Data mining is usually the last, but not for this less important, step of any food analysis process. It rather represents a critical phase: in fact, a proper data processing allows the extraction of useful information about the system under study from large amounts of collected data – and getting information is usually the main objective in analytical chemistry. The classical univariate approach, which considers one variable at a time, underutilises the global data structure and offers just a partial image of it. Instead, multivariate strategies allow a more complete interpretation of data and exploitation of the information contained therein. Multivariate techniques can be used both for exploratory purposes and for qualitative or quantitative modelling. Generally, modelling is performed for predictive applications: in such cases, a thorough model validation is always required.

Data Analysis and Chemometrics

Oliveri, Paolo;Forina, Michele
2012

Abstract

Data mining is usually the last, but not for this less important, step of any food analysis process. It rather represents a critical phase: in fact, a proper data processing allows the extraction of useful information about the system under study from large amounts of collected data – and getting information is usually the main objective in analytical chemistry. The classical univariate approach, which considers one variable at a time, underutilises the global data structure and offers just a partial image of it. Instead, multivariate strategies allow a more complete interpretation of data and exploitation of the information contained therein. Multivariate techniques can be used both for exploratory purposes and for qualitative or quantitative modelling. Generally, modelling is performed for predictive applications: in such cases, a thorough model validation is always required.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/786401
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