Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

High-performance instrumentation requires many critical components. We focus here on energy 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).

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.

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.

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.

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.

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

Igor K. Lednev at the University at Albany SUNY in New York explains advances in forensic analysis using a variety of chemometrics techniques to classify ATR FT-IR and Raman spectra of bodily fluids.

The carefully selected literature references in this curated set describe the application of 29 major chemometric methods used for analyzing molecular spectroscopy data.

L. Scott Ramos will receive the 2021 Eastern Analytical Symposium (EAS) Award for Outstanding Achievements in Chemometrics at the EAS Symposium taking place November 15–17, 2021, in Plainsboro, New Jersey.

“SneakerNet,” or the manual transfer of data using a disk or USB stick from one computer system to another, should be long dead, but this noncompliant transfer process still survives.

The details of applying deep learning algorithms and FT-IR spectra are described for classification research using the spectra of strawberries as an example.

Mathematics is a formal logic system, perhaps the ultimate formal logic system. Here we describe the elegance of the foundations of the mathematics that chemometrics is based on.

We explore how different algorithms and different numbers of factors affect the results.

Spreadsheets are often used to perform GMP-related calculations, but can lead to serious problems and unnecessary risk. We explain why the use of spreadsheets is heavily discouraged in a regulated laboratory environment.

We provide a scorecard of chemometric techniques used in spectroscopy. The tables and lists of reference sources given here provide an indispensable resource for anyone seeking guidance on understanding chemometric methods or choosing the most suitable approach for a given analysis problem.

Chemometrics in Spectroscopy is a collection of column articles that the authors published in Spectroscopy over a period spanning more than two decades. Each article is generally arranged as a chapter in the book, and chapters dealing with the same or similar topics are arranged closely as a section block rather than following the original sequence in the magazine. Although each article or series of articles only discusses one specific topic, collectively, the articles form a comprehensive reference that is a valuable source for readers wanting to learn chemometrics, especially with its applications in spectroscopy.

A newly discovered effect can introduce large errors in many multivariate spectroscopic calibration results. The CLS algorithm can be used to explain this effect. Having found this new effect that can introduce large errors in calibration results, an investigation of the effects of this phenomenon to calibrations using principal component regression (PCR) and partial least squares (PLS) is examined.

In the current data-integrity–centric world, is outsourcing your spectroscopy work a good idea? To answer this question, one must consider several factors.

Calibration transfer involves multiple strategies and mathematical techniques for applying a single calibration database to two or more instruments. Here, we explain the methods to modify the spectra or regression vectors to correct differences between instruments.

A big question in forensic science today is, “How do we best report uncertainty?” The answer to which approach is “best” turns out to be surprisingly complex, for many reasons.

The archnemesis of calibration modeling and the routine use of multivariate models for quantitative analysis in spectroscopy is the confounded bias or slope adjustments that must be continually implemented to maintain calibration prediction accuracy over time. A perfectly developed calibration model that predicted well on day one suddenly has to be bias adjusted on a regular basis to pass a simple bias test when predicted values are compared to reference values at a later date. Why does this problem continue to plague researchers and users of chemometrics and spectroscopy?

When using any regression technique, either linear or nonlinear, there is a rational process that allows the researcher to select the best model.

The statistical methods used for evaluating the agreement between two or more instruments (or methods) for reported analytical results are discussed, with an emphasis on acceptable analytical accuracy and confidence levels using two standard approaches, standard uncertainty or relative standard uncertainty, and Bland-Altman "limits of agreement."

This article describes the application of chemometric methods and statistics for reporting clinical quantitative measurement methods. The equations and terminology are consistent with the Clinical and Laboratory Standards Institute (CLSI) guidelines. These chemometric and statistical methods describe the accuracy and precision of a test method compared to a reference method for a single analyte determination. Part I will introduce these concepts and Part II will discuss the statistical underpinnings in greater detail.

Columnists Howard Mark and Jerome Workman, Jr. take a final look at the topic of principal components, which has been the subject of six previous installments.

This column is a continuation of the set we have been working on to explain and derive the equations behind principal components (1–5). As we usually do, when we continue the discussion of a topic through more than one column, we continue the numbering of equations from where we left off.

For a system of homogeneous equations to have a solution other than the trivial solution, the determinant of the system of equations must be zero.

Howard Mark and Jerome Workman, Jr. continue their discussion of the derivation of the principal component algorithm using elementary algebra.

Howard Mark and Jerome Workman, Jr. continue their discussion of the derivation of the principal component algorithm using elementary algebra.