Tej Oussama, Albanell Elena, Kaikat Ibtissam, Manuelian Carmen L
Group of Ruminant Research (G2R), Department of Animal and Food Science, Autonomous University of Barcelona (UAB), 08193 Bellaterra, Spain.
Animal Nutrition Program, Institute of Agrifood Research and Technology (IRTA), 43120 Constantí, Spain.
Animals (Basel). 2025 Jul 24;15(15):2181. doi: 10.3390/ani15152181.
This study evaluated fecal near-infrared spectroscopy (fNIRS) potential to predict three external markers (Yb, Ti, and polyethylene glycol (PEG)) and dry matter digestibility (DMD) calculated from these markers and fiber fractions. A total of 192 fecal samples were collected from 576 Ross 308 male chicks supplemented with TiO (2 g/kg), YbO (50 mg/kg), and PEG (5 g/kg) for 8 d. Reference values for Ti and Yb were obtained using an inductively coupled plasma-optical emission spectrometer, for fiber fractions via ANKOM, and for PEG content using an ad hoc fNIRS model. Prediction models were developed in external validation with 25% of the samples. Good and fair prediction models were built for Ti and Yb, respectively, and considered adequate for rough screening. The DMD models based on Yb and ADF were unreliable, whereas the model based on Ti was suitable for rough screening. The PEG prediction model built during the adaptation period performed exceptionally well; however, the DMD prediction based on PEG highlighted limitations due to diet differences during both the adaptation and experimental periods. In conclusion, fNIRS shows promise for screening Ti and Yb fecal content and DMD using Ti. However, tailored PEG prediction equations need to be developed for each specific diet.
本研究评估了粪便近红外光谱法(fNIRS)预测三种外部标志物(镱(Yb)、钛(Ti)和聚乙二醇(PEG))以及根据这些标志物和纤维组分计算得出的干物质消化率(DMD)的潜力。从576只补充了TiO(2 g/kg)、YbO(50 mg/kg)和PEG(5 g/kg)达8天的罗斯308雄性雏鸡中总共收集了192份粪便样本。使用电感耦合等离子体发射光谱仪获得Ti和Yb的参考值,通过ANKOM获得纤维组分的参考值,并使用专门的fNIRS模型获得PEG含量的参考值。利用25%的样本进行外部验证来建立预测模型。分别为Ti和Yb建立了良好和尚可的预测模型,认为适用于粗略筛选。基于Yb和酸性洗涤纤维(ADF)的DMD模型不可靠,而基于Ti的模型适用于粗略筛选。在适应期建立的PEG预测模型表现非常出色;然而,基于PEG的DMD预测突出显示了在适应期和实验期由于饮食差异导致的局限性。总之,fNIRS在筛选Ti和Yb粪便含量以及利用Ti预测DMD方面显示出前景。然而,需要针对每种特定饮食制定PEG预测方程。