Study programme competencies |
Code
|
Study programme competences / results
|
A14 |
Ability to demonstrate knowledge and understanding of concepts, principles and theories in chemistry |
A15 |
Ability to recognise and analyse new problems and develop solution strategies |
A16 |
Ability to source, assess and apply technical bibliographical information and data relating to chemistry |
A17 |
Ability to work safely in a chemistry laboratory (handling of materials, disposal of waste) |
A19 |
Ability to follow standard procedures and handle scientific equipment |
A20 |
Ability to interpret data resulting from laboratory observation and measurement |
A21 |
Understanding of qualitative and quantitative aspects of chemical problems |
A22 |
Ability to plan, design and develop projects and experiments |
A23 |
Critical standards of excellence in experimental technique and analysis |
A26 |
Ability to follow standard laboratory procedures in relation to analysis and synthesis of organic and inorganic systems |
B2 |
Effective problem solving |
B3 |
Application of logical, critical, creative thinking |
B4 |
Working independently on own initiative |
B5 |
Teamwork and collaboration |
C2 |
Oral and written proficiency in a foreign language |
C3 |
Ability to use basic information and communications technology (ICT) tools for professional purposes and learning throughout life |
C4 |
Self-development as an open, educated, critical, engaged, democratic, socially responsible citizen, equipped to analyse reality, diagnose problems, and formulate and implement informed solutions for the common good |
C6 |
Ability to assess critically the knowledge, technology and information available for problem solving |
C8 |
Understanding role of research, innovation and technology in socio-economic and cultural development |
Learning aims |
Learning outcomes |
Study programme competences / results |
To know how to select the proper analytical methodology for each particular problem. |
A15 A16 A20 A22 A26
|
B3
|
C4 C6 C8
|
To know how to plan and execute the different stages of the analytical procedure to quantify analytes at trace levels, including the interpretation of the data. |
A14 A17 A19 A20 A21 A23
|
B2 B4
|
C3
|
To know the main objectives of the most common chemometric techniques and to know their main application fields. To know how to extract relevant information from a multivariate study, in particular of a simplified environmental problem. |
A14 A15 A16 A20 A26
|
B2 B4 B5
|
C2 C3 C4 C6
|
Contents |
Topic |
Sub-topic |
Chapter 1: Introducing trace analysis |
Importance of quantifying substances at trace levels. The analytical process when determining trace amounts: special requirements. Basic requisites and importance of sampling. Sources of errors when storing and treating samples. Quality assurance in trace analyses.
|
Chapter 2: Analyzing inorganic substances |
Introduction. Decomposition and dissolution of inorganic matrices. Separation and preconcentration. Speciation of some relevant chemical elements. Examples of analytical applications.
|
Chapter 3: Analyzing organic substances |
Introduction. Extraction methods for solid and liquid samples. Purification, fractionation and concentration of organic extracts. Examples of analytical applications. |
Chapter 4: Automation in the analytical laboratory |
Objectives of laboratory automation. Pros and cons. Classification of the automated analytical systems. Robotics. Miniaturization. Analysis of industrial processes. |
Chapter 5: Introducing chemometrics |
Defining chemometrics and its role in the analytical process. Concept of uncertainty and basic calculations. |
Chapter 6: Statistical inference and univariate calibration |
Most common inference statistical tests in laboratories. Analysis of Variance. Examples of applications in laboratories and industrial process control. Classical calibration by the least squares fit. Validation. Confidence intervals. |
Chapter 7: Experimental design and optimization |
Basic ideas of experimental design and optimization. Factorial designs, fractional factorial designs, Plackett-Burman designs, response surfaces. Sequential optimization by Simplex.
|
Chapter 8: Multivariate data analyses |
Introduction. Classification of the most common pattern recognition methods. Unsupervised methods: principal components analysis, clustering. Supervised methods: SIMCA, k-nearest neighbours. |
Laboratory |
Pupils will apply the theoretical concepts studied throughout the theoretical lessons. Laboratory problems will deal with real problems in the environmental, industrial or foodstuff fields (among others) that pupils have to solve. |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Laboratory practice |
A15 A16 A17 A19 A20 A21 A22 A23 A26 B3 B4 B5 |
20 |
30 |
50 |
Seminar |
A15 A16 A20 A21 B2 B3 B4 C3 |
6 |
12 |
18 |
Workbook |
C4 C6 C8 |
1 |
2.5 |
3.5 |
Guest lecture / keynote speech |
A15 A16 A21 A22 C4 |
21 |
52.5 |
73.5 |
Mixed objective/subjective test |
A14 A15 C2 |
3 |
0 |
3 |
|
Personalized attention |
|
2 |
0 |
2 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Laboratory practice |
They will consist on the determination of some analytes at trace levels in samples gathered from real problems (environmental samples, foodstuff, clinic mixtures, etc.). The practices emulate a comprehensive problem: from sampling to sample treatment, separation of the analyte, measurement and data interpretation. The student must deliver a laboratory notebook.
In addition to laboratory practices where analytical instrumentation will be handled by students, other practices will be carried out on computers to study the chemometric concepts (mainly, the multivariate data analyses techniques).
At the end of the laboratory work the student will deliver a report of the work done with a critical and detailed analysis. |
Seminar |
They are intended to reinforce the understanding of several concepts given at the theoretical lessons. Numerical exercises will be solved by the students. A comparison of the results generated in the laboratory practices will be made with other values gathered from other students. From the discussions, common sources of errors will be visualized. The student should perceive the difficulties inherent to the analyses of trace amounts of substances.
Studies will be also made using computers to discuss a real multivariate dataset derived from environmental studies. |
Workbook |
Some specific, short readings will be proposed by the teacher. These will consist of reports where from the students will deliver a small report explaining some key ideas (e.g., a summary of the analytical strategy undertaken to solve the problem). |
Guest lecture / keynote speech |
The teacher will develop and explain the basic contents of each chapter. Some documents will be delivered to the students before the classes and they should have been reviewed before attending them. Audiovisual media will be employed throughout. Open dialogue will be empowered sometimes to solve doubts and improve the understanding of some basic issues. |
Mixed objective/subjective test |
Written test to be held in the official call on January / July, in which the degree of learning and the acquisition of skills by the student is evaluated.
It will consist of theoretical questions and applied questions, problem solving and practical content. The completion date is indicated on the examination timetable grade. |
Personalized attention |
Methodologies
|
Workbook |
Seminar |
Laboratory practice |
|
Description |
Close supervision here means that the teacher will monitor as close as possible the activities of the student. The personal work of the student will be required and tested. The teacher may recommend further readings, clarify wrong statements, recommend literature searches, etc.
Personal attention will also include the general scheduled tutorships.
Students being recognized officially as partial-time and entitled not to attend the lectures will be attended in a tutorships regime (set hour with teacher in advance). |
|
Assessment |
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
Workbook |
C4 C6 C8 |
The report delivered by the student will be examined. In particular, identification and justification of the analytical strategies presented into the work.
|
5 |
Seminar |
A15 A16 A20 A21 B2 B3 B4 C3 |
Active participation of the students will be scored, as well as the correct answers to questions or numerical calculations.
|
10 |
Laboratory practice |
A15 A16 A17 A19 A20 A21 A22 A23 A26 B3 B4 B5 |
They will be scored on a on-going basis (order into the laboratory, correctness of the calculations, good manual operations, report delivered on-time, etc.). |
15 |
Mixed objective/subjective test |
A14 A15 C2 |
The exam will consist of tests (with a unique true response), short questions and numerical exercises. They will be related to the theoretical aspects of the subject. Some questions on these practices will be included in the objective test.
|
70 |
|
Assessment comments |
Students will be evaluated in a continuous way according to their attendance to the scheduled activities, their engagement in the seminars, their discussions on the questions and numerical calculations, summary of workbook, the laboratory practices and the mixed test. Laboratory practices are mandatory, otherwise the subject will not be aproved. To pass the subject, all activities must be scored as 4, at least (on a 10-points scale). All the scores will be averaged and to pass the subject the average should be 5 or higher. However, note that the subject will not be aproved (even when the overall sum exceeds 5) if a particular score does not reach 4. In this case, the final score of the subject will be "fail" (score = 4,5). The mixed test will consist of two parts: theoretical questions and exercises, each part must be passed. The studen will obtain the qualification of "Not presented" when do not perform the laboratory practices and the mixed test. The ‘second opportunity’ of the subject this corresponds only to the mixed mixed. This means that the scores of the other activities will be maintained. If the student fails to pass the subject, allactivities should be repeated in following academic courses. No scores will be maintained. Students being recognized officially as partial-time and/or exempted from regular attendance to the lessons, will be evaluated only according to their scores on the objective tests (80%), workbook (5%) and the laboratory practices (15%). For them, the laboratory practices will be scheduled as flexible as possible, although taking into account the regular timetables, as well as the instrumental and human resources available. This holds for both evaluation opportunities.
|
Sources of information |
Basic
|
CaMARA, C.; PEREZ-CONDE, C (2011). Análisis químico de trazas. Madrid, Sintesis
MILLER, J.N.; MILLER, J.C. (2002). Estadística y quimiometría para química analítica, 4th edition. Madrid, Prentice-Hall
RAMIS, G.; GARCIA, M.C. (2001). Quimiometria. Madrid, Sintesis
CAMARA, C.; FERNANDEZ, P.; MARTIN-ESTEBAN, A.; PEREZ-CONDE, C.; VIDAL, M. (2002). Toma y tratamiento de muestra. Madrid, Sintesis |
|
Complementary
|
KELLNER, R,; MERMET, J.M.; OTTO, M.; WIDMER, H.M. (1998). Analytical chemistry: a modern approach to analytical science. Winheim, Willey-VCH
OTTO, M. (2007). Chemometrics. Weingeim, Willey-VCH
VALCARCEL, M.; CARDENAS, M.S. (2000). Automatización y miniaturización en química analítica. Barcelona, Springer-Verlag |
|
Recommendations |
Subjects that it is recommended to have taken before |
Química Analítica 1/610G01011 | Química Analítica 2/610G01012 | Química Analítica Instrumental 1/610G01013 | Química Analítica Instrumental 2/610G01014 |
|
Subjects that are recommended to be taken simultaneously |
Medio ambiente e calidade/610G01037 |
|
Subjects that continue the syllabus |
Traballo de fin de Grao/610G01043 |
|
Other comments |
To keep the
subject updated is highly recommended. This includes reviewing the theoretical
lessons after the classes, solving the numerical exercises, studying the
practical classes, etc. Students should take advantage of seminars, supervision
activities, etc. to solve their doubts. They should try to generate a sense of
‘analytical criterion’ to solve a problem; from sampling to data treatment. Students
will need knowledge of the analytical techniques studied in previous academic
courses (gravimetry, titration, spectrometry, chromatography, electrochemistry,
etc.) A minimum
knowledge of informatics is needed (word processors, spreadsheets, searches
throughout internet, etc.). |
|