PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

Elenco notifiche



Principles of chemometrics: a multivariate approach for the analysis of experimental data

01UIMIY

A.A. 2019/20

Course Language

Inglese

Degree programme(s)

Doctorate Research in Ingegneria Chimica - Torino

Course structure
Teaching Hours
Lezioni 15
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Savorani Francesco   Professore Associato CHEM-06/A 10 0 0 0 6
Co-lectures
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2019/20
PERIOD: SEPTEMBER The course presents Chemometrics as a set of tools for experimental data acquisition, preprocessing, exploration and analysis aimed at optimizing and maximizing the extraction of valuable physico-chemical information through a multivariate approach. The course consists of both frontal lessons, during which the chemometric techniques will be introduced, and practical sessions in which the students will be challenged on case studies proposed either by the lecturers or by the students themselves.
PERIOD: SEPTEMBER The course presents Chemometrics as a set of tools for experimental data acquisition, preprocessing, exploration and analysis aimed at optimizing and maximizing the extraction of valuable physico-chemical information through a multivariate approach. The course consists of both frontal lessons, during which the chemometric techniques will be introduced, and practical sessions in which the students will be challenged on case studies proposed either by the lecturers or by the students themselves.
The course will consist of a theoretical part with frontal lessons and a practical part strongly focused on problem solving, during which the students will be challenged on a dataset from which information has to be extracted using the chemometric techniques explained in the course. Introduction: What is and what is for Chemometrics?! Design of experiment (DoE): why are experiments performed, how to plan the experiments to optimize the amount of information that can be extracted, the experimental domain, why changing “one variable at the time” not always is the best approach, experimental designs and response surfaces, examples of DoE techniques. Data inspection and preprocessing: visual inspection, data pre-processing and pre-treatment. Exploratory analysis: Principal Component Analysis (PCA), clustering analysis, residuals inspection, outlier detection, rank of a dataset, overfit and underfit, the importance of metadata. Regression and classification: multivariate regression methods (PLS, Partial Least Squares), multivariate classification methods (PLS-DA, Partial Least Squares-Discriminant Analysis). Case study: application of the proposed chemometric techniques to a real-life dataset proposed either by the lecturers or the students themselves.
The course will consist of a theoretical part with frontal lessons and a practical part strongly focused on problem solving, during which the students will be challenged on a dataset from which information has to be extracted using the chemometric techniques explained in the course. Introduction: What is and what is for Chemometrics?! Design of experiment (DoE): why are experiments performed, how to plan the experiments to optimize the amount of information that can be extracted, the experimental domain, why changing “one variable at the time” not always is the best approach, experimental designs and response surfaces, examples of DoE techniques. Data inspection and preprocessing: visual inspection, data pre-processing and pre-treatment. Exploratory analysis: Principal Component Analysis (PCA), clustering analysis, residuals inspection, outlier detection, rank of a dataset, overfit and underfit, the importance of metadata. Regression and classification: multivariate regression methods (PLS, Partial Least Squares), multivariate classification methods (PLS-DA, Partial Least Squares-Discriminant Analysis). Case study: application of the proposed chemometric techniques to a real-life dataset proposed either by the lecturers or the students themselves.
Modalità di esame:
Exam:
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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam:
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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