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In recent times, multivariate methods have been used to study the sources of variation of surface and groundwater chemistry.

Multivariate methods have been developed to study complex data with many variables analyzed simultaneously.

Statistically classic multivariate methods have been applied to GWAS of multivariate phenotypes to tackle in an effective manner.

More recently, multivariate methods have been proposed that account for co-reported medicines and the indication for treatment [ 17– 20].

Although a variety of bivariate and multivariate methods have been proposed for this purpose [ 17, 32], there is a lot of room for new techniques that provide better estimates of the dependencies, or provide more information about the nature of these dependencies.

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The transesterification of triglycerides by means of alkaline catalysts is the most widely used route for biodiesel production and reaction optimisation by multivariate methods has been used for biodiesel production from different vegetable oils.

We also observe that RF, which is the more stable multivariate method, has globally a good performance also in terms of AUC.

Canonical correlation analysis (CCA), a bi-multivariate method, has been applied to imaging genetics applications.

In order to ensure operation safety and consistent product quality, multivariate statistical methods have been widely adopted in chemical batch process monitoring.

Soft-modeling and multivariate analysis methods have been shown to be very powerful for the analysis of electrochemical data, especially for multianalyte calibration and modeling in multicomponent dynamic systems.

Recently, the combination of circular dichroism, an UV-based spectroscopy, with multivariate analysis methods have allowed differentiation of low molecular weight heparins obtained by different methods of production, as well as naturally occurring GAGs [29].

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