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Classically, factorial multivariate methods are the only tool for analyzing and modeling sensory data provided by experts, panelists or consumers.
Traditional multivariate methods are known to have weaknesses [ 9, 10].
Multivariate methods are by their nature complex and difficult.
Multivariate methods are employed for the analysis of Raman spectra [ 12].
However, multivariate methods are computationally demanding since they consider the strength of association with disease of combinations of SNPs.
Multivariate methods are potentially more powerful than univariate approaches, but their output can also be more difficult to definitively interpret.
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Multivariate methods were utilized in a screening design with several responses of a tablet formulation.
The validation of the multivariate methods was realized by analyzing their synthetic mixtures.
Multivariate methods were used for patterns recognition and the classification of samples.
Here, we observe that the forecasting accuracy of the multivariate methods is relatively poor in the short runs.
The validation of the multivariate methods was realised by analysing the synthetic mixtures of hydrochlorothiazide and spironolactone.
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