Selection of reference samples for updating multivariate calibration models used in the analysis of pig faeces

Abstract

Monitoring and updating calibration models are common tasks when analytical methods are based on nearinfrared spectroscopy. This work describes a situation in which a PLS calibration model that is used routinely for the determination of phosphorus content in pig faeces in digestibility studies had to be updated in order to be used with the faeces collected in a new trial with phytases. An approach based on D-optimality is presented that selects a reduced number of the new samples to be analyzed with the reference analytical method so that the small set is used to confirm the need to update the model and validate it. The rest of the new samples that had not been selected by the algorithm were accurately predicted with the updated model. The updated model maintained its previous performance for the samples in the validation set (an RMSEP of 1.58 g kg− 1 compared with an RMSEP of 1.54 g kg− 1 before the update) and the prediction error for the new samples was RMSECV = 1.95 g kg− 1, much lower than the RMSEP = 11.38 g kg− 1 obtained before the model update. In addition, the predictive ability of the updated PLS model was significantly better than updated models selecting the reduced dataset using other sample selection methods such as Kennard-Stone, a leverage-based selection method and random selection.