The efficiency of the analytical methods based on vibrational spectroscopy has been widely verified in a high number of publications. In addition, it has been recognized that the pretreatment of the original signals is absolutely necessary to obtain enough quality in the subsequent classification and/or regression models. In fact, an inappropriate pretreatment makes the results worse. It is also impossible to give “a priori” rules that guarantee the adequacy of a pretreatment for specific data.
The effect of the pretreatments is evaluated through their impact on the quality of the classification and/or regression models built from them due to the double dependence (on the data and on the purpose of the analysis). The effect of the pretreatment has been evaluated using partial least squares regression (PLSR) in some works and the root mean squares in prediction or in cross-validation has been always used as a criterion to evaluate the regression in all these cases. However, it seems appropriate to use quality criteria of the calibration of the analytical method through the figures of merit: the significance of the regression, the absence of constant or proportional bias, the residual standard deviation, the mean of the absolute values of the relative errors and the capability of detection.
In this work, the use of these analytical criteria in a desirability function is proposed for the first time with calibration data of oxybenzone obtained by ATR-FTIR and PLSR. This desirability function enables to choose the best pretreatment among the 39 possibilities studied. In addition, it is shown that the same optimum is not obtained if the minimum of RMSEC_CV is considered as a criterion.
