OVERVIEW

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

AIMS AND CONTENT

LEARNING OUTCOMES

The educational objective of the course is to equip students with the multivariate analysis tools needed for quality control and process monitoring. Specifically, students will learn to use exploratory and classification methods such as Principal Component Analysis (PCA) and Discriminant Analysis (DA) for quality control, especially in the food sector, but also in other areas. Various applications of chemometric methods to industrial process monitoring will also be presented.

By the end of the course, students will be able to identify the most appropriate chemometric strategy depending on the specific problem, whether it involves authentication, characterization, or process monitoring

AIMS AND LEARNING OUTCOMES

The learning objective of the course is to provide students with the multivariate analysis tools necessary for quality control and process monitoring. Specifically, students will learn to use multivariate exploratory and classification methods such as Principal Component Analysis (PCA) and Discriminant Analysis (DA) for quality control, particularly for food products, but not limited to this. Additionally, various applications of chemometric methods for process monitoring in industrial settings will be demonstrated. By the end of the course, students will be able to understand which chemometric strategy to use depending on the problem presented, whether it involves authentication, characterization, or process monitoring.

PREREQUISITES

For further information not included in the course syllabus, please contact the teacher.

TEACHING METHODS

Theoretical lessons are complemented by practical sessions, consisting of computer-based exercises using chemometric software for data processing.

SYLLABUS/CONTENT

This course introduces various multivariate analysis techniques and demonstrates their role in detecting adulteration, ensuring product authenticity, quality control, and process monitoring. Product authenticity determination is one of the most critical issues in quality and safety control. PCA, DA, and other commonly used techniques will be introduced in this context.

Furthermore, the course will describe the chemometric approach to monitoring chemical processes and detecting issues. These approaches are based on developing mathematical/statistical models from historical process data; new process data can then be compared with ‘normal’ operation models to detect system changes. Typical modeling techniques include Principal Component Analysis (PCA), Partial Least Squares (PLS), and a variety of other chemometric methods.

RECOMMENDED READING/BIBLIOGRAPHY

Specific indications on reference bibliography will be provided by the professor at the beginning of the lectures

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Second semester

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam is conducted on a computer. Students are provided with a data set and must extract useful information to address the problem using various multivariate analysis techniques (PCA, LDA, PLS, etc.) implemented in the statistical software used during the lessons.

ASSESSMENT METHODS

Learning is assessed through various ongoing exercises and a final assessment evaluated by the Committee.

FURTHER INFORMATION

For further information not included in the course syllabus, please contact the teacher.