Chemometrics and Statistics

Course manual 2025/2026

Course content

In this course general aspects of chemometrics and statistics applied for analytical methods will be treated. Parameters to describe the quality of analytical methods (e.g. accuracy and precision, sensitivity, selectivity, robustness) will be defined. Basic statistical methods applied to modern analytical instrumentation will be discussed. These include data exploration and visualization, statistical inference, hypothesis testing, and calibration, applied to univariate and multivariate data.

In addition, an important component of the course is signal processing and the student will learn how to find and process useful information from signals obtained by instrumental analytical techniques.

Attention will also be given to design-of-experiments and validation procedures. One of the main objectives of the course is to acquire the skills for adequate software handling for data analysis using a higher programming language.

Study materials

Literature

• B.W.J. Pirok & P.J. Schoenmakers, Analytical Separation Sciences, 2025, Royal Society of Chemistry, Chapter 9: Data Analysis

Practical training material

• Exercises will be supplied through Canvas

Software

• MATLAB

• GPower

Objectives

• Classify statistical analysis methods.
• Propose suitable methods to data processing.
• Set-up an experimental design and interpret the experimental results.
• Evaluate whether the applied statistical method led to a useful answer to the analytical question.
• Examine the quality of analytical methods (e.g. accuracy and precision, sensitivity, selectivity, robustness).
• Find the main characteristics of signals obtained by instrumental analytical techniques.
• Write scripts to perform statistical computations.
• Defend the implication of the choice of statistical method.

Teaching methods

• Lecture
• Seminar
• Laptop seminar
• Computer lab session/practical training
• Self-study

The lectures deliver new theoretical concepts. Students will be able to apply and practice these concepts in the tutorials using the special exercises. The exercises are intended for self-study. Numerical answers will be provided on Canvas to help students evaluating their progress. In special computer lab sessions, students will acquire further help in developing limited programming skills to apply all theoretical concepts to real data that they can encounter in their career.

Learning activities

Attendance

This programme does not have requirements concerning attendance (TER part B).

Additional requirements for this course:

Attendance is not mandatory to the lectures and tutorials. However, the course is designed with the assumption that students participate. It is thus strongly encouraged to participate.

Assessment

Inspection of assessed work

Students may make an appointment to inspect their exam within one week of the publication of the grades for that exam.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• dr. B.W.J. Pirok