Pattern Recognition for Natural Science
Course infoSchedule
Course moduleNWI-SM299
Credits (ECTS)3
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Moleculaire Wetenschappen;
PreviousNext 3
dr. C.G. Bertinetto
Other course modules lecturer
A.J. Carnoli, MSc
Other course modules lecturer
dr. S.M. Cristescu
Other course modules lecturer
dr. J.J. Jansen
Other course modules lecturer
dr. J.J. Jansen
Other course modules lecturer
Academic year2022
KW2  (07/11/2022 to 29/01/2023)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
After following this course, the student will be able to:
  • Apply several advanced chemometric data analysis techniques and principles to analytical chemical data,
  • Design a comprehensive data analysis strategy consistent with the conducted experiment, that creates value for insight and prediction based on the experiment
  • Translate the results of this strategy into experimentally-relevant information,
  • Communicate these results orally and written to an audience of peer scientists/students
The complexity of chemical data in scientific experiments is rising exponentially. For example, data from the 'omics' technologies in systems biology provide the researcher with an unsurpassed view on all genes, proteins and metabolites in a living organism. Also in (bio)reaction process modelling and optimization, the number of parameters to be optimized can increase drastically and requires specialized optimization techniques and techniques to interpret the results. The relevant information cannot be extracted by eye or 'simple' data analysis, but requires advanced, multivariate data analysis methods. Even the standard multidimensional chemometric data analysis techniques can be insufficient.

This course focuses on the diversity of more advanced multivariate data analysis techniques and their relevance for research questions in the natural sciences. The course is a follow-up of the bachelor "Chemometrics/Chemometrics for Molecular Life Sciences" course or the "Omics" course from the "Chemistry for Life" Master Track, in which only the basic techniques are studied. The underlying theories will be taught and practiced and time will be devoted on a proper set up of the data analysis strategy and interpretation of the results.
During the course (real life) examples will be provided. In addition, each student selects a case study for which they have to develop a data analysis strategy and execute part of this strategy using routines supplied during the course.
We encourage students from all fields of Natural Sciences to participate, as the principles raised in the course apply to all branches of quantitative research. Either the course Omics or Chemometrics (for Molecular Life Sciences) is required to follow this course.

Instructional Modes
Lectures and computer courses

Presumed foreknowledge
  • Introductory statistics
  • Basic linear algebra
  • In the first lecture, we will give a primer on the principles of analytical chemical data, which will be essential to generate chemically valuable models in the remainder of the course 
  • Omics (NWI-MOL410) or Chemometrics (NWI-MOL065). This implies that the students are not expected to be experienced at programming. All topics will be covered in MATLAB, where we will provide the students with easy-to-use toolboxes or runtime scripts to perform the assignments.
Test information
On a selected case study the students perform an appropriate data analysis. They present this in intermediate (non-evaluated) and semi-finalized form (evaluated). They produce a written report that details the research questions, the obtained data and the developed data analysis strategy and results in a finalized form. The course is evaluated as ¼ a grade for the presentation and ¾ a grade for the report. The students have to be responsible for half the work each and have to explicitly specify which part: students are separately evaluated for these parts.
This course is compulsory for Chemistry students doing a major master internship at the department of Analytical Chemistry.
Required materials
Lecture slides
Research and tutorial papers, divided into primary and secondary material
Course guide
MATLAB programmes and tutorials for all methods and principles covered in the course.

Instructional modes
Attendance MandatoryYes

Each 2h lecture describes one or two topics. These topics may be either methods or principles from good chemometric practice.

14 h lectures

Attendance MandatoryYes

14 h Project
4 h Response lecture

Attendance MandatoryYes

Each lecture is followed by 4 hours problem session, that covers the topics from the lectures.

14 h

Test weight1
Test typePresentation
OpportunitiesBlock KW2, Block KW3

Test weight3
Test typeAssignment
OpportunitiesBlock KW2, Block KW3