Raman Spectroscopy

This tutorial explores the motivation, mathematical underpinnings, and practical approaches to fusing spectral data, with emphasis on early, intermediate, and late fusion strategies.

This Icons of Spectroscopy Series article features William George “Bill” Fateley, who shaped modern vibrational spectroscopy through landmark reference books and research papers, pioneering instrumentation, decades of editorial leadership, and deep commitments to students and colleagues. This article reviews his career arc, scientific contributions, and enduring legacy.

This tutorial provides an in-depth discussion of methods to make machine learning (ML) models interpretable in the context of spectroscopic data analysis. As atomic and molecular spectroscopy increasingly incorporates advanced ML techniques, the black-box nature of these models can limit their utility in scientific research and practical applications. We present explainable artificial intelligence (XAI) approaches such as SHAP, LIME, and saliency maps, demonstrating how they can help identify chemically meaningful spectral features. This tutorial also explores the trade-off between model complexity and interpretability.

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

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

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.

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.

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.

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.

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.

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.

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.

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

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

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.

A new study reveals that as Hawaiian volcanoes evolve, their magma storage shifts from shallow crustal reservoirs to deeper mantle zones, offering critical insights into volcanic behavior and future hazard potential.

Researchers at the Czech Academy of Sciences have demonstrated that cost-effective silver and gold nanoparticles, used with surface-enhanced Raman spectroscopy (SERS), can sensitively detect stress-induced adenine release in bacteria, paving the way for rapid, point-of-care diagnostic tools.

Using a custom-built 785 nm Raman instrument, a recent study identified 14 pesticides and employed multivariate and machine learning techniques—particularly Random Forests (RF)—to automate classification. Readers will learn practical steps in spectral acquisition, spectral comparison across wavelengths, data preprocessing, and implementing machine learning models for real-world chemical monitoring (1).

This Icons of Spectroscopy Series article features Charles Kenneth Mann, a pioneer of quantitative Raman spectroscopy.

Top articles published this week include an interview with Shreya Singh, a tutorial about using Raman spectroscopy to probe water content and structures in biological tissues, and an article about detecting honey adulteration using near-infrared (NIR) spectroscopy.