E17-03: Chemometrics Without Equations Parts 1 & 2

Two-Day Course
Sunday, Nov. 12 – Monday, Nov. 13; 8:30am – 5:00pm

Dr. Donald Dahlberg, Lebanon Valley College (Emeritus), Annville, PA
Dr. Barry Wise, Eigenvector Research, Wenatchee, WA

COURSE DESCRIPTION
This is a combination of two one-day courses: Chemometrics Without Equations and Intermediate Chemometrics Without Equations.  A discount will be offered for the combined course over separately registering for the two one-day courses.  See course descriptions for Chemometrics Without Equations and Intermediate Chemometrics Without Equations.

WHO SHOULD ATTEND

This course is designed for those who wish to explore the problem-solving power of chemometric tools, but are discouraged by the high level of mathematics found in many software manuals and texts. Course emphasis is on proper application and interpretation of chemometric methods as applied to real-life problems.  The objective is to teach in the simplest way possible so that participants will be better chemometrics practitioners and managers.

Day Two should be of interest to those involved in chromatography and hyperspectral imaging, following changes over time or location, and the analysis of materials in complicated matrices. It presents the material without the use of high-level mathematics found in many software manuals and texts. Course emphasis is on proper application and interpretation of chemometric methods as applied to real-life problems. The objective is to teach in the simplest way possible so that participants will be better chemometrics practitioners and managers.

TOPICS

Day One Day Two
1. Introduction
* What is chemometrics
* Resources
2. Pattern Recognition Motivation
* What is pattern recognition
* Relevant measurements
* Some statistical definitions
3. Principal Component Analysis
* What is PCA
* Scores, Loadings and Eigenvalues
* Interpretation
* Cluster Analysis
* Mean Centering and Autoscaling
* SIMCA
* Savitzky-Golay Derivatives
* Examples
4. Regression
* What is regression
* Classical Least Squares (CLS)
* Inverse Least Squares (ILS)
* Principal Component Regression (PCR)
* Partial Least Squares Regression (PLS)
* Multiplicative Signal Correction
5. Resources
6. Using Regression for Pattern Recognition
* Partial Least Squares – Discriminant Analysis
7. Summary
1. Introduction
* Review of basic chemometric techniques
include PCA, PCR, PLS
2. Multivariate Mixture Analysis
* The probability of overlapping peaks in chromatography
* Determining the number of components in a mixture
* Evolving factor analysis (EFA) and related techniques
* Multivariate curve resolution (MCR) and alternating least
squares (ALS)
* Examples of time and spatial dependent systems
* Self modeling mixture analysis (SMA)
3. Multivariate Image Analysis (MIA)
* Intro to 3-way arrays and simple visualizations and size/shape analyses
* Practical multivariate image analysis (MIA)
- PCA, SIMCA, PLSDA and clustering
* Variance Filtering for Images:
- Maximum autocorrelation factors, maximum difference
factors, generalized least squares weighting (MAF, MDF, GLSW)

ABOUT THE INSTRUCTORS
Dr. Donald Dahlberg (Course Director) is Professor Emeritus of Chemistry at Lebanon Valley College. Dr. Dahlberg earned a B.S. in Chemistry from the University of Washington and a Ph.D. in Physical Chemistry from Cornell University. After decades of doing research in the area of Physical Organic Chemistry, he got involved in Chemometrics while on sabbatical in 1988 at the Center for Process Analytical Chemistry at Washington. There he learned chemometrics in the Bruce Kowalski group (co-founder of chemometrics). Upon returning to LVC, he taught chemometrics to undergraduate students for over a decade. Although retired from the classroom, he continues to do consulting and supervises undergraduate research in industrial chemistry.  Dr. Dahlberg wrote and teaches this course so that those not fluent in matrix algebra can take advantage of the powerful tool of chemometrics.

Dr. Neal B. Gallagher, Vice President and co-founder of Eigenvector Research, Inc., received B.S. degrees in Chemical Engineering and Engineering Physics from the University of Colorado in 1985, an M.S. in Chemical Engineering from the University of Washington in 1987, and a Ph.D. in Chemical Engineering with a minor in mathematics from the University of Arizona in 1992. During his studies he performed mathematical modeling on mass transfer and reacting systems, became familiar with polymeric composites and polymer physics, and also studied aerosol formation in combustion systems. During a post-doctoral appointment at Battelle Pacific Northwest National Laboratory in 1994, Neal became interested in chemometrics and applied multivariate analysis. On January 1, 1995 he co-founded Eigenvector Research, Inc. with Barry M. Wise, Ph.D. Eigenvector Research specializes in chemometrics consulting, algorithm development, short courses and software.

Neal has authored and contributed to several scientific publications and is a co-author of PLS_Toolbox advanced chemometrics software for use with MATLAB and it’s companion stand-alone product Solo. His consulting projects and teaching assignments include a wide variety of applications. Recently, he has been working extensively in developing algorithms for hyperspectral image analysis with an emphasis on anomaly and target detection. Neal is actively involved in new algorithm development that enables bleeding edge research and methods to be used in a practical environment and has a strong interest in making chemometrics accessible to the widest audience possible. He is a member of Sigma Xi, AAAS and IEEE.