Teaching GuideTerm Faculty of Science |
Grao en Química |
Subjects |
Advanced Analytical Chemistry and Chemometrics |
Contents |
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Identifying Data | 2020/21 | |||||||||||||
Subject | Advanced Analytical Chemistry and Chemometrics | Code | 610G01015 | |||||||||||
Study programme |
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Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Graduate | 1st four-month period |
Fourth | Obligatory | 6 | ||||||||||
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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 | Students will apply the theoretical concepts studied in the theoretical lessons with the application of the analytical methodologies necessary to solve a real problem in the environmental, industrial, food, clinical ... |
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