NWI-MOL065
Chemometrics
Course infoSchedule
Course moduleNWI-MOL065
Credits (ECTS)6
CategoryBA (Bachelor)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Moleculaire Wetenschappen;
Lecturer(s)
Coordinator
dr. J.J. Jansen
Other course modules lecturer
Examiner
dr. J.J. Jansen
Other course modules lecturer
Contactperson for the course
dr. J.J. Jansen
Other course modules lecturer
Lecturer
dr. J.J. Jansen
Other course modules lecturer
Lecturer
dr. G.J. Postma
Other course modules lecturer
Academic year2019
Period
KW3  (03/02/2020 to 12/04/2020)
Starting block
KW3
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
The Chemometrics course teaches the student to program, apply and interpret results from the most commonly used modern chemical data analysis techniques. These data visualization and analysis techniques are crucial in contemporary analytical chemistry, biology and medicine. 
At the end of the course the student:
  • ...can select the proper data analysis method, based on the chemical/biochemical question and the principles behind each method.
  • ...can write MATLAB programs to executte these methods on several real life data sets of different size and complexity.
  • ...can correctly interpret and validate the method results,  to translate them to answers to the chemical/biochemical question.
The chemometric techniques that the student will master are listed under Subjects.
Content
During the statistics course, the main focus was on univariate chemical and biological data analysis. However, most measurements on chemical and biological systems are multivariate. Examples are (N)IR or NMR spectra, mass spectra and chromatographic data. Analysis of such data requires multivairate methods, which provide better predictions and more insight into the chemical system; better calibration curves, improved disease predictions, earlier detection of process errors etc. This course teaches you to program and interpret the main tools for multivariate data visualization and analysis.
 
The course gives an overview of the basic chemometric methods, sometimes described as 'Chemical Data Science'. Examples from all fields of the natural sciences will make this course useful for students with very diverse backgrounds. A short and non-exhaustive list of areas where these techniques are applicable is: organic and industrial chemistry, proteomics and metabolomics (finding biomarkers that are indicative for specific diseases) and the detection of unknown pollutants in drinking water. During the course, the various chemometric methods are studied and exercised thoroughly by programming in MATLAB. The student will also learn how to interpret the resulting models to obtain information for further study.
The techniques that will be covered are :
  • Principal Component Analysis: Exploratory Analysis of multivariate data
  • Hierarchical and K-means Clustering 
  • Linear and Quadratic Discriminant Analysis for Classification
  • Partial Least Squares Regression and Discriminant Analysis 

This course will involve programming in MATLAB; Lectures are shared with Chemometrics for Molecular Life Sciences and practicals are course-specific. 
The course (or Chemometrics for MLS) is obligatory for a master internship at the Department of Analytical Chemistry.
Level

Presumed foreknowledge
  • Statistics (NWI-MOL028)
  • Basic Linear Algebra
  • Basic knowledge of Matlab (Matlab course) This is a course in the theme 'Methods'.
  • Test information
    Four assignments: three in pairs of students, forth individual. All grades must be sufficient and the forth assignment counts for 50% in the final grade.
    Specifics
    A follow-up course is Pattern Recognition in the Natural Sciences (SM299). The course will be given in parallel with the course Chemometrics for MLW (MOL109), that focuses more on interpretation of the model results.
    Additional comments
    A follow-up course is Pattern Recognition in the Natural Sciences (SM299).
    The course will be given in parallel with the course Chemometrics for MLW (MOL109), that focuses more on interpretation of the model results.

    Topics
    • Multivariate analysis, Principal Component Analysis (PCA)
    • Clustering (hierarchical, k-means)
    • Classification (discriminant analysis, nearest-neighbour methods)
    • Multivariate regression (PCR, PLS)
    • Validation strategies.

    Test information
    Four assignments: three in pairs of students, forth individual. All grades must be sufficient and the forth assignment counts for 50% in the final grade.

    Prerequisites
    • Statistics (NWI-MOL028)
    • Basic Linear Algebra
    • Basic knowledge of Matlab (Matlab course)

    This is a course in the theme 'Methods'.

    Required materials
    Reader
    On BrightSpace the students can find a reader, a guide to the computer excercises, and several data sets.

    Recommended materials
    Book
    Massart, Vandeginste et al., Handbook of chemometrics and qualimetrics, parts A and B. These books are available for consultation in the library as well as in the reading room of the Department of Analytical Chemistry.

    Instructional modes
    computer training / individ.project work

    Remark
    16 h computer training
    12 h individual project work
    4 h response lecture

    Lecture

    Zelfstudie

    Tests
    Assignments
    Test weight1
    Test typeAssignment
    OpportunitiesBlock KW3, Block KW4

    Final assignment
    Test weight1
    Test typeAssignment
    OpportunitiesBlock KW3, Block KW4