Chemometrics

Recommended Prerequisites

  It is recomended to have previous approval on the following courses of the 1st cycle: Calculus I and II, and Caracterization and Validation of Analitical Methods  

Teaching Methods

 - Interactive lectures given by the teacher(s).

- Practical, computational, problem solving.    

Learning Outcomes

 Acquire proficiency in methods, algorithms and tools in multivariate analysis, for comprehensive chemical applications.   

Work Placement(s)

No

Syllabus

   1. Introduction

Data processing and information handling. Chemometrics and related disciplines.

2. Basic Statistics (brief review)

3. Experimental design

Simple factorial design. Two level  procedures. Fractional replication. Composite designs.

4. Methods of correlation and time series.

Covariance, correlation and regression. The covariance matrix. Time series: autocovariance and autocorrelation.

5. Cluster analysis

Definition. Distance and similarity. Hierarchical techniques. Partitioning-optimization techniques. Other methods.

6. Principal component and factor analysis

Interpretation. Principal components in two and n dimensions. Applications.

Analysis of factors: determination and rotation.

7. Supervised pattern recognition

Decision rules. Linear discriminant analysis. The method of k-nearest neighbors.

8. Advanced Topics

Partial least squares (PLS). Structure-activity relationships (QSAR). 

Head Lecturer(s)

Alberto António Caria Canelas Pais

Assessment Methods

Assessment
Resolution Problems: 25.0%
Exam: 75.0%

Bibliography

   75L. Sachs, “Applied Statistics: a Handbook of Techniques”, Springer, New York, 1982.

D.L. Massart, B.G.M. Vandeginste, S.N. Deming, Y. Michotte e L. Kaufman, “Chemometrics: a text book”, Elsevier, Amsterdam, 1988.

R.G. Brereton, Chemometrics : Data Analysis for the Laboratory and Chemical Plant, Wiley (Chichester, 2003).