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P1150571
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International School of Chemometrics  -  ISC 2021

INFORMATION ABOUT THE SEMINARS

NOTE: All seminars include all the material that the student might need: 

- Slides of the course (pdf).

- Exercises

- Datasets

- Toolboxes

- Refreshment during the lessons (coffee, tea, candies, cookies, and other amenities). This, of course, if we are allowed due to the Covid-19 pandemic situation

- Unlimited Off-line access to the videos of the lessons until the end of 2021.

- We do NOT provide: Matlab and lunch

 

BASIC - Basic introduction to Chemometrics, data types, data pre-processing, PCA, Multivariate Linear Regression and Multivariate Linear Classification

 

This seminar contains 2 topics:

 

- EXPLORE - Data exploration and regression: Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here we present a course to show the main benefits and drawbacks of PCA when it is used for different kind of analytical data: Spectroscopy, environmental assessment, sensory, experiments performance, chromatography, etc. Moreover, preprocessing of different type of data will be also addressed in the seminar as a prerequisite for having the optimal possibility for exploring the data. If PCA is the keystone of pattern recognition methods, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies and will focus in Partial Least Squares regression.

- CLASS - Multivariate Classification: The course will deal with main classification linear methods. We will initially introduce what is classification and a bit of terminology, such as the difference between class modelling and discriminant methods, classification measures, validation approaches. Then, we will move to main classification methods, such as Discriminant Analysis, Partial Least Squares Discriminant Analysis (PLSDA) and SIMCA. We will see both theoretical aspects and practical applications. There will be in fact practical sessions where we will apply classification methods to real data with ad-hoc toolboxes in MATLAB.to learn how to handle these tasks.

 

Dates and timetable: From the 2nd of May until the 6th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (30 hours)

 

Previous knowledge needed: None

Software needed: Matlab, PLS_Toolbox and Classification toolbox working under Matlab. IMPORTANT: For the PLS_Toolbox a fully functional demo will be available for the School. The Classification toolbox can be freely downloaded from here: 

https://michem.unimib.it/download/matlab-toolboxes/classification-toolbox-for-matlab/

Teachers: José Manuel Amigo (EXPLORE) and Davide Ballabio (CLASS).

ECTS: 2.5

Price student: 1500 DKK (Approx. 200 euro / 245 USD)

Price industry: 3750 DKK (Approx. 505 euro / 612 USD)

 

INTERMEDIATE - Intermediate topics on Chemometrics. Variable selection methods, Multivariate Curve Resolution and Multi-Way Modeling

 

This seminar contains three topics:

 

- VARSEL - Variable selection methods: This seminar aims at revisiting the most important variable selection methods for regression and classification purposes with the aim of improving the performance of the models. The emphasis will be on practical applications, and what methods could be applied to which problem. There will also be hints as to what methods are good, and which ones to stay away from.

- MCR - Multivariate Curve Resolution: The module will address the theoretical description and hands-on application of Multivariate Curve Resolution (MCR). MCR is a multivariate resolution (unmixing) method that can provide the description of a multicomponent data set through a bilinear model of chemically meaningful profiles, e.g., when analyzing an HPLC-DAD data set, MCR would provide the real elution profiles and the related UV spectra for each compound in the sample. It has application in many diverse fields, such as process analysis, chromatographic data, hyperspectral images or environmental data, essentially in any context where a mixture analysis problem can be encountered. MCR can be applied to a single data matrix or to multiset structures formed by blocks of different information (data fusion). The module focuses mainly on the algorithm MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares) and hands-on work will be done using a dedicated free GUI interface adapted to MATLAB environment. Applications will cover many of the areas mentioned above.

- MULTIWAY - Multiway data analysis: Multi-way data is gaining popularity due to the capability of scientific devices to generate data with, at least, 3 dimensions (elution time – mz channel – samples, excitation-emission – sample, etc). Therefore, learning the basics of multi-way analysis will help to extract the most of that complex data structure. In this sense, methods like parallel factor analysis (PARAFAC) and PARAFAC2 will be studied and applied to different examples.

Dates and timetable: From the 9th until the 13th of May, 2022. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours)

 

Previous knowledge needed: Basic multivariate data analysis and Matlab

Software needed: Matlab and PLS_Toolbox. IMPORTANT: For the PLS_Toolbox a fully functional demo will be available for the School. MCR-ALS toolbox. MCR-ALS toolbox can be freely downloaded here: https://mcrals.wordpress.com/download/mcr-als-2-0-toolbox/

Teachers: Asmund Rinnan (VARSEL), Anna de Juan (MCR) and Rasmus Bro (MULTIWAY).

ECTS: 2.5

Price student: 1500 DKK (Approx. 200 euro / 245 USD)

Price industry: 3750 DKK (Approx. 505 euro / 612 USD)

CHALLENGES - Topics for a further understanding of advanced modeling methods

This seminar contains three topics:

- NONLIN - Nonlinear modeling: This module aims at providing a basic introduction to the techniques which may be used in all those situations when a linear relation is not enough to provide accurate results (e.g. due to the presence of multiple sources of variability). In this respect, the most important aspects of data modeling will be considered (exploratory analysis, classification and calibration). Topics such as kernel and dissimilarity-based approaches (including support vector machines), local modeling (kNN and locally weighted regression/classification) and artificial neural networks will be covered. 

- FUSION - Multivariate data fusion approaches: The seminar will deal with the chemometric approaches for integrating (“fusing”) data from different sources. First of all the various configurations which may occur when dealing with multiple data matrices will be presented and discussed and a hierarchy/systematization of the possible data fusion approaches will be introduced. Then the main multi-block strategies for data exploration and predictive modelling will be discussed and compared and further classification of models depending on whether the globally common, locally common and distinct information is considered or not will be introduced. The theoretical and algorithmic description of the methods will be accompanied by worked examples of real data sets.

- ERROR - Error propagation in multivariate models: The seminar will deal with “Measurement Error (ME)”. First, we will introduce measurement errors, show the different types of noise and learn how to propagate them to the final result in the univariate case. Then, we will move to multivariate data and will show how to characterize and simulate multivariate ME, with special emphasis in error covariance matrices and how these can provide a clue about the structure of the errors of our data. Finally, we will apply the error propagation theory to the multivariate scenario and learn how to calculate the uncertainty of prediction (PLS) and classification.

Dates and timetable: From the 16th until the 20th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (30 hours)

 

Previous knowledge needed: Basic multivariate data analysis and Matlab

Software needed: Matlab and PLS_Toolbox. IMPORTANT: For the PLS_Toolbox a fully functional demo will be available for the School.

Teachers: Rasmus Bro (NONLIN), Federico Marini (FUSION) and Ricard Boqué (ERROR).

ECTS: 2.5

Price student: 1500 DKK (Approx. 200 euro / 245 USD)

Price industry: 3750 DKK (Approx. 505 euro / 612 USD)

DoE - Design of Experiments

The course gives an introduction to Experimental Design. Most part of it will be devoted to understand the correct approach a researcher (in any field) should have when performing experiments and to highlight the critical points to be coped with when doing an Experimental design.
Compatibly with the very limited time allowed, some classical designs will be discussed (Factorial, Plackett-Burman, Central Composite) together with the very powerful D-Optimal Designs. 

Dates and timetable: From the 23rd until the 24th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (12 hours)

 

Previous knowledge needed: Basic knowledge of PCA and multivariate regression methods.

Software: The free software CAT (Chemometric Agile Tool) will be used, downloadable from:

http://www.gruppochemiometria.it/index.php/software.

Teacher: Riccardo Leardi

ECTS: 1

Price student: 600 DKK (Approx. 81 euro / 98 USD)

Price industry: 1500 DKK (Approx. 200 euro / 245 USD)

ASCA - ANOVA Simultaneous Component Analysis

Anova Simultaneous Component Analysis (ASCA) is a versatile tool for analyzing multivariate data from designed experiments. Based on ANOVA variance partitioning followed by bi-linear modeling of the individual effect matrices the method offers detailed insight into small systematic variance contributors that otherwise are masked and uncovered by traditional techniques such as PCA. This workshop will uncover the basic principles in ASCA and give the attendees hands-on experience on how to use the tool on real data using the PLStoolbox in Matlab® as well as Matlab on its own.

Dates and timetable: From the 23rd until the 24th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (12 hours)

 

Previous knowledge needed: Basic knowledge of PCA and multivariate classification methods.

Software: Matlab and PLS_Toolbox. IMPORTANT: For the PLS_Toolbox a fully functional demo will be available for the School.

Teacher: Morten A. Rasmussen

ECTS: 1

Price student: 600 DKK (Approx. 81 euro / 98 USD)

Price industry: 1500 DKK (Approx. 200 euro / 245 USD)

HYPER - Multivariate and Hyperspectral Image Analysis

Hyperspectral imaging is an important analytical tool in a growing number of areas including the chemical sciences, process monitoring and forensics, cultural heritage, and remote and standoff sensing. Images are acquired utilizing a wide variety of spectroscopic techniques including, but not limited to, infra-red, mass spectroscopy, Raman, etc., and it is often confusing how to efficiently extract information from the sizeable, multi-wavelength images. This course will cover a variety of multivariate methodologies that can be applied to analysis and interpretation of hyperspectral data starting with principal components analysis (PCA) and using linked plots. Other techniques that will be discussed will include maximum autocorrelation factors, maximum difference factors, multivariate curve resolution (end-member extraction), target detection and targeted anomaly detection. This course will use the MATLAB environment with PLS_Toolbox+MIA_Toolbox and also the standalone software Solo+MIA_Toolbox. The course content will be useful for those involved in chemical, food, pharmaceutical and medical imaging, remote and standoff imaging.

Dates and timetable: From the 25th until the 26th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (12 hours)

 

Previous knowledge needed: Principal Component Analysis. Multivariate Curve Resolution and Regression would also be useful.

Software: Matlab and PLS_Toolbox. IMPORTANT: For the PLS_Toolbox a fully functional demo will be available for the School.

Teacher: Neal B. Gallagher

ECTS: 1

Price student: 600 DKK (Approx. 81 euro / 98 USD)

Price industry: 1500 DKK (Approx. 200 euro / 245 USD)

METABO - Metabolomics Data Analysis

 

The seminar will start with introducing various pre-processing techniques suitable for various –omics data. The problems of noise, baseline removal, alignment and normalization will be discussed using several examples. The seminar will follow on presenting several machine learning ensemble techniques, such as unsupervised random forest, random forest, gradient boosting trees and the principle of non-linear bi-plots. The theoretical background of these techniques will be provided, accompanied by several real-life data examples. The seminar will summarise by introducing the theory and application principle of ensemble stacking. 

Dates and timetable: From the 25th until the 26th of May, 2022. From 9 am until 4 pm (CET) with a 1-hour lunch break (12 hours)

 

Previous knowledge needed: Basic knowledge of PCA and multivariate regression and classification methods

Software: Matlab and git

Teacher: Agnieszka Smolinska

ECTS: 1

Price student: 600 DKK (Approx. 81 euro / 98 USD)

Price industry: 1500 DKK (Approx. 200 euro / 245 USD)

GLUE - How not to make Chemometrics

In this half-day seminar, we will take a very close look at all the most common mistakes that even experienced people will do when doing multivariate analysis. We will cover exploration, calibration, interpretation, visualization and many other subjects. And always with a focus on what is the most common problem as well as a sounder alternative.

 

Dates and timetable: 27th of May, 2022. From 9 am until 12 pm (CET) (3 hours)

Previous knowledge needed: Chemometrics

Software: None

Teacher: José Manuel Amigo and Rasmus Bro

ECTS: 0

Price student and industry: 0 DKK (Approx. 0 euro / 0 USD)