- Docente: Dora Melucci
- Credits: 6
- SSD: CHIM/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Chemistry (cod. 8856)
Learning outcomes
Students learn basics about theory and practice of Chemometrics for Analytical Chemistry.
At the end of the course, students have the following expertise: design of experiments; data processing by multivariate analysis; use of modern software for the application of mathematical and statistical methods.
Students will be able to apply the new skills to real problems concerning applications and research.
Course contents
Prerequisites
Students attending this course must have a good education in the fundamentals of analytical chemistry and instrumental analytical techniques.
Target
The course aims to provide students with the ability to design a chemical-analytical methodology from sampling to data analysis, starting from the design of experiments to arrive at the correct processing of data and presentation of the final technical report.
With these objectives, the following mathematical and statistical knowledge are provided:
Elements of multivariate statistical analysis
Methods of exploration of multivariate data
Multivariate modeling methods: multivariate classification and regression
Design of experiments (DOE)
The student will acquire the computer skills for the application of chemometric methods learned.
Finally, the student will develop the specific competence of a Chemometrician: optimize an entire chemical analysis process.
Contents
DATA EXPLORATION.
Multivariate structure of data. Matrices: dimension, transposition, centering, covariance, correlation. Pretreatment of the data. Transformation of variables. Handling of missing data.
Principal component analysis. Loading plots. Score plots. Choice of principal components (rank analysis), both numerically and graphically (scree plot).
Clusters analysis. Distance matrix, similarity matrix. Agglomerative hierarchical methods for the analysis of clusters. Dendrograms.
MODELLING
Models. Order and linearity of a model. Control parameters. Validation of a model.
Classification: qualitative models. Confusion matrix. Loss matrix. Control parameters. Misclassification risk (MR%). Classification by K-NN. Discriminant analysis (DA). Classification by SIMCA. Classification by CART.
Calibration: quantitative models. Linear regression: MLR method. Leverages. Regression coefficients. Evaluation parameters for a regression model. Correlation coefficient. Prediction coefficient. Standard error of the estimate. Diagnostic methods for regression models. Principal Component Regression (PCR). Partial Least Squares Method (PLS). Practical examples of calibration by means of PLS regression: spectrophotometry, pulsed stripping voltammetry, chromatography-mass spectrometry.
DESIGN OF EXPERIMENTS
Multivariate methods for the selection of standard samples and variables for model creation. Full Factorial Design. Fractional Factorila Design. Mixtures Design.
Readings/Bibliography
- Roberto Todeschini, Introduzione alla chemiometria, Edises, 1998.
- J.C. Miller, J.N. Miller, Statistics and Chemometrics for Analytical Chemistry, Pearson Education, 2010.
- Richard G. Brereton, Applied Chemometrics for Scientists, Wiley, 2007.
- Richard Kramer, Chemometric techniques for quantitative analysis, Marcel Dekker, 1998.
Teaching methods
The course consists of lectures (32 hours) and exercises in the
computer lab (24 hours).
Lectures are dedicated to the acquisition of the basic concepts of
Chemometrics and to the acquisition of
specific informatic tools (software for mathematics and
statistics).
Exercises in the computer lab are designed to enable students to
use Chemometrics tools and to apply concepts and software to solve
real problems of multivariate chemical analysis.
Crucial will be the use of material provided by the lecturer made
available online [https://iol.unibo.it/] and lecture notes.
Assessment methods
At the end of the course, students must deliver a report about a
chemometric problem, relevant to a dataset provided by the
teacher. Problems are individual: each student works on a different
dataset. The chemometric data-processing in the final report is
similar to what explained during the course, in guided exercises
relevant to model-datasets. The report must be in the form of
a text document. It is NOT required to deliver file corresponding
to the numerical calculations, while it is required that numerical
or graphical outputs be included . The teacher assigns a mark to
the final report.
The examination consists of oral questions about the final report
and the theory explained in the room lessons (definitions and
demonstrations). The teacher assigns a mark to the oral
examination.
The final mark is the average of the mark assigned to the report
and the mark assigned to the oral exam.
Teaching tools
Blackboard for theoretical lessons. Video projector for explanation
of spreadsheets. Informatic laboratory for exercises.
For lectures and exercises the teacher uses the following programs:
Microsoft Excel and The Unscrambler.
To carry out individual exercises and calculations for the final
report, students can use the PCs of the informatic laboratory.
Outside the dates scheduled for the course, students can access the
informatic lab in the presence of the teacher.
Links to further information
Office hours
See the website of Dora Melucci
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.