AI Boosts SERS for Next Generation Biomedical Breakthroughs
July 2nd 2025Researchers 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.
Evaluating Microplastic Detection with Fluorescence Microscopy and Raman Spectroscopy
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.
Artificial Intelligence Accelerates Molecular Vibration Analysis, Study Finds
July 1st 2025A 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 and Optical Spectroscopy Advance CNS Tumor Diagnostics
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.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers 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%.
New Ecofriendly Spectrophotometric Method Boosts Accuracy in Veterinary Drug Analysis
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.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This 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.
New Imaging Techniques Explored to Assess Quality of Sustainable Fertilizers
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.