Chemometrics without Equations

September 29-30, 2008
Reno, Nevada at FACSS

November 17, 2008
Somerset, New Jersey at EAS

Eigenvector Research, Inc. is pleased to offer Chemometrics without Equations (or Hardly Any) at two conferences in Fall of 2008. This the Federation of Analytical Chemistry and Spectroscopy Societies, FACSS:2008, and the Eastern Analytical Symposium, EAS 2008. More information about the course can be found below.

Target Audience

Chemometrics Without Equations (or Hardly Any) 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.

Course Description

Chemometrics without Equations concentrates on two areas of chemometrics: 1) exploratory data analysis and pattern recognition, and 2) regression. Participants will learn to safely apply techniques such as Principal Components Analysis (PCA), Principal Components Regression (PCR), and Partial Least Squares (PLS) Regression. Examples will include problems drawn from process monitoring and quality control, predicting product properties, and others. The target audience includes those who collect and/or manage large amounts of data that is multivariate in nature. This includes bench chemists, process engineers, and managers who would like to extract the most information from their measurements. The course will finish with a short section on how to apply these models for online predictions, Multivariate Statistical Process Control and inferential sensing.

At FACSS:2008, students will work problems using MATLAB and PLS_Toolbox on computers provided (maximum of two students per computer). Sorry, computers will not be provided at EAS, but we do encourage particpants to bring their laptops with MATLAB installed.

About the Instructors

Chemometrics without Equations was developed by Dr. Donald Dahlberg, Professor Emeritus of Chemistry at Lebanon Valley College. Dahlberg earned his Ph.D. in Physical Chemistry from Cornell University. Don got involved in Chemometrics while on sabbatical in 1988 at the Center for Process Analytical Chemistry at the University of Washington. Upon returning to LVC, he taught chemometrics to undergraduate students for over a decade. Although retired from the classroom, he continues do consulting and supervises undergraduate research in industrial chemometrics. He wrote and teaches this workshop so that those not fluent in matrix algebra can take advantage of the powerful tool of chemometrics. Don will be assisted by PLS_Toolbox creator and Eigenvector co-founder Barry M. Wise. Dr. Wise holds a doctorate in chemical engineering and has experience in a wide variety of applications spanning chemical process monitoring, modeling and analytical instrument development. He has extensive teaching experience, having presented over 100 chemometrics courses. Dr. Wise has also taught MATLAB and SIMULINK for The MathWorks, Inc.

Class Schedule

Typical days in each course will be as follows:

  8:00 -  8:30  Check-in
  8:30 - 10:00  Instruction
 10:00 - 10:15  Break 
 10:15 - 12:00  Instruction
 12:00 -  1:00  Lunch 
  1:00 -  3:00  Instruction
  3:00 -  3:15  Break  
  3:15 -  5:00  Instruction

A revised schedule will be posted when exact details become available.

Course Fees, Registration and Deadlines

Course fees, registrations and deadlines are determined by the individual conference organizations. Please see course information for FACSS:2008 or EAS 2008 as appropriate.

Chemometrics without Equations Course Outline

1. Introduction
1.1 what is chemometrics?
1.2 resources

2 Pattern Recognition Motivation
2.1 what is pattern recognition?
2.2 relevant measurements
2.3 some statistical definitions

3. Principal Components Analysis
3.1 what is PCA?
3.2 scores and loadings
3.3 interpretation
3.4 supervised and unsupervised pattern recognition
3.5 examples

4. Regression
4.1 what is regression?
4.2 classical least squares (CLS)
4.3 inverse least squares (ILS)
4.4 principal components regression (PCR)
4.5 partial least squares regression (PLS)
4.6 examples

5 On-line Application
5.1 clients and servers
5.2 available technologies (COM, ActiveX, etc.)
5.3 using MATLAB and PLS_Toolbox on-line

6. Summary

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