Aarhus University, Department of Bio-systems Engineering, Blichers Allé 20, DK 8830, Tjele, Denmark.
Bioresour Technol. 2011 Sep;102(17):7835-9. doi: 10.1016/j.biortech.2011.05.049. Epub 2011 May 27.
This paper investigates near infra-red spectroscopy (NIRS) as an indirect and rapid method to assess the biochemical methane potential (BMP) of meadow grasses. Additionally analytical methods usually associated with forage analysis, namely, the neutral detergent fibre assay (NDF), and the in-vitro organic matter digestibility assay (IVOMD), were also tested on the meadow grass samples and the applicability of the models in predicting the BMP was studied. Based on these, regression models were obtained using the partial least squares (PLS) method. Various data pre-treatments were also applied to improve the models. Compared to the models based on the NDF and IVOMD predictions of BMP, the model based on the NIRS prediction of BMP gave the best results. This model, with data pre-processed by the mean normalisation method, had an R(2) value of 0.69, a root mean square error of prediction (RMSEP) of 37.4 and a residual prediction deviation (RPD) of 1.75.
本文研究了近红外光谱(NIRS)作为一种间接且快速的方法,用于评估草地植物的生物甲烷潜力(BMP)。此外,还对草地植物样本进行了通常与饲料分析相关的分析方法测试,即中性洗涤剂纤维测定法(NDF)和体外有机物消化率测定法(IVOMD),并研究了这些模型在预测 BMP 方面的适用性。在此基础上,使用偏最小二乘法(PLS)方法获得了回归模型。还应用了各种数据预处理方法来改进模型。与基于 NDF 和 IVOMD 预测 BMP 的模型相比,基于 NIRS 预测 BMP 的模型给出了最佳结果。该模型采用均值归一化方法进行数据预处理,具有 0.69 的 R²值、37.4 的预测均方根误差(RMSEP)和 1.75 的残差预测偏差(RPD)。