E22: Introduction to Chemometrics without Equations (Part 1)

One-Day Course 
Date to be announced; 8:30am – 5:00pm

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

This introductory course concentrates on two areas of chemometrics: (1) exploratory data analysis and pattern recognition, and (2) regression. Participants learn to apply techniques such as Principal
Component Analysis (PCA), SIMCA, Principal Component Regression (PCR), and Partial Least Squares
regression (PLS) safely. The most commonly used methods of outlier detection and data pretreatment
with also be illustrated. The course emphasizes understanding the chemometric process without having
to learn matrix algebra.

This course can be combined with Intermediate Chemometrics without Equations (Part 2) as a two-day course.  A discount will be offered over separately registering for the two one-day courses.

This course on Chemometrics without Equations (or Hardly Any) is designed for those with a desire 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.

1. Introduction
     a. What is chemometrics
     b. Resources
2. Pattern Recognition Motivation
     a. What is pattern recognition
     b. Relevant measurements
     c. Some statistical definitions
3. Principal Component Analysis
     a. What is PCA
     b. Scores, loadings and Eigenvalues
     c. Interpretation
     d. Cluster analysis
     e. Mean centering and autoscaling
     f. SIMCA
     g. Savitzky-Golay derivatives
     h. Examples 
4. Regression
     a. Motivation for regression
     b. Classical least squares (CLS)
     c. Multiple Linear Regression (MLR)
     d. Principal Component Regression (PCR)
     e. Partial least squares regression (PLS)
     f. Partial least squares discriminate analysis (PLS-DA)

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, PLS_Toolbox co-author and co‐founder of Eigenvector Research, Inc., holds a doctorate in Chemical Engineering and has experience in a wide variety of applications spanning chemical process monitoring, hyperspectral image analysis, anomaly detection, quantification and classification, regression modeling and analytical instrumental development. He has extensive teaching
experience including Eigenvector University and dozens of chemometric courses.