Near Infrared (NIR) Spectroscopy

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.

Researchers have demonstrated a non-invasive method using milk and near-infrared spectroscopy combined with Aquaphotomics to accurately detect Paratuberculosis in dairy cattle. The technique offers faster, more sensitive diagnosis than traditional methods.

Researchers have demonstrated that visible and near-infrared spectroscopy, combined with chemometric and aquaphotomic analysis, can accurately classify and quantify aflatoxin contamination in white and yellow maize, offering a faster, non-destructive alternative to traditional methods.

Researchers have developed a rapid, non-invasive screening method for esophageal squamous cell carcinoma (ESCC) using near-infrared spectroscopy and aquaphotomics. The approach analyzes plasma water patterns, achieving over 95% accuracy in distinguishing patients from healthy controls

A recent study demonstrated that combining hyperspectral imaging with multivariate curve resolution can non-invasively detect and monitor intestinal necrosis in acute mesenteric ischemia, offering a promising tool for earlier diagnosis and improved patient outcomes.

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.

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.

A recent study presented an AI-enhanced NIRS-XRF fusion spectroscopy method that significantly improves coal classification and quality prediction for coking enterprises.

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 Brazil have demonstrated that near-infrared (NIR) spectroscopy, combined with chemometric modeling, can accurately and non-invasively analyze methane, carbon dioxide, and water content in high-pressure natural gas systems.

Researchers have developed a fast, chemical-free method using near-infrared spectroscopy to accurately analyze the quality of dark chocolate, offering a sustainable alternative to traditional testing techniques.

A new study led by Gaëlle Belleau-Magnat at Université de Sherbrooke reveals that Arctic gossans, analyzed using rover-compatible techniques, may serve as valuable analogs for Martian environments and help guide the search for past life on Mars.

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.

A new bibliometric study published in Infrared Physics & Technology highlights the growing global impact of near-infrared (NIR) spectroscopy in biofuel research, revealing key trends, contributors, and future directions for advancing sustainable energy solutions.

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.

Researchers at INIAV in Portugal have demonstrated that near-infrared (NIR) spectroscopy combined with chemometric algorithms offers a rapid, non-destructive, and accurate method for detecting harmful fumonisins in maize, enhancing food safety monitoring.

Researchers at China Agricultural University developed a rapid and accurate spectroscopic method using NIR and FT-IR combined with PLS regression to measure protein content in rice noodles, enhancing quality control for the popular river snail rice noodle (luosifen) industry.

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.

Researchers from China Agricultural University introduce PeaNet, promising rapid, accurate, and nondestructive protein analysis.

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.

Published in Food Chemistry, researchers from Jiangsu University of Science and Technology and Jimei University use near-infrared (NIR) spectroscopy and machine learning to tackle food adulteration and enhance quality control.

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 from Jiangsu University and Jimei University developed an advanced FT-NIR-based method for food safety monitoring, achieving over 97% accuracy in identifying multiple oil-based contaminants in peanut oil.

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 new review highlights how vibrational spectroscopy techniques like FTIR, NIR, and Raman offer rapid, non-destructive tools for accurately analyzing plant-based protein content and structure.

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 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%.