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Authors: Carlos Cernuda ; Edwin Lughofer and Erich Peter Klement

Affiliation: JKU Johannes Kepler University Linz and FLLL Fuzzy Logic Lab Linz, Austria

ISBN: Not Available

Keyword(s): Chemometrics, Incremental Modelling, Active Learning

Abstract: The European chemical industry is the world leader in its field. 8 out of the 15 largest chemical companies are EU based. Furthermore, 29 % of the worldwide chemical sales originate from the EU. These industries face future challenges such as rising costs and scarcity of raw materials, an increase in the price of energy, and an intensified competition from Asian countries. Process Analytical Chemistry represents one of the most significant developments in chemical and process engineering over the past decade. Chemical information is of increasing importance in today's chemical industry. It is required for efficient process development, scale-up and production. It is used to assure product quality and compliance with regulations that govern chemical production processes. If reliable analytical information on the chemical process under investigation is available, adjustments and actions can be undertaken immediately in order to assure maximum yield and product quality while minimizing e nergy consumption and waste production. As a consequence, chemical information has a direct impact on the productivity and thus competitiveness, and on the environmental issues of the respective industries. Chemometrics is the application of mathematical or statistical methods to chemical data. The International Chemometrics Society (ICS) offers the following definition: “Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods”. Chemometric research spans a wide area of different methods which can be applied in chemistry. There are techniques for collecting good data (optimization of experimental parameters, design of experiments, calibration, signal processing) and for getting information from these data (statistics, pattern recognition, modeling, structure-property-relationship estimations). In this extense list of tasks, we are focused on calibration. Calibration consists on stablishing relationships, i.e. chemometric models, between some instrumental response and chemical concentrations. The usual instrumental responses come from the use of spectrometers, because they allow us to get a lot of on-line cheap data in a non-destructive way. There are two types of calibration, univariate or multivariate calibration, depending on the use of only a single predictor variable or several ones. The current instalations provide us with thousands of variables and thousands of samples, thus more and more new sophisticated techniques, which are capable to handle and take advantage of this tsunami of data, are required. Our goal is to provide the analytical chemistry community with modern and sophisticated tools in order to overcome the incoming future challenges. (More)

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Paper citation in several formats:
Cernuda, C.; Lughofer, E. and Klement , E. (2013). Advanced Learning Techniques for Chemometric Modelling.In Doctoral Consortium - Doctoral Consortium, (IJCCI 2013) ISBN Not Available, pages 19-28

@conference{doctoral consortium13,
author={Carlos Cernuda. and Edwin Lughofer. and Erich Peter Klement .},
title={Advanced Learning Techniques for Chemometric Modelling},
booktitle={Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)},
isbn={Not Available},


JO - Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)
TI - Advanced Learning Techniques for Chemometric Modelling
SN - Not Available
AU - Cernuda, C.
AU - Lughofer, E.
AU - Klement , E.
PY - 2013
SP - 19
EP - 28
DO -

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