OVERVIEW

Quality control and process monitoring are two aspects of the production process that play a fundamental role in companies.
Quality control is the production phase in which it is verified that a product meets the required quality requirements; the monitoring involves the collection of data necessary to evaluate and control the progress of a process.
This course aims to give students the basic skills necessary for quality control and process monitoring using cutting-edge strategies based on multivariate data analysis.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of the course is to provide the students with simple and powerful tool for multivariate quality control and process monitoring.

AIMS AND LEARNING OUTCOMES

The educational objective of the course is to provide students with the multivariate analysis tools necessary for quality control and process monitoring. In detail, students will learn to use exploration and classification methods such as Principal component analysis (PCA) and discriminating analysis (DA) for quality control especially of food products but not only. In addition, different applications of chemometric methods to process monitoring in the industrial field will be shown.

Students at the end of the course will be able to understand which chemometric strategy to use depending on the problem presented, be it authentication, characterization or process monitoring.

TEACHING METHODS

All lessons are composed of a first theoretical part where the theory the of multivariate analysis methods applied to quality control and process monitoring (without going into much detail of mathematical algorithms) is explained and a part of computer exercises. In this second part, a specific problem and the related data set are described to the students, that must try to extract the desired information using a statistical software.

SYLLABUS/CONTENT

In this course, several multivariate techniques will be described showing their role both in adulteration detection, authentication, quality control, and in the process monitoring.

Determination of product authenticity is in fact one of the most crucial issues in quality control and safety: multivariate analysis comprising principal component analysis (PCA), discriminant analysis (DA) and other methods that are effectively employed in quality control, will be introduced.

Moreover, the chemometrics approach to chemical process monitoring and fault detection will be described. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data; new process data can then be compared with models of normal operation in order to detect a change in the system. Typical modelling approaches rely on principal components analysis (PCA), partial least squares  (PLS) and a variety of other chemometric methods. 

TEACHERS AND EXAM BOARD

Exam Board

BRUNO TASSO (President)

ELEONORA RUSSO (President Substitute)

LESSONS

Class schedule

L'orario di tutti gli insegnamenti è consultabile all'indirizzo EasyAcademy.

EXAMS

EXAM DESCRIPTION

The exam takes place at the computer. Students are provided with a data set and they have to extract the information useful for the problem by using different techniques of multivariate analysis (PCA, LDA, PLS ...) implemented on a statistical software used during the lessons.