UV-vis Spectroscopy

Here are ten main unsolved problems in vibrational and atomic spectroscopy, each accompanied by a tutorial-style synopsis suitable for advanced practitioners or graduate-level students. Each of these tutorials, spanning advanced spectroscopy modeling, chemometrics, machine learning (ML) interpretability, and standardization, consists of a descriptive article. Each piece is well-referenced (with detailed matrix equations, radiative transfer models, chemometric derivations, and so forth), and includes the following. • Special focus on each topic—including mathematical derivations in matrix notation. • Conservative, verifiable content anchored to established reference sources. • Appropriate tutorial article structure: Title, Summary, Abstract, Introduction, Theory with equations, Examples, Discussion & Future Research, and References.

A new study reveals that resveratrol binds to peanut protein arachin through hydrophobic and hydrogen-bond interactions, enhancing protein stability and offering valuable insights for developing functional peanut-based food products.

This tutorial explains how baseline drift and multiplicative scatter distort spectroscopic data, reviews correction techniques such as MSC, SNV, EMSC, wavelet-based detrending, and AsLS baseline estimation with matrix-based derivations, and explores emerging data-driven scatter modeling strategies and future research directions.

Astronomers have captured the first detailed optical spectrum of 3I/ATLAS, the third known interstellar object to visit our Solar System. Using the VLT’s MUSE instrument, the team finds a red, dust-dominated coma with no detectable gas emissions, offering a rare glimpse into the composition of alien comets.

This tutorial examines the development of universal spectral libraries, reviewing standardization efforts, mathematical frameworks, and practical examples across multiple spectroscopies, while emphasizing metadata harmonization, FAIR principles, and the emerging role of AI in building interoperable, machine-readable repositories. This remains an unsolved problem in spectroscopy.

New observations of interstellar comet 3I/ATLAS, the third known interstellar object ever to visit our solar system, reveal unexpected activity and composition, challenging many previous assumptions about interstellar objects.

Newly captured spectroscopic data of the third-ever known interstellar object, 3I/ATLAS, reveals a red, organic-rich surface and an enigmatic early dust coma, providing unprecedented insight into materials from beyond our solar system.

Astronomers have conducted detailed spectrophotometric observations of the mysterious interstellar comet 3I/ATLAS using the Palomar 200-inch and Apache Point telescopes. The findings reveal unexpected activity and unique spectral features, enhancing our understanding of this cosmic visitor.

The interstellar object 3I/ATLAS, discovered in July, has captivated astronomers with its unusual characteristics. While some scientists attribute its behaviors to natural cometary processes, others propose more speculative theories, including the possibility of it being an artificial probe. This article examines both mainstream and speculative interpretations of 3I/ATLAS's anomalous features. Other news articles this week will look specifically at the spectroscopic results of telescopes recently analyzing this mysterious object.

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

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.

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.

A recent study demonstrated that UV–visible (UV-vis) spectroscopy combined with machine learning (ML) can provide a fast, cost-effective, and automated method for detecting biological contamination in microalgae cultures.

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.

Researchers at Santiago de Compostela University (Santiago, Spain) find ultraviolet–visible (UV–vis) spectroscopy can detect and quantify post-COVID condition with high accuracy, paving the way for real-time clinical use.

A new study reveals that anthocyanin-rich fruit extracts degrade rapidly under sunlight but remain most stable in cold, dark storage.

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.

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.

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.

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.

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 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.

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.

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.