faster, more accurate, and more accessible.

Machine learning models and spectral analysis provide a scalable alternative to conventional trace metal detection.

A new review article highlights how researchers in Moscow are integrating machine learning with optical spectroscopy techniques to enhance real-time diagnosis and surgical precision in central nervous system tumor treatment.

Experts from across the industry prepare to explore the critical efficiency hurdles semiconductor fabricators are facing today, introducing themselves and their backgrounds.

Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.

A recent study showcases a cost-effective, ecofriendly UV spectrophotometric method enhanced with dimension reduction algorithms to accurately quantify veterinary drugs dexamethasone and prednisolone, offering a sustainable alternative to traditional analysis techniques.

Spectroscopy spoke to Lingyan Shi of the University of California San Diego, about her team's research exploring a noninvasive cancer classification tool.

A recent study reports high-purity blue emission and thermal stability in novel lanthanum (III) complex synthesized via low-energy precipitation method.

This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.

Researchers develop robust diagnostic method using functional near-infrared (fNIR) spectroscopy and deep neural networks with high accuracy.

Top articles published this week include a compilation of some of the top KOL interviews for the month of June, an announcement from DialAct Corporation about a new product line of Raman spectrometers, and an inside look at Raman spectroscopy in biomedicine.

Researchers from Cranfield University and partners from industry demonstrated the feasibility of using advanced, non-destructive imaging techniques to analyze and standardize organo-mineral fertilizers.