Chemometrics

Course objectives

Chemometrics is the branch of chemistry that uses mathematics and statistics to extract useful information from experimental data. EXPECTED LEARNING RESULTS: 1) Knowledge and ability to understand The course aims to provide students with an introduction to the main techniques for analyzing data, in particular multivariate data, produced in the context of chemical experiments. Starting from classical statistics and proceeding through approaches of increasing complexity, students will learn the main methodologies that allow a rational design of the experiments, an exploratory analysis of the collected data and the formulation of predictive models. 2) Applied knowledge and understanding skills Through practical examples of application discussed in the classroom, the course aims to give students the chemometric bases to be able to critically apply the approaches illustrated to the various problems that may arise in the development, preparation and interpretation of the results of any chemical analysis or a chemical experiment, in general. 3) Autonomy of Judgment During the course, students are stimulated through targeted questions and short discussions to extend the examples seen in the classroom to more general situations, so as to develop as much as possible the ability to critically evaluate every aspect of the execution of a chemometric analysis. 4) Communication Skills In addition to the interaction during the lessons, through questions and short discussions, the course includes an oral exam for the passing of which students will have to acquire and demonstrate their ability to critically discuss the topics covered. 5) Learning Ability The set of course topics is designed to present students with increasing complexity issues and guide them in identifying how to extrapolate from each of them those points that can be generalized to other specific cases. In this way, the goal is that students can acquire a modus cogitandi/modus operandi that allows them to select and critically apply the most suitable chemometric tools to solve any problem that may arise in their subsequent training and professional experience.

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FEDERICO MARINI Lecturers' profile

Program - Frequency - Exams

Course program
Introduction: What is chemometrics and how it fits within the analytical chemistry disciplines. Recalls of statistics and probability theory: The measurement error as a result of random and systematic contributions. Statistical inference: from the sample to the population. Parameters characterizing a statistical distribution (mean and standard deviation). The Normal and standardized Normal (Z) statistical distributions. Student's t and Fisher's F distributions and their use. Statistical tests. Univariate Regression: The need for building a regression model. The least squares method. Calculation of the parameters of the interpolating line (intercept and slope) and of the respective uncertainties. Uncertainty on x and y predictions and confidence bands. Figures of merit to evaluate the regression (R2, RMSE, bias). Residual analysis. Principal component analysis (PCA): What principal components are and how to calculate them. Importance of the experimental variables in the definition of the principal components. Some criteria to select the appropriate number of principal components. Examples of application of the PCA method. PCA for the identification of anomalous data and outliers. Multivariate Regression: Direct and Inverse Calibration. The least squares method for multivariate data (multiple linear regression, MLR) in the case of one or more responses. Principal component regression (PCR). PLS regression for one or more responses. Interpretation of multivariate regression models. Classification: Discriminant classification methods and class-modeling techniques. Linear (LDA) and quadratic (QDA) discriminant analysis. Nonparametric classification: the kNN method. Using abstract variables for classification: the PLS-DA method. Class-modeling techniques: SIMCA. Validation: The use of predictive multivariate methods and the need for a more accurate estimate of their predictive ability.
Prerequisites
There are no particular prerequisites for attending the course
Books
Slides of the course. As a support, the following textbooks are recommended: Michele Forina, Fondamenta per la Chemiometria, Edizioni SISNIR, Lodi, 2012. Freely downloadable from: http://www.sisnir.org/index.php/edsisnir/10-fondamenti-di-chemiometria Roberto Todeschini, Introduzione alla chemiometria, EdiSES, Napoli, 1998. D.L. Massart et. al., Chemometrics. A textbook, Elsevier, Amsterdam, 1988. F. Marini (ed.), Chemometrics in food chemistry, Elsevier, Oxford, 2013.
Frequency
Course attendance is optional. Students can prepare the exam also only by studying the teaching material provided.
Exam mode
The students will be assessed based on the outcome of an oral examination to check the knowledge of the topics under consideration.
Lesson mode
The course consists of classroom lectures where the topics will be addressed from the theoretical point of view and through critically discussed application examples.
  • Lesson code10621168
  • Academic year2025/2026
  • CourseAnalytical Chemistry
  • CurriculumSingle curriculum
  • Year1st year
  • Semester2nd semester
  • SSDCHIM/01
  • CFU6