麻豆社

Course Topics
Part A) Recall of basic statistical elements; introduction to and application of exploratory multivariate statistics (e.g., PCA, HCA, FA) in food quality data analysis; introduction to other exploratory models (MFA, MCA); Part B) Supervised multivariate classification and discrimination models (eg. SIMCA; LDA); Part C) Multiple and multivariate regression models (MLR, PCR, PLS), and derived methods (PLS-DA). Practical activities: Training and use of software tools for multivariate statistical analysis for applications to data from real food analytical and sensory studies; team working on real datasets; preparation of a presentation.

Propaedeutic courses
no

Teaching format
Frontal lectures, practical exercises

Educational objectives
Knowledge and understanding. (a) adequate knowledge and understanding of the application of appropriate statistical tools for studying the chemical and sensory markers of food products quality and authenticity; (b) autonomous ability of devising and applying statistical strategies to investigate the chemical and sensory markers of quality and authenticity of food products; Applying knowledge and understanding. (a) developing the capability of integrating information, both in horizontal way (technological, chemical, biological, and regulatory aspects related to the chemical and sensory analysis of food products) and in vertical way (reasonable sequence of processes affecting the composition of food products for the evaluation of their quality and authenticity); Making judgements. Capability of identify critical parameters in order to improve the efficiency of the chemical and sensory analysis of food products; Communication skills. Capability of clearly and exhaustively communicate notions, ideas, problems and technical solutions by interpreting and integrating the results of chemical and sensory analyses of food products to interlocutors, either professional or not, representative of the various and specific competencies in the food supply chain (agronomist, engineers, biologists, chemists, nutritionists, administrators) Learning skills. Ability to achieve independence in the evaluation of statistical tools for the analysis of chemical and sensory data from food products, with an appropriate level of autonomy in the interpretation of results.

Additional educational objectives and learning outcomes
Knowledge and understanding. (a) adequate knowledge and understanding of the application of appropriate statistical tools for studying the chemical and sensory markers of food products quality and authenticity; (b) autonomous ability of devising and applying statistical strategies to investigate the chemical and sensory markers of quality and authenticity of food products; Applying knowledge and understanding. (a) developing the capability of integrating information, both in horizontal way (technological, chemical, biological, and regulatory aspects related to the chemical and sensory analysis of food products) and in vertical way (reasonable sequence of processes affecting the composition of food products for the evaluation of their quality and authenticity); Making judgements. Capability of identify critical parameters in order to improve the efficiency of the chemical and sensory analysis of food products; Communication skills. Capability of clearly and exhaustively communicate notions, ideas, problems and technical solutions by interpreting and integrating the results of chemical and sensory analyses of food products to interlocutors, either professional or not, representative of the various and specific competencies in the food supply chain (agronomist, engineers, biologists, chemists, nutritionists, administrators) Learning skills. Ability to achieve independence in the evaluation of statistical tools for the analysis of chemical and sensory data from food products, with an appropriate level of autonomy in the interpretation of results.

Assessment
At the end of the course the students, divided in groups (2 to 4 persons max.), will give the class presentations of a case stud of their choice, highlighting their understanding of theoretical and practical aspects seen during the lectures.

Evaluation criteria
n/a

Required readings

Keynotes and scientific publications provided by the lecturer. Some material is also available on Teams (course page) upon enrollment.

Supplementary readings

鈥淐hemometrics in Food Chemistry鈥� (ed. Federico Marini); 鈥淐hemometrics with R鈥� (ed. Springer); 鈥淚ntroduction to Multivariate Statistical Analysis in Chemometrics鈥� (ed. CRC Press); other specific learning material proposed during the lectures

Further information
Participants will work in groups during the lectures hours on a project (application of the seen methodologies on a dataset of choice), which will be presented the last day of the course. The presentation should be organized as if delivered to a scientific conference. The lecturer can provide data for testing and exercise. Further information will be provided at the beginning of the lectures.