Second-order advantage when applying different artificial neural networks after unfolded principal component analysis/residual bilinearization

GOICOECHEA, HÉCTOR C; GARCIA-REIRIZ; PATRICIA DAMIANI,; CULZONI MARÍA JULIA; ALEJANDRO OLIVIERI,

Montpellier, Francia

Congreso; CAC2008; 2008

When processed by adequate algorithms, second-order instrumental data allow analysts to obtain the useful second-order advantage, a property with immense implications in the analysis of real samples of complex composition [4]. Recently, attention has been focused on the possibility of extracting the second-order advantage from non-linear second-order information. This requires the coupling of two separate methods, which are able to accomplish the following successive tasks: 1) model the calibration and test data so that the contribution of unexpected components, not present in the calibration set, is removed from the test sample, and 2) model the non-linear relationship between calibration data and analyte concentration, and interpolating the pre-processed test data for prediction purposes