Chemometrics

Code:

Q4022

Acronym:

Q4022

Level:

400

Keywords
Classification	Keyword
OFICIAL	Chemistry

Instance: 2018/2019 - 2S

Active?	Yes
Responsible unit:	Department of Chemistry and Biochemistry
Course/CS Responsible:	Master in Chemistry

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:BQ	1	Plano de Estudos do MBIOQ_2013-2014	1	-	6	56	162
M:Q	4	Official study plan until 2022/2023	1	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

The objective of Chemometrics is to teach the theoretical background and use chemometric software tools for the experimental design and linear and non-linear data analysis.

Learning outcomes and competences

At the end of this unit the student will have acquired knowledge and skills to:

Knowledge:
- Summarize basic chemometric methods;
- Describe techniques for data pre-preprocessing;
- Develop suitable experimental designs;
- Describe chemometric methods for multivariate data analysis (clustering, classification and regression);
-Understand when to use multiway decomposition techniques

Skills:
- Apply theory on real life chemical data cases;
- Apply commercial software for data analysis;
- Report in writing a full data analysis of a given problem including all aspects presented under Knowledge.

Competences:
- Selection of the best chemometric to solve specific chemical problems;
- Discuss advantages and drawbacks of chemometric methods.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Program

1. Data structures and data preprocessing techniques

2. Experimental design (DOE)
2.1 – Factorial analysis
2.2 – Response surface

3. Principal components analysis (PCA)

4. Factor analysis (FA)

5. Multivariate curve resolution with alternating least squares (MCR-ALS)


6. Supervised classification techniques
6.1 – Linear discriminate analysis (LDA)
6.2 – Soft independent modeling by class analogy (SIMCA)
6.3 – Parallel factor analysis (PARAFAC)
6.4 - K nearest neighbors (KNN)
6.5 – Artificial neural networks (ANN)

7. Unsupervised classification techniques
7.1 – Linear and non-linear projection techniques
7.2 – Hierarchical clustering analysis techniques (HCA)

8. Calibration techniques
8.1 – Ordinary least squares (OLS)
8.2 - Multiple linear regrassion (MLR)
8.3 – Principal components regression (PCR)
8.4 – Partial least squares (PLS)

9. Introduction to Artificial Neural Networks (ANN)

10. Kohonen self-organizing maps (SOM)
11. Genetic algorithms (GA)

Theoretical and Practical

Mandatory literature

D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke; Handbook of Chemometrics and Qualimetrics: Part A, Elsevier, 1997
Brereton, R.G.; Applied Chemometrics for Scientists, Wiley, Chichester, 2007. ISBN: 978-0-470-01686-2

Complementary Bibliography

Brereton, R.G. ; Chemometrics: Data Driven Extraction for Science, Wiley, Chichester., 2018. ISBN: 978-1-118-90466-4 (2nd Edition)
K.R. Beebe, R.J. Pell, M.B. Seasholtz; Chemometrics: A Practical Guide, Wiley, 1998
E.Morgan; Chemometrics: Experimental Design, Wiley, 1991
D.C. Montgomery; Design and Analysis of Experiments, Wiley, 2001
D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke; Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, 1997
J. Zupan, J. Gasteiger; Neural Networks for chemistry – an introduction, VCH, 1993
H. Martens, T. Naes; Multivariate calibration, Wiley, 1989
E.R. Malinowski; Factor Analysis in Chemistry, Wiley, 1991

Teaching methods and learning activities

The students will be introduced to the theory through lectures. The students will work on data analytical problems using the taught methods and software to analyze data. The results are presented in written reports which are orally defended at predetermined dates during the semester.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Exame	35,00
Participação presencial	5,00
Prova oral	15,00
Trabalho prático ou de projeto	45,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	1,00
Estudo autónomo	85,00
Frequência das aulas	56,00
Trabalho escrito	20,00
Total:	162,00

Eligibility for exams

Calculation formula of final grade

The final rank has 3 components:

(1) Practical Component (M3TP):
Three (3) practical/written works (Papers) and corresponding oral presentation, distributed throughout the semester.
The maximum classification of each work is 4 values ​​(3 values ​​for the report and 1 value for the oral presentation). Failure to perform one or more practical works and its presentation has a zero weight in its components.

Papers should cover topics taught and should include the practical work of applying taught methods / techniques

In the oral presentations three factors are evaluated: quality of the presentation of support; presentation fluency; scientific quality.

(2) Theoretical Component (M2Ex)
Completion of two (2) written tests during the semester with a total weight of 30% (6 points)

(3) Participatory Component
Participation in all classes will have a bonus of 2 (Participative Component)



Final Score = 0.6 M3TP + 0.30 M2Ex + 0.10 CP

M3TP - Average of 3 assignments
M2Ex - Average of 2 written tests
CP - Participatory Component

CP = (P-NAP) * (2 / NAP),

where P is the number of attendances, NAP the number of classes planned.
The CP value is rounded to 2 decimal places.

Examinations or Special Assignments

Internship work/project

Special assessment (TE, DA, ...)

Classification improvement

Observations