Using the Advanced Features of PLS_Toolbox/Solo 6.5

December 8-9, 2011
Hyatt Regency
New Brunswick, NJ, USA

Eigenvector Research, Inc. is pleased to offer Using the Advanced Features of PLS_Toolbox/Solo 6.5 at the Hyatt Regency in New Brunswick, New Jersey, USA. More information about the course can be found below.

Target Audience

Using the Advanced Features of PLS_Toolbox/Solo 6.5 is designed for those with a good understanding of basic chemometrics who wish to explore the power of more advanced chemometric tools. While prior experience with PLS_Toolbox or Solo is not required, the course is especially useful for those who have experience with older versions of Eigenvector software and want to learn about newer features.

Course Description

The course concentrates on "how to" perform advanced chemometric analyses and data manipulations. After an introduction to the basic interface, the course shows how to use PLS_Toolbox and Solo to make and view Principal Components Analysis (PCA) and Partial Least Squares (PLS) regression models. More advanced techniques are then demonstrated including Nonlinear Methods, Advanced Preprocessing, Robust Methods, Multivariate Curve Resolution (MCR) and Calibration Transfer. An option second half day includes a review of our MIA_Toolbox and Solo+MIA for Multivariate Image Analysis and our new Design of Experiment (DOE) tools. The course will finish with a question and answer session and time for one-on-one help with the software. Attendees are urged to bring their own data for the second day! This is the best way to see how the tools can help you do your job.

Particpants are encouraged to bring their laptops with Solo 6.5 or PLS_Toolbox 6.5 installed. (Demo copies will work just fine.) User with Eigenvector accounts can download free demos. If you don't have an account, start by creating one.

About the Instructors

The course will be led by Eigenvector's Vice-President Neal B. Gallagher and Chief of Technology Development Jeremy M. Shaver. Drs. Gallagher and Shaver have broad experience in a wide variety of chemometric applications spanning analytical instrument and method development, remote sensing, and chemical process monitoring and modeling. Neal and Jeremy both have extensive teaching experience, including leading courses each spring at Eigenvector University, the most comprehensive offering of chemometrics classes in the world.

Course Fee, Registration and Venue

The course fee is $125 for industrial participants, $75 for academics. Fee includes coffee breaks and lunch. Registration can be done through your Eigenvector account.

The course will be held at the Hyatt Regency at Two Albany Street, New Brunswick, NJ 08901 (see map), phone (732)873-1234.

Schedule

 

Thursday, December 8 Friday, December 9
8:30 - 10:00 Instruction
10:00 - 10:15 Break
10:15 - 12:00 Instruction
12:00 - 1:00 Lunch (provided)
1:00 - 3:00 Instruction
3:00 - 3:15 Break
3:15 - 4:30 Instruction		 8:30 - 10:00 Instruction
10:00 - 10:15 Break
10:15 - 12:00 Instruction
12:00 - 1:00 Lunch (provided)

Course Outline

1. Introduction
1.1 Basic layout of PLS_Toolbox/Solo
1.2 Browse interface
1.3 Analysis interface
1.4 Plot control interface

2. Editing and selecting data
2.1 The DataSet Editor
2.2 Selecting data graphically
2.3 Highlighting samples and variables
2.4 Setting and displaying classes

3. An example with Principal Components Analysis
3.1 Loading data
3.2 Setting preprocessing
3.3 Selecting number of components
3.4 Viewing model parameters: scores, loadings, variance captured

4. An example with Partial Least Squares Regression
4.1 Loading calibration and test data
4.2 Selecting variable ranges
4.3 Setting preprocessing
4.4 Setting cross-validation
4.5 Selecting number of components
4.6 Viewing model parameters
4.7 Options for implementing models on-line

5. Non-linear Methods
5.1 Locally Weighted Regression
5.2 Augmenting data with non-linear terms
5.3 Support Vector Machines

6. Using the Robust Methods
6.1 Robust PCA
6.2 Robust PLS

7. Advanced Preprocessing
7.1 Preprocessing interface
7.2 Selecting and ordering steps, viewing preprocessed data
7.3 Filtering and orthogonalization methods: GLS, EPO, defining clutter
7.4 Looping

8. Orthogonalizing a PLS model--fauxPLS
8.1 Initial model development
8.2 Viewing orthogonalized model

9. Curve Resolution Approaches
9.1 Setting constraints in Multivariate Curve Resolution
9.2 The Purity approach

10. Calibration Transfer Methods
10.1 Piece-wise Direct Standardization
10.2 Generalized Least Squares
10.3 Orthogonal Signal Correction

Optional Second Half Day

11. Multivariate Image Analysis
11.1 Importing Image Data
11.2 PCA and MAF on Images
11.3 Using the Imgage Manager Tool
11.4 Particle Counting and Analysis

12. Design of Experiments
12.1 The Experiment Designer Interface
12.2 Reading Experiment Files
12.3 Types of Experimental Designs

13. One-on-One Consulting Time