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The optimization of the extraction conditions was carried out using multivariate strategies such as factorial design and response surface methodology.
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When a large variation in both chemical and physical variables is defined, the traditional two-way calibration strategies, such as partial least squares (PLS), do not allow the obtaining of multivariate model with good predictive ability (nonlinear predictions and prediction error over 8% w/w).
Multivariate strategies to disease diagnosis have been proposed for risk assessment and clinical decision-making, in diseases such as acute coronary syndromes[7], [8].
The optimization of the extraction conditions was carried out using multivariate strategies, which were factorial design and response surface methodology.
Strategies such as adding notes have been called 'optimising' strategies.
This included using strategies such as journaling.
But more important, we need to translate these biomarkers from discovery to clinical diagnostics using good strategies, such as the lessons learned from translating the biomarkers discovered using proteomic technologies to OVA 1, the first FDA-cleared In Vitro Diagnostic Multivariate Index Assay (IVDMIA).
A multivariate strategy for the microwave digestion of mastic was carried out.
The multivariate strategy was applied to estimate the optimum values of experimental variables (pH, time temperature, ligands and surfactant concentrations).
Experimental designs were used as a multivariate strategy for the effect's evaluation of varying several variables.
The final multivariate strategy we introduce here is a variation of the FD strategy.
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