Jerome Workman, Jr.

ATR FT-IR shows polyethylene and polypropylene particles were common in facial scrubs and creams.

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.

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 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.

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 team of scientists from institutions in Brazil have demonstrated the effectiveness of handheld near-infrared (NIR) spectroscopy in rapidly detecting pesticide residues in intact fruits.

A team of researchers from universities in China have developed a rapid, smartphone-integrated sensor system that uses uranium-based fluorescent probes to detect pesticides and antibiotics in food samples with exceptional speed and selectivity.

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.

In a 2025 study, Indian researchers demonstrated that combining near-infrared (NIR) spectroscopy with aquaphotomics enables rapid, non-destructive detection of adulterants in honey by analyzing changes in water’s spectral behavior. Using chemometric models, they accurately identified and quantified six common adulterants, offering a powerful tool for food authenticity and quality control.

Researchers in Bangladesh have developed a rapid, non-destructive method to detect honey adulteration using UV-Vis-NIR spectroscopy paired with machine learning. Their findings could protect consumers and support food quality enforcement.

This tutorial introduces how NIR spectroscopy works for honey analysis, explores practical workflows, discusses real-world applications, and outlines best practices for implementing this technique in food labs.

Mississippi State University researchers show that mid-infrared (MIR), a.k.a. infrared (IR), portable spectrometers, combined with calibration transfer techniques, can match lab instruments for soil property analysis.

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 team from the University of Cordoba demonstrated that a portable near-infrared spectral sensor can accurately assess olive oil quality, offering a practical, low-cost alternative to laboratory methods.

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.

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.

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.

DOGE-related federal funding cuts have sharply reduced salaries, lab budgets, and graduate support in academia. Researchers view the politically driven shifts in priorities as part of recurring systemic issues in U.S. science funding during administrative transitions. The impact on Federal laboratories has varied, with some seeing immediate effects and others experiencing more gradual effects. In general, there is rising uncertainty over future appropriations. Sustainable recovery may require structural reforms, leaner administration, and stronger industry-academia collaboration. New commentary underscores similar challenges, noting scaled-back graduate admissions, spending freezes, and a pervasive sense of overwhelming stress among faculty, students, and staff. This article addresses these issues for the analytical chemistry community.

Researchers in Scotland have developed a drone-mounted infrared imaging system that can detect and map methane gas leaks in real time from up to 13.6 meters away. The innovative approach combines laser spectroscopy with infrared imaging, offering a safer and more efficient tool for monitoring pipeline leaks and greenhouse gas emissions.

Researchers in Northeast China have demonstrated a new approach using drone-mounted multispectral imaging to monitor and predict soybean bacterial blight disease, offering a promising tool for early detection and yield protection.

A Virginia Tech study has combined drone-mounted NIR hyperspectral imaging (400 nm to 1100 nm) and AI to estimate soil moisture at root depths with remarkable accuracy, paving the way for smarter irrigation and resilient farming.

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 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.

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%.

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.

A team of researchers from Spanish institutions has found that polystyrene used in healthcare packaging shows strong resistance to UVC sterilization, with minimal chemical degradation detected using FT-IR and Raman spectroscopy.