COURSE
Chemometry
NUMBER OF CLASSES
20
INDICATIVE CONTENT OF THE COURSE / MODULE
Selection of variables. Main components. Random forest. Experimental design. Two-level design. Fractional design. Multi-level design. Mixture design. Modeling. Multiple and polynomial regression. Nonlinear regression. The steepest descent methodology and the Marquard methodology. Artificial intelligence. Artificial neural networks. Advanced, reversible and self-organizing topologies. Methodologies with and without an external teacher. Fuzzy logic. Theory of conventional and fuzzy sets. Cluster analysis. Pattern recognition. Optimization methods. Simplex. Decision making based on multiple criteria. Derringer function. Pareto optimality. Global search strategies. Genetic algorithms. Simulated hardening. Ant colonies. Relationships for quantifying structural properties. Hybrid systems. The main components - artificial neural networks. Genetic algorithms - artificial neural networks. Neuronexpressed systems. Signal processing. Fourier transforms. Leveling and filtering. Signal amplification. Deconvolution. Multivariate and nonlinear calibration. Internal and external validation. Measurement uncertainty.
DESCRIPTION OF TEACHING METHODS
Lectures, seminars, consultations.
DESCRIPTION OF THE MANNER OF PERFORMANCE OF OBLIGATIONS
Exam, seminar presentation.
LEARNING OUTCOMES AT THE LEVEL OF THE COURSE:
1. To design measurement or experiment by applying mathematical and statistical methods.
2. To extract maximal amount of useful information from an analytical system with a limited number of data by applying mathematical and statistical methods.
3. To select and apply tools of artificial intelligence in modelling and optimisation of chemical and related systems.
4. To predict the properties of molecules by the calculations based on the molecular structure.
5. To synthesize the obtained useful information into new concepts.
LEARNING OUTCOMES AT THE LEVEL OF THE STUDY PROGRAM:
1. To systematise knowledge, skills and competences for the respective field and academic area of the programme of study
2. To evaluate the skills and methods for experimental and theoretical research relating to the respective field and academic area of the programme of study
3. To design a real research process, including all the respective professional and scholarly aspects
LITERATURE
1. Paul Gemeprline, Practical Guide to Chemometrics, 2nd ed. CRC Press, Taylor & FrancisG roup, 2006, Boca Raton, USA, 2006.
2. Richard G. Brereton: Chemometrics Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons Ltd, West Sussex, UK, 2003.
3. Peter C. Meier,Richard E. Zund, Statistical Methods in Analytical Chemistry, 2nd ed. John Wiley & Sons Ltd, New York, USA, 2000.
4. Ivan Šošić, Primijenjena statistika, Školska knjiga, Zagreb, Croatia, 2004.
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