Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part I

This installment, and the next one, comprise lists of four key explanatory or tutorial references for each of 29 chemometric topics described in a previous article, with the addition of programming platforms often used for chemometrics. The references are selected as being particularly helpful to explain the use of each technique with spectroscopic data whenever possible. This reference list cannot be exhaustive, due to space limitations, but it is extensive and comprehensive. Included is a series of tables listing the key reference numbers for each chemometric technique.

The August 2020 installment of the “Chemometrics in Spectroscopy” column was entitled, “Survey of Chemometric Methods Used in Spectroscopy” (1). In that article, we delineated 29 common chemometric methods (or techniques) in use today by spectroscopists, and selected a single literature reference for each method. This current installment, and the one that will follow, continue this theme, forming a two-part series. In this two-part series, the respective chemometric methods, with their corresponding literature reference numbers, are given in Tables I through V. The tables for the two-part series include the following topics (in order of appearance); in all, 30 chemometrics topics will be covered.

• Table I: Signal preprocessing comparison table (alphabetical order)
• Table II: Component analysis comparison table (alphabetical order)
• Table III: Quantitative (calibration) methods comparison table (alphabetical order)
• Table IV: Qualitative (classification) methods comparison table (alphabetical order)
• Table V: Programming platforms often used for chemometrics (alphabetical order)

Part I includes Tables I and II with references. Part II will include Tables III through V with corresponding literature references.

The science of chemometrics has rapidly advanced to now be included in the broader field of data analytics. There are now degree programs in data analytics and all types of data processing related to computer science or specific other data fields, including econometrics, biometrics, biomedical statistics, data mining, chemometrics—and software design and architecture specifically for design engineering, control systems, manufacturing engineering, robotics, instrument control, predictive modeling and learning, and many other fields. This includes artificial intelligence and its subfields of machine learning algorithms (used in data filtering and computer imaging applications), computational statistics and optimization, supervised, semi-supervised, and unsupervised learning, and various forms of predictive modeling. Other machine learning family members include deep learning (deep structured learning), artificial neural networks, and specialized learning algorithms. A menagerie of techniques or combinations of techniques is continuously being introduced, with ever-changing names. However, the basic mathematical concepts behind these changing names are much the same, and advances are mostly in name, the computer processing power used, and in the combinations and applications of the data analysis algorithms used. At some future point, we hope to at least summarize the nomenclature of these fields in this column.

Description of References

The general references 1–12 are basic descriptions of chemometrics reviews specific to spectroscopy applications. Then, a set of four references are given in Tables I through IV for each chemometric method name given, and five references are given for each computer software platform introduced in Table V. The references for the two-part series will be sequential from 1 to 148 so that the series can be viewed as a single body of work.

Introduction to the Tables

Many excellent videos, technical notes, online sources, and published articles exist for the purpose of instruction and understanding of algorithms and chemometrics topics. Here, we have selected a set of papers from the technical literature that includes chemometrics reviews for spectroscopy (1–12) as well as a set of articles for each of 30 selected chemometrics topics (including software platforms): We included four references for each topic that we considered most applicable to Spectroscopy readers, and we also tried to include those references that might be considered “classic” or tutorial papers. As we specifically delve into each subject or topic, we will include additional references that will be helpful to the reader in understanding and using these various chemometric methods.

Here in part I of this series, Table I presents the references for various signal preprocessing techniques. These data processing methods are often used prior to the application of data exploration, or prior to qualitative or quantitative methods. Table II lists references for component analysis techniques used mostly for data exploration and discovery. In Part II of the series, Table III will show the variety of references for quantitative (calibration) methods used to take raw or preprocessed data and compute predictive calibration models for quantitative determination of physical or chemical parameters in a dataset. Table IV will provide references for the qualitative (classification) methods used to take raw or preprocessed data and compute predictive calibration models for qualitative (classification) of different groups or types of samples or of physical or chemical parameters in a dataset. Table V will include references for using the most common programming languages or platforms for general data interpretation using chemometrics or other statistical analysis.

References

Chemometrics Reviews for Spectroscopy

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Signal Preprocessing

1. Baseline Subtraction

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2. Derivative Preprocessing

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3. Detrending

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4. Mean Centering

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5. Multiplicative Signal Correction

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6. Normalization

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7. Standard Normal Variate

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8. Successive Projections Algorithm (SPA)

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9. Wavelets

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Component Analysis

10. Classical Least Squares (CLS)

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11. Independent Component Analysis (ICA)

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12. Inverse Adding Doubling (IAD)

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13. Multivariate Curve Resolution (MCR)

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14. Principal Components Analysis (PCA)

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Jerome Workman, Jr. serves on the Editorial Advisory Board of Spectroscopy and is the Senior Technical Editor for LCGC and Spectroscopy. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.

Howard Mark serves on the Editorial Advisory Board of Spectroscopy, and runs a consulting service, Mark Electronics, in Suffern, New York. Direct correspondence to: SpectroscopyEdit@mmhgroup.com ●