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Prediction of the degradability and ash content of wheat straw from different cultivars using near infrared spectroscopy

机译:近红外光谱法预测不同品种小麦秸秆的降解性和灰分

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摘要

Degradability of straw is important in connection with the fermentation process for bioethanol production, while the ash content is important for its suitability for incineration. Therefore, a fast method for assessment of straw quality could be very useful in determining the price and in helping choose between different applications for specific straw batches, such as fermentation for ethanol production, incineration or animal feed. This study investigated the ability of near infrared (NIR) spectroscopy to predict the degradability and ash content of 106 cultivars of wheat straw grown at two different sites. In general, calibrations based on NIR spectra recorded on air-dried samples performed better than those on oven-dried samples. A partial least squares (PLS) calibration based on the spectra of the air-dried samples predicted degradability with r po =0.72 and RMSECV =1.4% with 3 components using samples from the two different sites. The ash content was well predicted with r po =0.99 and RMSECV =0.195% using a complex 15-component PLS model validated using repeated random segmented cross-validation. However, this model proved to be sensitive to site in a validation using the two sites as segments, where the accuracy of ash content prediction decreased to r po =0.91 and RMSECV =0.691% using a 9-component PLS model. NIR spectroscopy proved useful for predicting degradability and ash content of wheat straw from different wheat cultivars. However, when developing predictive models of ash content based on NIR spectra, it should be ensured that the models are transferable to locations other than those used for model calibration.
机译:秸秆的可降解性与生产生物乙醇的发酵过程有关,而灰分对其焚化的适用性也很重要。因此,一种用于评估秸秆质量的快速方法对于确定价格以及帮助在特定秸秆批次的不同应用(例如用于乙醇生产,焚烧或动物饲料的发酵)的不同应用之间进行选择非常有用。这项研究调查了近红外(NIR)光谱预测在两个不同地点种植的106个小麦秸秆品种的可降解性和灰分含量的能力。通常,基于风干样品上记录的NIR光谱的校准效果要优于烘箱干燥样品。基于风干样品的光谱进行的偏最小二乘(PLS)校准可预测降解性,其中r po = 0.72和RMSECV = 1.4%,使用来自两个不同地点的样品的3种成分。使用复杂的15分量PLS模型(通过重复随机分段交叉验证验证),可以很好地预测灰分含量,其中r po = 0.99和RMSECV = 0.195%。但是,在使用两个部位作为分段的验证中,该模型证明对部位敏感,其中使用9分量PLS模型,灰分含量预测的准确性降低至r po = 0.91和RMSECV = 0.691%。 NIR光谱证明可用于预测不同小麦品种的麦秸的可降解性和灰分。但是,在基于NIR光谱开发灰分含量预测模型时,应确保该模型可转移到模型校准所用位置以外的其他位置。

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