Quality control of the analytical process

The course will cover theoretical, practical, and investigative aspects of data analysis for chemical analysis.

This course covers data visualization and characterization of collected data, basic statistics for hypothesis testing, quantification and method validation. The course also covers material from the fundamentals of statistical tests to application in analytical method validation and modelling. The gained knowledge is applied throughout the course in mini projects.

Additionally, the course content serves as bases for data analysis in the following courses in the Master Program in Analytical Chemistry.
 

The course features lectures, workshops, computer lab work, oral and written presentation of scientific work within the following subjects:

Data presentation, characterization, distributions

Different statistics for average and spread, plotting data (scatter plot, histogram, box-plot, etc.), distributions (uniform distribution, normal distribution, t-distribution, etc.), central limit theorem.

Hypothesis testing 

Type I and II error, p-value, t-test, and F-test, analysis of variance.
Regression analysis for quantification and modeling: residuals, significance testing, slope and interpretation, calculating the concentration, lack-of-fit, goodness-of-fit, multilinear regression, robust regression.


Method validation

Precision, trueness, accuracy, uncertainty, different validation guidelines,  quality control, and accreditation. 


Teaching Format

The teaching consists of lectures, seminars, exercises and laboratories.

After the course, the student is expected to be able to

  • Define the hypothesis for testing statistical significance depending on the scientific question. Apply the statistical significance test and interpret the results. Evaluate the suitability of different statistical tests for given data and suggest a hypothesis testing method depending on the research question.
  • Explain the assumptions, limitations, and advantages of the calibration graph method, standard addition method, and internal standard method. Calculate the analysis result for different quantification methods. Evaluate the linear range of a calibration graph and evaluate the statistical significance of a regression line (residual analysis, Lack-of-Fit, significance of the intercept, etc.). Design a suitable calibration method depending on the instrumental method, available materials, and requirements for the analysis. Assess the suitability of the calibration approach for a given quantification approach. 
  • Name method performance characteristics. Calculate the method characteristics and evaluate if they fulfill the method requirements. Evaluate the need for estimating different method performance characteristics depending on the research question. Design a plan for method validation depending on the method under development. 

Assessment

Written lab reports and a written exam.

Examiner

Anneli Kruve
anneli.kruve@su.se

The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Note that the course literature can be changed up to two months before the start of the course.


Course reports are displayed for the three most recent course instances.








Course Coordinator
Anneli Kruve, email: anneli.kruve@su.se

Chemistry Section & Student Affairs Office: chemistry@su.se