Near Infrared (NIR) Spectroscopy

A study published in Food Control shows that near-infrared spectroscopy can accurately and non-destructively authenticate the feeding regime of purebred Iberian pigs in vivo.

During 2025, near-infrared (NIR) spectroscopy has accelerated its transition from a mature analytical technique into a digitally enabled cornerstone of biopharmaceutical manufacturing and quality control. Advances in miniaturized instrumentation, process analytical technology (PAT), chemometrics, artificial intelligence (AI), and real-time process control technologies have driven NIR spectroscopy into new roles spanning upstream fermentation, downstream processing, raw material characterization, and continuous manufacturing. This article reviews and contextualizes ten influential peer-reviewed publications from 2025 that collectively define the current state and near-term trajectory of NIR spectroscopy in biopharmaceutical analysis.

Over the past two years, near infrared spectroscopy (NIRS) and related NIR techniques have seen rapid adoption in biomedical research. These developments span non invasive diagnostics, functional monitoring, machine learning integration, point of care probes, and applications in complex clinical settings such as liver fibrosis, viral detection, neonatal care, brain injury, and neurodegenerative disorders. This article synthesizes 10 key publications, highlighting trends, methodologies, and clinical potential.

A recent study shows that handheld near-infrared (NIR) spectroscopy combined with artificial neural networks can rapidly and non-destructively distinguish human from animal bones with high accuracy, offering a practical new tool for on-site forensic investigations.

For Pittcon 2026, the James L. Waters Symposium, scheduled for Monday, March 9, from 2:30 to 4:40 p.m. in Room 221A, turns its focus on Generative artificial intelligence (AI) systems in analytical chemistry, which are increasingly being used for analytical data interpretation, algorithm development, experimental planning, and scientific communication. This article introduces the general concepts of generative AI and its use in spectroscopy.

Artificial intelligence is transforming vibrational spectroscopy by automating calibration, feature extraction, and interpretation across Raman, infrared, near-infrared (NIR), and hyperspectral imaging (HSI) systems. This review of articles highlighted in Spectroscopy during 2025 captures several major developments, spanning data fusion, spectral imaging, and industrial and biomedical applications.

In 2025, the vibrational-spectroscopy community saw a convergence of deep learning, advanced simulation, and portable instrumentation that materially changed how spectra are interpreted and applied.

Vibrational spectroscopy is undergoing a major transformation driven by advances in new AI and machine learning, portable instrumentation, nanofabrication, hyperspectral imaging, and robust chemometrics. These developments are enabling more sensitive measurements, field-deployable analysis, multimodal data fusion, and automated spectral interpretation suitable for real-world industrial and clinical use. As these technologies converge, the field is positioned for a renaissance that may redefine how spectroscopy is practiced by 2030.

This review article highlights how a new review by Da-Wen Sun demonstrates that integrating spectroscopy with chemometric techniques can significantly improve cold chain food quality monitoring, authentication, and overall system efficiency.

A new review by researchers from the University of Waterloo, Sanofi, and McGill University highlights how vibrational and fluorescence spectroscopy are reshaping real-time monitoring of pharmaceutical bioprocesses. The authors detail recent advances in UV-Vis, NIR-MIR, Raman, and fluorescence sensing, supported by modern chemometrics and AI tools.

A recent study demonstrates that near-infrared (NIR) spectroscopy is a fast, cost-effective, and reliable tool for assessing soil and tree ecological traits, offering major potential for large-scale forest conservation and monitoring.

A new perspective article by Anna de Juan and Rodrigo Rocha de Oliveira highlights how hyperspectral imaging (HSI), paired with advanced chemometrics, is redefining process analytical technology (PAT) by coupling chemical specificity with full-field spatial resolution. Their work outlines how HSI surpasses classical spectroscopic PAT tools and enables quantitative, qualitative, and mechanistic insight into chemical processes in real time.

A research team has developed the first short synthetic peptide-based biosensor for real-time tracking of the disease-related protease matrix metalloproteinase-9 (MMP-9), using multi-parametric surface plasmon resonance spectroscopy (MP-SPR).

This tutorial introduces spectroscopy professionals to the operational principles, practical workflows, and laboratory applications of biosensors. It covers core definitions, biosensor types, transduction methods, nanomaterials-enabled strategies, and optical/electrochemical approaches relevant to spectroscopic analysis. Readers will learn how biosensors integrate biological recognition with physicochemical detection, how to implement them in real-world measurement tasks, and how to avoid common technical pitfalls when translating biosensor theory into laboratory practice.

A recent study establishes how particle size, particle shape, phase angle, and ice abundance influence VNIR spectral signatures, providing a refined framework for accurately detecting and quantifying lunar water ice in polar regions.

A new study from Heilongjiang Bayi Agricultural University pioneers rapid, non-destructive detection of illicit food additives using deep learning and near-infrared spectroscopy.

The miniaturization of spectroscopic instruments has reached a remarkable milestone: wearable vibrational spectroscopy. Techniques such as Raman, surface-enhanced Raman scattering (SERS), infrared (IR), and functional near-infrared (fNIRS) spectroscopy are no longer confined to the laboratory bench—they now fit on our bodies, into household devices, and onto industrial equipment. These wearable devices promise continuous, real-time monitoring, offering molecular-level insights for personal health, household management, clinical care, and industrial applications.

Phil C. Williams (1933–2025) was an internationally recognized pioneer in near-infrared spectroscopy whose visionary work transformed grain analysis from chemical assays to rapid, environmentally responsible spectroscopic methods. His lifelong commitment to scientific rigor, mentorship, and practical innovation has left an enduring legacy that continues to shape industrial spectroscopy for grain analysis that impacts the global economy.

This second part of the Recent Research in Chemometrics and AI for Spectroscopy article surveys current and emerging applications of artificial intelligence (AI) in spectroscopy, highlighting explainable AI (XAI), deep learning, and generative AI frameworks.

This first article in a two-part series introduces the foundations and terminology of AI as applied to chemometrics, defines key algorithmic approaches, and explores their growing role in spectral data analysis, model quantitative calibration, classification, and interpretability.

A recent study explored using near-infrared (NIR) spectroscopy as a rapid, nondestructive method for accurately classifying THC levels in cannabis.

Using optical and near-infrared spectroscopy, researchers have identified crystalline water ice as the likely driver of explosive outbursts on comet 12P/Pons-Brooks. Their findings link this dramatic cometary activity to a process once observed in comets 17P/Holmes and 332P/Ikeya–Murakami.

A new international review highlights how hyperspectral imaging (HSI) is revolutionizing diverse fields—from counterfeit detection and agriculture to cancer diagnostics—by capturing unprecedented spectral detail invisible to traditional cameras. The study identifies major advances, challenges, and the growing role of artificial intelligence in real-time HSI applications.

Researchers have developed a new method combining unmanned aerial vehicle (UAV) hyperspectral imaging with satellite data to monitor chlorophyll-a (Chla) and total nitrogen (TN) concentrations in coastal wetland waters. Their approach enhances the precision and scalability of water quality assessments, providing a model for managing eutrophication in fragile ecosystems.

Researchers at the European Space Research and Technology Centre (ESTEC) have developed a new framework for onboard hyperspectral image processing that uses deep learning to analyze massive volumes of spectral data in real time. Their review highlights lightweight neural networks, generative models, and hardware accelerators as key technologies shaping the next generation of spaceborne Earth observation.