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Multivariate Analysis of Fermentation Data

Multivariate Analysis of Fermentation Data (3 days)
Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions. 

Course objective:
The objective of this course is to teach the attendees how to convert data collected on fermentation processes from data tables to interpretable figures via multivariate models.



Selected course contents:
• Organization, vizualisation and treatment of different types of data
• Multivariate data modelling and diagnostics
• Modeling of Batch evolution and batch-to-batch variation
• Model interpretation
• Possibilities for on-line process monitoring

Who should participate?
The course is intended for researchers, scientists and engineers working in upstream fermentation development and production who are interested in gaining maximum information from their data.



Course schedule
Lunch daily between 12:00 - 13:00
Day one
9:00 Focus on objectives and values of models.
After the first day the attendee should know what batch modeling is, potentials and values.
» Values, Objectives and Possibilities for Multivariate Batch modeling and monitoring
• QbD in Fermentation
» Problem formulation
• Data sources, expression and use of data- what is data and what is a model?
» Intro to Multivariate data analysis, PCA and PLS
• Multivariate batch modeling
• Properties of batch data
» Demo and Exercises
17:00 End of lectures and exercises.
Day two
9:00 Modeling and Focus on data sources and values.
After the second day the attendees should know which data sources to use and how, how to build a multivariate
model and how to interpret it.
» Model diagnostics and validation
» Use of models for prediction
» Multi-step/ phase processes
» Demo and Exercises
• Participants are encouraged to bring their own data
17:00 End of lectures and exercises.
Day three
9:00 How to critically work with historical data and possibilities to use spectroscopic
data.
After the third day the attendee should know how to model data from their own processes, how to plan experiments and select representative data from historical data bases.
» Combining multiple data sources
» DoE Intro
• DoE in lab scale fermentor- development
• DoE for design space
» Efficient use of historical data
• Selection techniques, selection of batches for modeling of historical data
15:00 End of course.

Cost and conditions
Early bird registration: if you book at least 2 months prior to course date the price DISCOUNT is 17%. Also if you are more than three participants from one company booking the same course date you will receive an 8% DISCOUNT/person.
Course fee (+VAT) includes coffee, lunch and course documentation. An invoice will be sent and payment is required within 30 days of the invoice date. Course application is binding. Cancellations registered later than two weeks before the course start will not be refunded. Provided that Umetrics AB is notified, the registering company may substitute its participant(s).
If the customer cancels prior to 14 days of the course, 10% of the class fee will be applied to cover processing costs. The balance of class fees already paid to Umetrics may be credited towards a future course. The registering company may substitute its participant(s) provided that Umetrics is notified.
Umetrics holds courses based on a sufficient number of registrants. Therefore, Umetrics reserves the right to cancel the course 14 days prior to the course start date if the number of registrants is too low. Full refund will be made to these registrants. Alternately a 10% refund will be made to any registrant(s) enrolling in the next available course.

To register, please use the button "Begin registration" in this window frame, or send an email to Umetrics Academy.

COURSE INFORMATION

Course length: 3 days
Location: Switzerland, Basel
Start date: 2010-11-30
Price: 1200 EUR
Contact: umetrics.academy@umetrics.com

Please visit www.umetrics.com/training for more information and registration.

No prior knowledge of statistics is required, knowledge about in-house data base structure is beneficial.