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Articles

Our annual review of new products for atomic and molecular spectroscopy, including details by category and highlights of overarching trends.

2022 Review of Spectroscopic Instrumentation

High-performance instrumentation requires many critical components. We focus here on energy sources, lasers, and detectors.

A Survey of Basic Instrument Components Used in Spectroscopy, Part 1: Sources, Lasers, and Detectors

In this study, we propose a low-altitude unmanned aerial vehicle (UAV) hyperspectral visible near-infrared (vis-NIR) remote sensing hardware platform, which combines efficiency and accuracy for high-precision remote sensing-based ecological surveys and statistical data collection on grassland desertification. 

Deep Learning Classification of Grassland Desertification in China via Low-Altitude UAV Hyperspectral Remote Sensing

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

This issue is a compilation of five peer-reviewed articles on the combined application of UV-vis-NIR spectral data with advanced chemometrics.

Advances in UV-Vis-NIR Spectroscopy: A Peer-Reviewed Special Issue

Classification and identification of different wood species are demonstrated
using a portable near-infrared spectrometer, combined with four spectral pretreatment methods and three pattern recognition methods. Additional chemometric tools were used for comprehensive evaluation of classification model accuracy and complexity.

Rapid Identification of Wood Species Based on Portable Near-Infrared Spectrometry and Chemometrics Methods

Given that grape seed oil has shown beneficial effects for consumers, there is a interest in measuring oil quality and potential adulteration. This study demonstrates an effective near-infrared (NIR) spectroscopy method, using a series of machine learning approaches for wavelength variable selection, to rapidly discriminate grape seed oil adulteration.

Rapid Quality Discrimination of Grape Seed Oil Using an Extreme Machine Learning Approach with Near-Infrared (NIR) Spectroscopy

Spectral reflectance is a non-destructive method that is applicable to remote sensing and may be used to measure the chlorophyll content in a crop, which indicates the photosynthetic capacity, growth cycles, and degrees of stress (such as disease, insect infestation, and heavy metal stress) on plant ecosystems. This vis-NIR spectral reflectance method measures leaf chlorophyll using a wavelet analysis algorithm approach.

Model for Retrieving Leaf Chlorophyll Using the Wavelet Analysis Algorithm with the Prospect Radiative Transfer Model and Vis-NIR Spectra

Depletion of modern mineral resources due to continuous exploitation and utilization makes it economically necessary to quickly identify the locate sources of low-grade ore. Here, we propose a vis-NIR remote sensing method to determine copper content in mining areas as well as to measure the environmental impact of surface mining methods.

Inversion of Low-Grade Copper Mining Areas Based on Spectral Information and Remote Sensing Data Using Vis-NIR

Regulations have been imposed to set legal limits of nitrate and nitrite in water worldwide. In this study, a highly accurate and optimized ultraviolet (UV) spectroscopy method is proposed to simultaneously monitor nitrate and nitrite for rapid determination and continuous monitoring in environmental water applications.

Simultaneous Detection of Nitrate and Nitrite Based on UV Absorption Spectroscopy and Machine Learning

A model has been developed to predict the “cold” or “hot” nature of Chinese medicines based on UV spectral data.

UV-vis Spectroscopy