The course aims to present the main statistical techniques of univariate and multivariate analysis of chemical data. The goal is to provide the basis for using these techniques in the following contexts: experimental design, exploratory data analysis, construction of predictive models, management of process data. The course is structured in theoretical lessons and practical computer exercises with compulsory attendance. At the end of the course, the student will be able to design laboratory (or process) experiments following the logic of Experimental Design and to analyze the results. It will also be able to analyze a set of data from a qualitative point of view through the most common visualization techniques, and to build multivariate linear predictive models.
Course Prerequisites
Knowledge of general chemistry and analytical chemistry, and in particular of the main instrumental analysis techniques.
Teaching Methods
48 hours of lectures and computer exercises. The lectures will be supported by electronic material that will be provided to the student and by articles of literature that will also be made available to the student. The computer exercises will take place individually and with the use of open-source software, therefore the student can also practice at home with his personal PC (and then use that software in future projects such as the thesis). They will focus on the practical use of multivariate modeling: data sets of real examples will be provided on which the student can exercise the skills acquired during the theoretical lessons.
Assessment Methods
The written exam will be divided into two parts: a theoretical part, with a maximum grade of 15/30, and a practical part on the computer in which the student will have to independently develop a chemometric model, with a maximum grade of 15/30. The exam will be considered sufficient if both parties have reached a minimum of 8/15. The overall mark of the exam will then be calculated as the sum of the marks obtained in the two parts. If the student wants to improve the grade of the written test (this applies only to students who have reached the sufficiency in the written), he can present himself for an oral exam which consists of two short questions with a maximum overall score of 5/30. The score obtained in the oral exam will be added to that obtained in the written exam to obtain the final grade.
Contents
Introduction to chemometrics: the concept of multivariate data (4 hours) Data collection and data preparation (4 hours) Univariate and multivariate exploratory data analysis (4 hours) Projection methods (8 hours) Cluster analysis techniques (4 hours) Classification techniques (4 hours) Regression methods (8 hours) Basics of Spectroscopic Data Processing (4 hours) Introduction to experimental design: main screening and optimization designs (8 hours)
Course Language
Italian
More information
The teacher receives by appointment in his office or via the Teams platform.
