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.

Any Student with documented Specific Learning Disorders (SLD), or with any special needs, shall reach out to the Lecturer(s) and to the dedicated SLD Representative in the Department ( Prof. Luca Raiteri, Luca.Raiteri@unige.it ) before class begins, in order to liaise and arrange the specific learning methods and ensure proper achievement of the learning aims and outcomes. VERY IMPORTANT: any request for compensatory tools and adaptations in the exam MUST be done within 10 working days before the date of the exam according to the instructions that can be found at https://unige.it/disabilita-dsa/comunicazioni

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

Exam Board

BRUNO TASSO (President)

ELEONORA RUSSO (President Substitute)

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.