Multivariate data treatments (multiple regression models, variable ordination by multidimensional scaling, and discriminant analysis) were useful to identify the soil characteristics most significantly associated to its water repellency.
A rapid analytical methodology based on recording UV Vis spectra during the photoelectro-Fenton/photocatalytic decolorization process and the data treatment using multivariate calibration methods such as partial least square (PLS) was developed.
Several univariate and multivariate statistical techniques were employed for data treatment.
Artificial neural network (ANN) was applied for data treatment as a multivariate calibration tool in a potentiometric acid base flow injection titration.
The third strength is related to the data treatment itself where multivariate regression analyses were used stratified by age group (0 14 and ≥15 years) defined based on the cut-off seen for the observed distribution of individuals with confirmed AH1N1 PCR-diagnosis.
The sPCA-rSVD provides a uniform treatment of both classical multivariate data and high-dimension-low-sample-size (HDLSS) data.
It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis.
However, the spectral interpretation and treatment of this kind of multivariate data remains difficult without the use of chemometrics.
Serum samples were analyzed using gas chromatography-mass spectrometry (GC MS) followed by multivariate data analysis to classify samples and determine if such treatments can help revert obesity related metabolic changes back to normal status.
