Learning objectives
Students will be able to deal with different challenges related to planning of experiments, classification and discrimination of samples
Prerequisites
Good knowledge of univariate chemometrics techniques is requested
Course unit content
The course will provide student with knowledge of theoretical principles and applications to food analysis of multivariate chemometrics techniques, as bivariate ANOVA, principal component analysis, cluster analysis, discriminant analysis, and experimental design techniques
Full programme
Multivariate chemometrics techniques: bivariate ANOVA, principal component analysis, cluster analysis, discriminant analysis.
Experimental design techniques: factorial design, response surface design. Optimization. Modelling.
Bibliography
- J.N. Miller, J.C. Miller “Statistics and chemometrics for analytical chemistry” Pearson Prentice Hall
- D.L. Massart et al. “Handbook of chemometrics and qualimetrics” Part A Elsevier
- S.R. Chrouch, F.J. Holler “Applications of Microsoft Excel in analytical chemistry” 2 ed Belmont C.A. Brooks/Cole
Teaching methods
Theoretical lectures and exercises with the software Excel and SPSS will be performed in the classroom.
Assessment methods and criteria
Oral examination about topics discussed during the course
Other information
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2030 agenda goals for sustainable development
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