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Characteristic mass of 23.4 and 19.4 pg and LOD of 17.9 and 11.3 μg L− 1 was obtained to conventional and multivariate methods, respectively.
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Multivariate methods should, therefore, be considered instead.
Data were analyzed using univariate and multivariate methods.
The proposed discretization algorithms are incremental and multivariate methods.
The clustering is made by the use of multivariate methods.
The chemical data obtained were elaborated with statistical multivariate methods.
It can further be divided into bivariate and multivariate methods.
Agronomic potential was assessed with six descriptors and multivariate methods.
Compared to conventional multivariate methods, multilevel models are more flexible regarding the requirements of data.
The data analysis approaches include spectral assessment, as well as descriptive, bivariate and multivariate methods.
However, multivariate methods also consider other attributes to determine the best cut points.
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