97472 - Chemometrics for Cultural Heritage

Course Unit Page

  • Teacher Giorgia Sciutto

  • Credits 6

  • SSD CHIM/12

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Ravenna

  • Degree Programme Second cycle degree programme (LM) in Science for the Conservation-Restoration of Cultural Heritage (cod. 8537)

  • Teaching resources on Virtuale

  • Course Timetable from Oct 19, 2021 to Nov 30, 2021


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course, the student will acquire basic knowledge on chemometric methods for the analysis of data obtained from analytical investigations of cultural heritage. In particular, the student will acquire knowledge on multivariate methods for the evaluation and management of single point analysis and hyperspectral imaging analysis (HSI) data matrices. The student will also acquire new skills on the use of software for computation and will practice on real case studies.

Course contents

The course will be focused on well-established chemometric methods for the evaluation of spectroscopic data.

  • Preprocessing strategies (derivatives, standard normal variate transform, detrending, smoothing, row profile, etc).
  • Unsupervised exploratory methods (principal component analysis - PCA).
  • Supervised methods (classification, regression); construction, optimization and validation of predictive models.
  • Discussion of real case studies from Cultural Heritage researches.
  • Hands-on session on dedicated software packages freely available (CAT, Matlab).


Lecture slides

Reference materials (not compulsory):

Brereton, Richard G. Applied chemometrics for scientists. John Wiley & Sons, 2007.

Oliveri, Paolo, Cristina Malegori, and Monica Casale. "Chemometrics: multivariate analysis of chemical data." Chemical Analysis of Food: Techniques and Applications (2020): 33.

Sciutto, Giorgia, et al. "An advanced multivariate approach for processing X-ray fluorescence spectral and hyperspectral data from non-invasive in situ analyses on painted surfaces." Analytica chimica acta 752 (2012): 30-38.

Sciutto, Giorgia, et al. "Analysis of paint cross-sections: a combined multivariate approach for the interpretation of μATR-FTIR hyperspectral data arrays." Analytical and bioanalytical chemistry 405.2 (2013): 625-633.

Sciutto, Giorgia, et al. "Macroscopic mid-FTIR mapping and clustering-based automated data-reduction: An advanced diagnostic tool for in situ investigations of artworks." Talanta 209 (2020): 120575.

Teaching methods

The course includes both classroom lectures with powerpoint presentations and practical sessions based on the use of software packages for multivariate data processing.

Assessment methods

The final exam consists of a discussion on the topics covered in class, aimed at the evaluation of the theoretical knowledge acquired during the course, the possession of a specific language and the acquisition of an organic vision of the topics covered in class.
Good or excellent grades can be achieved by students who demonstrate a critical knowledge of the subject, who are able to apply theoretical concepts to practical examples and make use of an appropriate language. Mostly mnemonic knowledge, limited abilities of synthesis and analysis and imprecise language lead to grades ranging from discrete to sufficient. Important gaps, inappropriate language, lack of an overview of the topics covered will inevitably lead to a barely adeguate grade or to a negative evaluation.

The topics discussed during the laboratory session are an integral part of the oral examination.

At the end the teacher assigns a score (from 18/18 to a max of 30/30 cum laude) that reflects student's degree of preparation.

Teaching tools

Videoprojector, PC.

Office hours

See the website of Giorgia Sciutto