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首页> 外文期刊>Spanish Journal of Agricultural Research >Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value
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Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value

机译:粪便近红外反射率光谱(NIRS)预测估算热量值的适用性

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摘要

A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples.
机译:从不同实验试验中收集的共有220个粪便猪和家禽样品,目的是展示近红外反射光谱(NIRS)技术在能量余额研究中的产量产品估算粪便粗糙度值的适用性。使用FOSS NIRSYSTEM 6500仪器分析来自干燥和接地粪便样品的NIR光谱,扫描波长范围400-2500nm。进行定量分析模型的验证研究以估计与参考值相关的方法性能的相关性,以获得适当,准确和精度。对于个体物种获得的NIRS校准的预测结果表明,对于家禽和猪来说,比较化学分析和NIRS预测的高相关系数,范围为0.92至0.97。对于外部验证,交叉验证(SECV)标准误差与预测标准误差(SEP)的标准误差分别在0.73和0.86之间为禽类和猪而变化,表明校准的足够精度。此外,在两个物种中估算GCV的全局模型是开发的,并在外部验证的情况下开发出来。它显示出校准0.99的相关系数,交叉验证为0.98,外部验证为0.97。最后,对于NIRS为NIRS全球预测的单个NIRS物种模型应用了最终价值,NIRS开发了具有最终价值的预测模型的相对不确定性。本研究表明,NIRS是一种适当且准确的方法,用于测定粪便中的GCV,降低成本,永恒,方便地处理令人不快的样品。

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