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Howard Mark serves on the Editorial Advisory Board of 

 and runs a consulting service, Mark Electronics that provides assistance, training, and consultation in near-IR spectroscopy as well as custom hardware and software design and development.

Articles

The past decision to use binary representation in computer architectures affects the results of chemometric-based outputs, especially if different data values are used.

Decimal Versus Binary Representation of Numbers in Computers

We examine variations of the multiple linear regression (MLR) algorithm confer special properties on the model that the algorithm produces and critique the use of derivatives in calibration models.

Data Transforms in Chemometric Calibrations: Simple Variations of MLR, Part 2

Software tools for ICP-MS and ICP-OES can help analysts to simplify method setup and reduce the potential for errors.

Analysis of Elements in Snack Foods: A Closer Look at Pepperoni, Rice Noodles, Frozen Dinners, and Pizza

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

Raw data produced by an NIR instrument undergoes some sort of processing, or transformation, to make them easier to use. In this series, we explore options for that data transformation, starting with multiple linear regression (MLR).

Data Transforms in Chemometric Calibrations: Application to Discrete-Wavelength Models, Part 1: The Effect of Intercorrelation of the Spectral Data

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.

As forensic analysis continues to advance, such as in the understanding of source identification and analysis of trace quantities of bodily fluids, spectroscopic techniques and machine learning are playing a significant role. Igor K. Lednev, a chemistry professor at the University at Albany, SUNY, in Albany, New York, has been working in this field with his team. The analytical methods currently under investigation include Raman spectroscopy, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy, and advanced chemometric classification and analysis methods. We recently interviewed him about his work. 

Investigating Forensics Applications of Raman Spectroscopy, ATR FT-IR, and Chemometrics

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

The second in a two-part series highlighting key explanatory or tutorial references for each of 29 chemometric methods.

Data Analytics, Statistics, Chemometrics, and Artificial Intelligence