Raman Spectroscopy

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.

Researchers at Jiangnan University have developed a highly accurate method combining Raman spectroscopy with deep learning to monitor acid value in palm oil.

Researchers from Université de Montréal have demonstrated that a new handheld Raman spectroscopy device, the UltraProbe, can accurately and rapidly detect retroperitoneal soft tissue sarcomas during surgery, offering real-time tissue analysis that could improve surgical outcomes.

This is a brief tutorial review on the use of the Raman OH stretching bands of water for biomedical applications.

A new study used advanced techniques, including µ-Raman spectroscopy and machine learning, to map and predict microplastic pollution on São Paulo’s urban beaches.

A recent study demonstrated that portable Raman spectroscopy, combined with advanced statistical modeling, offers a reliable, non-destructive method for monitoring nitrate levels in greenhouse-grown spinach.

A new study has confirmed the presence of multiple microplastic types in human amniotic fluid using a dual-method approach, raising concerns about potential long-term impacts on fetal development.

A recent study presented a simple correction method that significantly improved the accuracy of Transmission Raman Spectroscopy by mitigating spectral distortions caused by tablet thickness, porosity, and compaction force.

German researchers have demonstrated a portable Raman laser system that analyzes soil composition directly in agricultural fields, offering precise, real-time data for precision farming.

Spectroscopy's 2025 Emerging Leader in Molecular Spectroscopy is Lingyan Shi of the University of California, San Diego. Shi’s research focuses on developing and applying molecular imaging tools, including stimulated Raman scattering (SRS), multiphoton fluorescence (MPF), fluorescence lifetime imaging (FLIM), and second harmonic generation (SHG) microscopy.

A recent study unveiled a new adaptive Raman spectroscopy and transformer-based model for fast, high-accuracy microbial classification.

This tutorial addresses the critical issue of analyte specificity in multivariate spectroscopy using the concept of Net Analyte Signal (NAS). NAS allows chemometricians to isolate the portion of the signal that is unique to the analyte of interest, thereby enhancing model interpretability and robustness in the presence of interfering species. While this tutorial introduces the foundational concepts for beginners, it also includes selected advanced topics to bridge toward expert-level applications and future research. The tutorial covers the mathematical foundation of NAS, its application in regression models like partial least squares (PLS), and emerging methods to optimize specificity and variable selection. Applications in pharmaceuticals, clinical diagnostics, and industrial process control are also discussed.

Top articles published this week include an interview about drug detection techniques with Robert Ewing of the Pacific Northwest National Laboratory, a feature about how funding cuts are impacting analytical chemists, and a compilation of articles about how Raman spectroscopy is being used in cancer diagnostics.

Irish researchers have developed a lightning-fast, label-free spectroscopic imaging method capable of classifying immune cells in just 5 milliseconds. Their work with broadband coherent anti-Stokes Raman scattering (BCARS) pushes the boundaries of cellular analysis, potentially transforming diagnostics and flow cytometry.

Chinese researchers have developed a cutting-edge cervical cancer diagnostic model that combines spontaneous Raman spectroscopy, CARS imaging, and artificial intelligence to achieve 100% accuracy in distinguishing healthy and cancerous tissue.

Researchers at the University of Belgrade have demonstrated that combining Raman and FT-IR spectroscopy with machine learning algorithms offers a highly accurate, non-destructive method for identifying seed varieties in lettuce, paprika, and tomato.

A compilation of articles that explore the role of Raman spectroscopy in cancer research is presented.

A new comparative study shows that scientific CMOS (sCMOS) cameras could rival traditional CCD detectors in certain Raman CARS spectroscopy applications, offering faster readout and dynamic range despite slightly higher noise levels.

Researchers from Guangdong Polytechnic Normal University highlight how combining Raman spectroscopy with machine learning enables rapid, non-destructive, and highly accurate analysis of fruit quality, offering transformative potential for food safety and agricultural diagnostics.