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Chemometrics without Equations (or Hardly Any)

June 23, 2020 - June 26, 2020

Eigenvector Research, Inc. is pleased to bring you Chemometrics without Equations (or Hardly Any), an online instructor-led live short course. This easily accessible introductory course covers the basics of chemometrics: the application of modern multivariate methods to chemical data. In this hands-on course you will be introduced to the most commonly used chemometric methods and learn how to interpret and apply them for best results.

Complete information about the course can be found by following the links below.

Target Audience

Chemometrics without Equations was developed for chemists and bio-scientists who need to analyze multivariate chemical data and for those who manage these staff members. It covers the basics of pattern recognition, quantitative (regression) and qualitative (classification) models. Additional sections discuss how to improve models through advanced data preprocessing and variable selection. The course is relevant in a large number of analytical applications including

  • Process Analysis, e.g. pharmaceutical, food & beverage, and chemical process industries.
  • Analytical Chemistry, e.g. QC labs, forensic investigations, etc.
  • Sensory and Consumer Science
  • Medical Devices
  • Metabolomics, genomics, etc.

Course Description

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 development 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.

Chemometrics without Equations starts by introducing one of the most important methods in data science, Principal Components Analysis (PCA). PCA is used in a myriad of applications for exploratory data analysis/pattern recognition. The course continues with regression methods including Classical Least Squares (CLS) and the now ubiquitous Partial Least Squares regression (PLS). It is then shown how these methods are adapted for sample classification in SIMCA and PLS Discriminant Analysis (PLS-DA). The course concludes with sections on how models can be improved with advanced preprocessing methods and variable selection.
This course will be delivered via WebEx webinar in three segments of three and a half hours each. The course material is based on our popular Chemometrics without Equations and Advanced Chemometrics without Equations courses that have been popular at many conferences including EAS and SCIX.

The course will include many follow-along examples and several homework problems. In order to take advantage of these, participants should equip their computers with current versions of our MATLAB based software PLS_Toolbox or our stand-alone Solo software (available for Windows, MacOS and Linux). Demo copies will work just fine for the course. Users 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 President and PLS_Toolbox creator Barry M. Wise. He will be assisted by members of the Eigenvector scientific and software development team. Dr. Wise and the Eigenvector staff have delivered over 200 chemometrics courses at scientific conferences, on-site for companies and at our popular Eigenvector University each year in Seattle.

Course Fee

Eigenvector Research is pleased to offer this course at a special reduced “COVID-19 Lockdown” price. Prices include instruction, course materials (provided in advance in .pdf format) and a certificate of completion.

Prices shown are shown below. Payment must be received by 5pm PDT, Monday, June 22, 2020.

Chemometrics without Equations

Note: Payment must be received by 5pm PDT, Monday, June 22, 2020. Credit card orders are strongly encouraged. Acceptable forms of payment include MasterCard, VISA, American Express, and checks drawn on a US bank. Wire transfers can also be arranged.

Academic discount: University students and faculty are eligible for the academic rate. Verification of University affiliation is required by providing valid university mailing and e-mail address. Note that we define academic as “degree granting institution.”

How to Register, Deadlines and Cancellations

To register, login to your Eigenvector account, or create an account, then select the class you would like to attend. You can pay directly with your MasterCard, VISA or American Express using our secure credit card processing. You may ask to be invoiced, however, Payments must be received by 5pm PDT, Monday, June 22, 2020.

Complete refunds will be made for cancellations prior to June 20, 2020. No refunds will be made for cancellations after that date, however, substitutions are gladly accepted.


Chemometrics without Equations will be taught over 4 days, Tuesday through Friday, June 23-26, from 7:00-9:45am PDT (that’s 10:00am to 12:45pm EST and 16:00-18:45 CET) each day. The daily schedule is as follows.

Daily Schedule, Pacific Daylight Time (PDT)
06:45 – 07:00 WebEx available for login
07:00 – 07:50 Instruction
07:50 – 08:00 Coffee Break
08:00 – 08:50 Instruction
08:50 – 09:00 Coffee Break
09:00 – 09:30 Instruction
09:30 – 09:45 Wrap-up and questions

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. Classification
5.1 what is classification?
5.2 classification based on PCA models: SIMCA
5.3 using regression for classification: PLS Discriminant Analysis (PLS-DA)

6 Advanced Preprocessing
6.1 what are the goals of preprocessing?
6.2 mean- and median-centering, autoscaling
6.3 normalization and standard normal variate
6.4 Savitsky-Golay and filtering
6.5 generalized least squares weighting (GLS)
6.6 multiplicative scatter correction (MSC)
6.7 extended multiplicative scatter correction (EMSC)

7. Variable selection
7.1 why do variable selection?
7.2 knowledge based selection
7.3 model based, e.g. on loadings
7.4 interval PLS (iPLS)

8. Conclusions