Non-invasive in vivo measurement of abdominal fat in broiler breeder pullets with portable Near Infrared technology

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ID: 315299
2026
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Ranked #188 of 221 articles by views in italian journal of animal science

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Abstract
Abstract In broiler breeder production, adequate body fat at the start of the laying period is considered crucial for maintaining reproductive efficiency. Currently, methods to measure body fat in these animals are either invasive, requiring euthanasia, or depend on the subjective skill of operators during pelvic bone palpation, limiting their practicality and accuracy. Therefore, this study aimed to evaluate the potential of two portables Near Infrared Spectroscopy (NIRS) devices with different optical configurations, weight, and price–the MicroNIRTM Pro 1700 (908—1676 nm) and the SCiO (740 -1070 nm) — for their ability to predict abdominal fat as percentage of body weight in live pullet broiler breeders. A total of 285 pullets from three different strains were analysed, collecting reflectance spectra at two anatomical regions (axillary and coccygeal). Modified partial least squares (MPLS) regression models were developed and evaluated using cross-validation and independent validation for accuracy. Models based on coccygeal spectra showed superior performance for both instruments. Among the evaluated models, the MicroNIR device applied to the coccyx region provided the best performance, with an r2cv of 0.80 and a SECV of 0.19. In independent validation for accuracy, this model achieved an r2 of 0.77 and a SEP of 0.20, confirming its predictive capability. These results suggest that NIRS technology, combined with appropriate anatomical selection and chemometric modelling, offers a non-destructive and easy-to-use solution to improve in vivo assessments in the poultry industry.
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openalex_W7162751876 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors D Pérez-Marín, J A Entrenas, I Torres-Rodríguez, M Garrido-Cuevas, J C Abad
Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag173
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