首页> 外文期刊>Applied Spectroscopy: Society for Applied Spectroscopy >Near-Infrared Spectroscopy Calibrations Performed on Oven-Dried Green Forages for the Prediction of Chemical Composition and Nutritive Value of Preserved Forage for Ruminants
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Near-Infrared Spectroscopy Calibrations Performed on Oven-Dried Green Forages for the Prediction of Chemical Composition and Nutritive Value of Preserved Forage for Ruminants

机译:在烘箱干燥的绿色饲料上进行近红外光谱校准,用于预测反刍动物的化学成分和保存饲料的营养价值

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Predicting forage feed value is a vital part of estimating ruminant performances. Most near-infrared (NIR) reflectance calibration models have been developed on oven-dried green forages, but preserved forages such as hays or silages are a significant part of real-world farm practice. Fresh and preserved forages give largely similar fodder, but drying or ensiling processes could modify preserved forage spectra which would make the oven-dried green forage model unsuitable to use on preserved forage samples. The aim of this study was to monitor the performance of oven-dried green forage calibration models on a set of hay and silage to predict their nutritive value. Local and global approaches were tested and 1025 green permanent grassland forages, 46 types of hay, and 27 types of silage were used. The samples were scanned by NIR spectroscopy and analyzed for nitrogen, neutral detergent fiber, acid detergent fiber, and pepsin-cellulase dry matter digestibility (PCDMD). Local and global calibrations were developed on 975 oven-dried green forage spectra and tested on 50 samples of oven-dried green forages, 46 samples of hay, and 27 samples of silage. For oven-dried green forage and hay validation sets, Mahalanobis distance (H) between these samples and the calibration population center was lower than 3. No significant standard error of prediction differences was obtained when calibration models were applied to oven-dried green forage and hay validation sets. For silage, the H-distance was higher than 3, meaning that calibration models built from oven-dried green forages cannot be applied to silage samples. We conclude that local calibration outperforms global strategy on predicting the PCDMD of oven-dried green forages and hay.
机译:预测饲料馈送值是估计反刍动物性能的重要组成部分。大多数近红外(NIR)反射型校准模型已经在烘箱干绿色饲料上开发,但保存的饲料如海盗或青贮饲料是现实世界农业实践的重要组成部分。新鲜和保存的饲料在很大程度上具有相似的饲料,但干燥或禁止过程可以改变保存的饲料光谱,这将使烘箱干燥的绿色饲料模型不适用于保存的饲料样品。本研究的目的是监测在一组干草和青贮饲料上的烤箱干绿色牧草校准模型,以预测其营养价值。测试本地和全局方法,使用1025种绿色永久性草地饲料,46种干草类型和27种青贮饲料。通过NIR光谱扫描样品,并分析氮气,中性洗涤剂纤维,酸性洗涤剂纤维和胃纤维素酶干物质消化率(PCDMD)。在975个烘箱干绿色牧草谱上开发了本地和全局校准,并在50个烘箱干绿色饲料,46个样品上进行测试,干草样品和27个青贮样。对于烤箱干绿色牧草和干草验证套,这些样品与校准人口中心之间的mahalanobis距离(h)低于3.当校准模型应用于烘箱干燥的绿色饲料时,没有获得预测差异的显着标准误差干草验证集。对于青贮饲料,H距离高于3,这意味着由烘箱干绿色饲料构建的校准模型不能应用于青贮样品。我们得出结论,局部校准优于预测烘箱干绿色饲料和干草PCDMD的全球战略。

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