In this study, four multivariate techniques (principal components regression, PCR; partial least squares regression, PLSR; back-propagation neural network, BPNN; and support vector machine regression, SVMR) were compared with the aim of rapidly and accurately predicting soil properties, including soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and total potassium (TK).
Among multivariate techniques, Principal Component Analysis (PCA) is often used as an exploratory tool to identify the major sources of air pollutant emissions [38, 41 43].
Cunha and Wiendahl (2005) have proposed an evaluation method based on the use of multivariate techniques: principal component analysis (PCA) and cluster analysis (CA) to improve the effectiveness of evaluation and decision making, monitoring and manufacturing control.
To extract trends from within the complex ToF-SIMS dataset, a multivariate analysis technique, principal component analysis (PCA), was employed.
The data was analysed using multivariate statistical technique (principal component analysis) to identify the hydrogeochemical processes which result in the variations in the chemical composition of groundwater.
A multivariate statistical technique, Principal component analysis (PCA) (Ammer et al. 2006; Salome et al. 2011) was performed to analyze the relationship of plant growth parameters, fruiting properties, earthworm density and biomass with the soil chemical properties.
The two networks extracted in this work could be compared to those commonly extracted by many groups in the decomposition of fMRI/PET resting state signals, using blind data-driven multivariate techniques as principal component analysis, independent component analysis, subprofile scaling model, fuzzy clustering [35, 39, 40].
Multivariate techniques like principal component and factor analyses utilise dimension reduction to facilitate the identification of uncorrelated subgroups of variables (i.e. principal components and factors).
It is based on statistical multivariate analysis techniques, Principal Components Analysis and Cluster Analysis.
For the present study, farm household typologies were identified by using two sequential multivariate statistical techniques: principal component analysis (PCA) and cluster analysis (CA) (Ding and He [2004]).
