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

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