CODE 42396 ACADEMIC YEAR 2022/2023 CREDITS 2 cfu anno 4 CHIMICA E TECNOLOGIA FARMACEUTICHE 8451 (LM-13) - GENOVA 2 cfu anno 3 CHIMICA E TECNOLOGIA FARMACEUTICHE 8451 (LM-13) - GENOVA 2 cfu anno 4 FARMACIA 8452 (LM-13) - GENOVA 2 cfu anno 5 FARMACIA 8452 (LM-13) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR CHIM/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester MODULES Questo insegnamento è un modulo di: OPTIONAL SUBJECTS TEACHING MATERIALS AULAWEB OVERVIEW The course covers the basic techniques of multivariate analysis applied to the extraction of information from complex chemical data, including: exploratory analysis (PCA: Principal Component Analysis) to visualize the structure of multivariate data; classification and modelling methods to identify a sample as belonging to a previously defined sample group; regression methods to determine the amount of a component, a property or other value from the X block of the measured variables. AIMS AND CONTENT LEARNING OUTCOMES Provide students with the necessary tools to process complex chemical data and to extract useful information from them. AIMS AND LEARNING OUTCOMES Students will acquire the knowledge necessary to process complex chemical data. In particular, they will be able to develop predictive classification or regression models using the multivariate analysis software (CAT). TEACHING METHODS The lessons will be partly theoretical and partly practical. During the theoretical lessons the main methods of multivariate analysis (PCA, LDA, PLS, etc.) will be presented. The practical lessons will take place on the computer and students will learn how to use chemometric software (CAT) to process chemical data. SYLLABUS/CONTENT Exploratory Analysis (PCA) to visualize the structure of multivariate data; classification and modelling methods to identify a sample as belonging to a previously defined sample group (LDA: Linear Discriminant Analysis); regression methods to determine the amount of a component, property, or other value from the X block of measured variables (PLS: Partial Least Square regression). Software for multivariate analysis: CAT. TEACHERS AND EXAM BOARD MONICA CASALE Ricevimento: Appointment by email: monica.casale@unige.it. CRISTINA MALEGORI Ricevimento: Reception: At the Chemistry and Pharmaceutical and Food Technologies Section of the Department of Pharmacy - DIFAR (Viale Cembrano, 4) or online via MS-Teams, by appointment with the teacher, to be agreed via e-mail (cristina.malegori @ unige.it) Exam Board BRUNO TASSO (President) ELEONORA RUSSO LESSONS LESSONS START Second semester. Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Students will be provided with a set of chemical data that they will have to process using the appropriate multivariate analysis tools. They will then prepare a power point presentation with the main results obtained to be presented to the Commission.
