Ep. 31: Clarifying the Meaning of Chemometrics, Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks (NNs)
Welcome to “Analytically Speaking,” the podcast from LCGC International and Spectroscopy.
Here in Episode #31, podcast host Dr. Jerry Workman speaks with Dr. Barry M. Wise, Founder and President of Eigenvector Research, Inc. about the meaning of the terms chemometrics, artificial intelligence (AI), machine learning (ML), and neural networks (NNs) within the context of analytical chemistry and process analysis.
References and Further Reading
“What is Chemometrics and how does it relate today to Artificial Intelligence and Machine Learning in Analytical Chemistry and Process Analysis.”
Website (Eigenvector Research Incorporated):
https://eigenvector.com/about-eigenvector-research-inc/staff-associates/barry-m-wise-ph-d/
Website (LinkedIn):
https://www.linkedin.com/in/barry-m-wise-151b9112/
Google Scholar:
https://scholar.google.com/citations?hl=en&user=spk0keMAAAAJ
Podcast Referenced Articles
(1) Blog post about chemometrics terms: https://eigenvector.com/we-used-to-call-it-chemometrics/
(2) Gray models like the Gray-CLS Barry has been working with: https://eigenvector.com/aiovg_videos/evri-thing-you-need-to-know-about-gray-classical-least-squares/
(3) Manifold learning techniques like UMAP and t-SNE for data visualization/pattern recognition and compression: https://eigenvector.com/aiovg_videos/evri-thing-you-need-to-know-about-t-sne-umap-in-pls_toolbox-solo/
(4) B.M. Wise and R.T. Roginski, “Model Maintenance: the unrecognized cost in PAT and QbD,” Chemistry Today 2015, 33 (2) March/April, 38–43.
Recent and Classic Articles
(1) Yan, H.; Neves, M. D. G.; Wise, B. M.; Moraes, I. A.; Barbin, D. F.; Siesler, H. W. The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products. Molecules 2023, 28, 7891. https://doi.org/10.3390/molecules28237891
(2) Marchi, L.; Krylov, I.; Roginski, R. T.; Wise, B.; Di Donato, F.; Nieto‐Ortega, S.; Pereira, J. F. Q.; Bro, R. Automatic hierarchical model builder. J. Chemom. 2022, 36 (12), e3455. https://doi.org/10.1002/cem.3455
(3) Wise, B.M., 2022. Teach Chemometrics in short course format. J. Chemom. 2022, 36 (5), e341. https://doi.org/10.1002/cem.3411
(4) Wise, B. M.; Shaver, J. M. Detection of cervical cancer from evoked tissue fluorescence images using 2- and 3-way methods. Proc. SPIE 10872, Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XIX, 1087210 (27 February 2019). https://doi.org/10.1117/12.2516584
(5) Wise, B. M.; Roginski, R. T. Model maintenance: the unrecognized cost in PAT and QbD. Chim. Oggi 2015, 33 (2), 38–43. https://doi.org/10.1002/cem.2634
(6) Gallagher, N. B.; Shaver, J. M.; Bishop, R.; Roginski, R. T.; Wise, B. M. Decompositions using maximum signal factors. J. Chemom. 2014, 28 (8), 663–671. https://doi.org/10.1002/cem.2634
(7) Wold, S.; Høy, M.; Martens, H.; Trygg, J.; Westad, F.; MacGregor, J.; Wise, B. M. The PLS model space revisited. J. Chemom. 2009, 23 (2), 67–68. https://doi.org/10.1002/cem.1171
(8) Wise, B. M.; Gallagher, N. B. The process chemometrics approach to process monitoring and fault detection. J. Process Control 1996, 6 (6), 329–348. https://doi.org/10.1016/0959-1524(96)00009-1
(9) Wise, B. M.; Gallagher, N. B.; Butler, S.W.; White, D. D.; Barna, G. G. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 1999, 13 (3‐4), 379-–96. https://doi.org/10.1002/(SICI)1099-128X(199905/08)13:3/4<379::AID-CEM556>3.0.CO;2-N
More about our hosts:
Dwight Stoll, PhD:
Dwight R. Stoll is a professor of chemistry at Gustavus Adolphus College in St. Peter, Minnesota. He received his PhD from the University of Minnesota, under Professor Peter Carr, working on the development of fast, comprehensive two-dimensional liquid chromatography (2D-LC). Stoll’s current primary research focus is on the development of 2D-LC for both targeted and untargeted analyses. Active research projects in his laboratory touch on most aspects of multidimensional separation methodologies, including optimization strategies, characterization of selectivity in reversed-phase LC, instrument development, and applications in biopharmaceutical analysis. Stoll is the author or co-author of more than 80 peer-reviewed publications and six book chapters and has instructed numerous short courses in 2D-LC. In 2011 he was the recipient of LCGC’s Emerging Leader in Chromatography Award. In 2017 he received the Georges Guiochon Faculty Fellowship, and was recognized with an Agilent Technologies Thought Leader Award. He is also a member of LCGC’s editorial advisory board and is the editor of the “LC Troubleshooting” column in LCGC.
Jerome Workman, Jr., PhD:
Jerome (Jerry) J. Workman, Jr. is the Executive Editor for LCGC and Spectroscopy. He has held positions as CTO, executive VP, senior research fellow, director, and senior scientist at companies of all sizes, from start-ups to world-leading corporations. He has been an adjunct faculty member of four universities and advised multiple graduate students. He has more than 75 U.S. and international patent applications and 30 issued U.S. and international patents and multiple trade secrets, as well as 500+ technical publications, and 20 reference book volumes on a broad range of spectroscopy and data processing techniques. He has received multiple awards from scientific societies, and has taught annual courses in spectroscopy, chemometrics, and statistics for the AOAC, ACS, ISA, FACSS, and at several universities and corporations. He is a Fellow of the American Institute of Chemists (FAIC), the American Society for Testing and Materials (ASTM), and the Royal Society of Chemistry in the UK (FRSC, CChem, CSci). Jerry holds B.A and M.A degrees from Saint Mary's University of Minnesota, and a PhD degree from Columbia Pacific University working in near-infrared spectroscopy. He is an alumnus of both Columbia University Business School and the MIT Sloan School of Management.
About the Analytically Speaking Podcast:
Analytically Speaking, the podcast from LCGC and Spectroscopy, addresses important issues in separation science and analytical spectroscopy. Topics include new analytical techniques, methods, and approaches; the latest trends; advances in instrument and software technology; practical solutions for specific applications; recent papers in the scientific literature and their applicability; challenges and solutions for data analysis and interpretation; analytical chemistry theory and fundamentals (from advanced research to tutorials and troubleshooting); and more. Our regular hosts are Dwight Stoll, PhD, a professor of chemistry at Gustavus Adolphus College in St. Peter, Minnesota, and Jerry Workman, PhD, a spectroscopist, noted author, and currently the Senior Technical Editor of Spectroscopy and LCGC. Dwight covers separation science and Jerry addresses spectroscopy related topics.
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