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NEAR INFRARED SPECTROSCOPY – AN ALTERNATIVE TO DETERMINE THE CRUDE FIBER CONTENT OF FORAGES

机译:近红外光谱法–一种测定粗饲料粗纤维含量的替代方法

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In our days NIR spectroscopy represent a promising alternative to the chemical methods for crude fiber contents of forages. The main objective of this study was to obtain a NIR calibration model for prediction this parameter of forages harvested in June 2009 from hill permanent grassland (Gr?dinari, Cara?-Severin District). The experimental field was organized in ten experimental trials fertilized organic, mineral, and organo-mineral. The floristic composition of forages from this period was determined gravimetrically. From Poaceae were present Festuca rupicola and Calamagrostis epigejos. Fabaceae family was represented by Trifolium repens and Lathyrus pratensis. From other botanical family: Rosa canina, Filipendula vulgaris, Galium verum and Inula britanica. Like input data for NIR calibration were used the results for this qualitative parameter by chemical method and the reflectance values from 150 NIR spectra for all analysed samples. Partial last square (PLS) regression was used to obtain the “NIR - Total Fiber” model, implemented in Panorama program (version 3, LabCognition, 2009). The statistical parameters (R 2 =0.80; RMSEC=2.73) and the differences between references and predicted values situated in range 0.03 and 9.24% suggest a medium quality of calibration model, but it is promising to use it to predict the crude fiber contents of forages from grassland in this period of year using higher number of samples for calibration.
机译:在当今的时代,近红外光谱法代表了一种化学方法,可替代草料中粗纤维的化学方法。这项研究的主要目的是获得一个NIR校准模型,以预测2009年6月从丘陵永久性草地(格拉迪纳里,卡拉·塞韦林区)收获的草料的这一参数。在十个有机,矿物和有机矿物受精的试验试验中组织了该试验领域。用重量分析法确定了这一时期草料的植物学组成。从禾本科(Paceae)中出现了小花金莲花(Festuca rupicola)和and蒲(Calamagrostis epigejos)。车前草科以白三叶和山thy豆为代表。来自其他植物家族:蔷薇蔷薇,寻常型菲律宾金枪鱼,大蒜和菊苣。像用于NIR校准的输入数据一样,通过化学方法使用该定性参数的结果,并使用所有分析样品的150 NIR光谱的反射率值。使用部分最后平方(PLS)回归获得“ NIR-总纤维”模型,该模型在Panorama计划(版本3,LabCognition,2009)中实现。统计参数(R 2 = 0.80; RMSEC = 2.73)以及参考值和预测值之间的差异(范围为0.03和9.24%)表明校准模型的质量中等,但有望将其用于预测纤维的粗纤维含量一年中这段时间内从草地上采集的草料,使用了更多的样本进行校准。

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