Correction of non-linearities in spectroscopic multivariate calibration by using transformed original variables and PLS regression

The use of transformed original variables is proposed to correct non-linearities in the original data. The additional information that these new variables introduce can be linearly modelled by the PLS algorithm, giving better models than the ones obtained by using only the original variables. Non-informative absorbances can be eliminated later giving more stable models. Several artificial data sets have been studied to check the validity of the method in function of parameters such as degree of non-linearity, noise in X variables and noise in y variable. Three real data sets, with different degrees and sources of non-linearity, have been tested and the methodology proposed always yielded better results (lower rmsecv values or the same rmsecv values with lower complexity) than the PLS regression with original variables.