B2142 - Chemometrics for Cultural Heritage

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 introduces the theoretical foundations and practical application of chemometric methods for the analysis of multivariate spectroscopic datasets, with particular emphasis on hyperspectral imaging (HSI) applications in Cultural Heritage.

Topics include:

• fundamentals of chemometrics and multivariate analytical data;
• signal preprocessing and its influence on data interpretation;
• exploratory data analysis using Principal Component Analysis (PCA);
• supervised methods for classification, regression, model optimisation and validation;
• processing and interpretation of hyperspectral imaging datasets;
• discussion of real case studies and practical sessions using the software packages CAT and PoliBrush.
  

Readings/Bibliography

Reference materials (not compulsory!):

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

P. Oliveri, C. Malegori, R. Simonetti, and M. Casale, “The impact of signal pre-processing on the final interpretation of analytical outcomes – A tutorial,” Analytica Chimica Acta, vol. 1058, pp. 9–17, 2019, doi: 10.1016/j.aca.2018.10.055

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 an oral interview on the topics covered during the course. It aims to assess the acquired knowledge, critical and methodological skills, specific terminology, and an overall understanding of the subjects discussed.

The ability to present concepts with fluency, excellent command of language, strong integration of acquired knowledge, and critical thinking will be rewarded with top grades (30 cum laude – 29).

Thorough knowledge of the concepts and good language skills, even if some support from the instructor is needed to make connections between topics, will result in high to mid-range grades (28–26).

Primarily rote memorization, limited analytical and summarizing skills, and/or language that is not always appropriate will lead to grades ranging from satisfactory to sufficient (25–20).

Significant gaps in knowledge and inadequate language use will result in a barely passing grade (18) if a minimal factual foundation is demonstrated, or a failing grade in the case of more serious deficiencies.

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

For the purposes of assessment, the use of Artificial Intelligence (AI) tools is permitted only to a limited, declared, and non-substantive extent for support activities (e.g., summarising or rephrasing text). The substantial use of AI to complete any part of the assessment is not permitted.

Teaching tools

Videoprojector, PC.

Software packages freely available (CAT, Polibrush)

Office hours

See the website of Giorgia Sciutto