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

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.info:eu-repo/semantics/publishedVersio

Use of visible-near infrared spectroscopy to predict nutrient composition of poultry excreta

Nowadays optimal feed formulation for poultry is sought for available content, which takes into account how the nutrients are digested and metabolized by the animal. The digestibility coefficients of the nutrients are usually obtained in in vivo trials that require feeding the birds with different diets of well-known composition and analyzing a large number of excreta samples. Nutrient excreta composition is usually found by wet analytical methods. This work presents visible-near infrared (Vis-NIR) calibrations for organic matter, protein, fat, gross energy, uric acid and phosphorus in excreta from bioassays involving broiler chickens, laying hens and broiler turkeys carried out between 2017 and 2020. The Vis-NIR spectra (400–2499.5 nm) were pretreated by generalized least squares weighting (GLSW) and partial least squares regression (PLSR) was used to obtain the prediction models. The six parameters were properly predicted with the values of ratio of performance of deviation (RPD) and coefficient of determination of prediction (R2p) of the validation set ranging from 3.7 to 4.6 and from 0.91 to 0.95 respectively. All but one of the calibrations passed the statistical tests for fit for purpose described in ISO 12099:2017. Despite the global calibrations provided satisfactory results, specific calibrations for broiler chicken excreta and for laying hen excreta were developed to check if their predictions could be even better but the results did not improve. Finally, the root mean square error of prediction (RMSEP) of the global calibrations was compared with the standard error of the reference methods employed for the analysis of these parameters, confirming their high performance and direct applicability.info:eu-repo/semantics/publishedVersio

Uncertainty in aflatoxin B1 analysis using information from proficiency tests

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