We present this special issue of Spectroscopy on ultraviolet (UV), visible (vis), and near-infrared (NIR) spectroscopy. This special issue features five specially selected and peer-reviewed papers that highlight exciting and important new developments in the potential of laboratory and remote sensing, combined with chemometric methods, to the application of UV-vis-NIR spectral data. Combining UV-vis-NIR spectra data and chemometrics provides a set of powerful analytical tools capable of discriminant analysis, classification, identification, and quantitative analysis for a variety of important applications. The UV-vis-NIR spectral regions reveal the details of electronic and molecular information for solid, liquid, and gas phases for natural and synthetic materials. The advantages of these spectral regions often include little or no sample preparation, capabilities for remote sensing, and rapid data acquisition and analysis. When multipurpose machine learning and other chemometric approaches are used for data analysis, the results can be surprising and dramatic. Many of these papers often include detailed chemometric terms and equations and we hope you will enjoy exploring these data analysis methods for your own use. The selected papers for this digital issue of Spectroscopy include the specific aspects of spectral data collection, data preprocessing, and chemometric model development.
Our first paper demonstrates classification and identification of different wood species using a portable near-infrared spectrometer, combined with four spectral pretreatment methods and three pattern recognition methods. Additional chemometric tools are used for comprehensive evaluation of classification model accuracy and complexity.
In the second paper, an effective NIR spectroscopy method is described, using a series of machine learning approaches for wavelength variable selection, to rapidly discriminate grape seed oil adulteration. Grape seed oil has shown beneficial effects for consumers as a dietary supplement, and there is now an interest in measuring grape seed oil for oil quality and potential.
In our third selected paper, a vis-NIR spectral reflectance method is proposed that measures leaf chlorophyll using a wavelet analysis algorithm approach. Here, spectral reflectance is shown as a non-destructive method that is applicable to remote sensing for chlorophyll content in a crop. Measured chlorophyll content is a recognized indicator for photosynthetic capacity, growth cycles, and degrees of stress on plant ecosystems.
Our fourth paper proposes a vis-NIR remote sensing method to determine copper content in mining areas as well as inferred measurement of the environmental impact of surface mining methods. This analytical method is important due to depletion of modern mineral resources from continuous exploitation and utilization, making it economically necessary to quickly identify and locate sources of low-grade copper ore.
In the final paper of this edition, a highly accurate and optimized UV spectroscopy method is proposed to simultaneously monitor nitrate and nitrite for rapid determination and continuous monitoring in environmental water applications. Recent regulations have been imposed to set legal limits of nitrate and nitrite in water worldwide, making accurate measurements of these analytes an important water quality analysis requirement.
Jerome Workman, Jr. is the senior technical editor of LCGC North America and Spectroscopy. Direct correspondence to: jworkman@mjhlifesciences.com
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers 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%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This 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.
Combining AI and NIR Spectroscopy to Predict Resistant Starch (RS) Content in Rice
June 24th 2025A new study published in the journal Food Chemistry by lead authors Qian Zhao and Jun Huang from Zhejiang University of Science and Technology unveil a new data-driven framework for predicting resistant starch content in rice
New Spectroscopy Methods Target Counterfeit Oral Medication Syrups
June 23rd 2025Researchers at Georgia College and Purdue University have developed a fast, low-cost method using Raman and UV–visible spectroscopy combined with chemometric modeling to accurately screen and quantify active ingredients in over-the-counter oral syrups, helping to fight counterfeit medications.