Chemometric Techniques for Quantitative Analysis
This course is completely software neutral. It is widely applicable to anyone working with multivariate data of any kind. This course is also valuable for those who are responsible for management of projects and teams involving these techniques. While the primary target audience comprises those employing the material in industrial settings, this course is also completely relevant and very beneficial for academic researchers and students. The course is presented in a way which is extremely accessible those who may not feel comfortable, or well versed in mathematics or statistics. It presents the concepts using data visualization rather than equations. Although the use of mathematics is deliberately minimized, the course is completely rigorous. Students introduced to the topics by this course have gone on to succeed with important work in myriad fields.
- Important charateristics of multivariate data
- Proper way to approach, develop, deploy, maintain empirically derived calibrations
- Multiple Linear Regressions – CLS and ILS
- Understanding factor based techniques and the benefits of factor spaces
- Factor based regressions – PCR and PLS
- The essential role of proper validation
This course is designed to get the student quickly “up to speed” with the Multiple Linear Regressions and Factor-Based techniques used to produce quantitative calibrations from instrumental and other data: Classical Least-Squares (CLS), Inverse Least-Squares (ILS), Principle Component Regression (PCR) and Partial Least-Squares in latent variables (PLS). The emphasis will be on using actual and synthetic data to gain a practical understanding of the different techniques and their proper application.
Who Should Attend
This course is intended for chemists, spectroscopists, chromatographers, biologists, programers, technicians, mathematicians, statisticians, managers, engineers, and anyone responsible for developing analytical calibrations using laboratory or on-line instrumentation, managing the development or use of such calibrations and instrumentation, or designing or choosing software for the instrumentation. This introductory course requires no prior exposure to the material. Students who have explored the topics but are not yet comfortable using them will also find this course beneficial. The data-centric approach to the topics does not require any special mathematical background, but a familiarity with matrix multiplication would be helpful.
Learn the principles which are essential to the proper development, deployment, and maintenance of empirically derived chemometric calibrations. Gain an intuitive understanding of factors spaces, together with the advantages and limitations of factor based regression techniques. Understand how to efficiently approach and execute your projects. Learn how to use your software correctly.
- Basic Nomenclature
- Strategy, overall approach, assessing feasibility, gathering data, generating calibrations, validation, deployment, ongoing validation
- Generation of simulated data with particular characteristics and artifacts
- Classical Least Squares
- Inverse Least Squares
- Understanding Factors Spaces
- Principal Component Regression
- Partial Least Squares
About the Instructor
is President of Applied Chemometrics, Inc., a comprehensive supplier of chemometrics consulting, software, training, and support.