Comparative analysis of the performance of different approaches for the adaptation of a calibration model for diesel analysis

This work presents a comparative study of three model adaptations from an existing PLS model between two infrared spectrophotometers used in diesel analysis. Domain invariant partial least squares (di-PLS), dynamic orthogonal projection (DOP), and model updating (MU) were compared. The density of diesel was used as a case study. A set of 1156 diesel fuel samples was randomly split into a training set (766 samples) and a validation set (390 samples) of the PLS model. Also, a new set of 105 diesel samples was divided with the Kennard-Stone algorithm into a transferring set (15 samples) used to adapt the calibration model and a test set (90 samples) used to perform the external validation. The comparison was carried out considering the root mean square error of prediction (RMSEP) after model adaptation and the percentage of samples whose prediction error falls within the tolerance limits admitted by the reference method according to the ASTM-E-1655 standard. The results showed reductions of 80%, 87%, and 92% in RMSEP after applying di-PLS, DOP, and MU, respectively. However, di-PLS and DOP failed the test of agreement with the reference method since more than 5% of the validation samples had prediction errors beyond the admitted tolerance limits. In the case study, the results point to MU as the only valid model adaptation to be used in diesel analysis. These results are expected to be of value to the audience interested in the implementation of infrared spectroscopy in industrial applications.