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Determination of ash content in solid cattle manure with visible near-infrared diffuse reflectance spectroscopy.

机译:可见近红外漫反射光谱法测定固体牛粪中的灰分。

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摘要

In this study, we explored the feasibility of visible and near-infrared diffuse reflectance spectroscopy (VisNIR-DRS) for the rapid prediction of ash content in solid manure from beef feedyards in the southern High Plains (USA). Proportionally mixed samples of soil and manure (n=201) were evaluated for ash content by conventional analysis and then used to calibrate a statistical model for prediction of ash content by VisNIR-DRS based on multivariate partial-least squares regression and random test-set validation. Two thirds of the samples were randomly selected to build a calibration model, and the remaining third was used for validation. The coefficient of determination (r2), root mean squared deviation (RMSD), and ratio of prediction to standard deviation (RPD) were calculated to assess the prediction model. The prediction model had an r2 of 0.94, an RMSD of 5% ash (dry basis, d.b.), and an RPD of 4. The VisNIR-DRS model successfully predicted crude ash content within +or-5% of the observed ash content (d.b.) as determined by dry oxidation using the accepted ASTM standard E1755-01.
机译:在这项研究中,我们探索了可见光和近红外漫反射光谱(VisNIR-DRS)用于快速预测美国南部高平原地区牛肉饲养场固体粪肥中灰分含量的可行性。通过常规分析评估按比例混合的土壤和粪便样品(n = 201)的灰分,然后基于多元偏最小二乘回归和随机检验集,使用VisNIR-DRS校准用于校准灰分预测的统计模型验证。随机选择三分之二的样本建立校准模型,其余三分之一用于验证。计算确定系数(r 2 ),均方根偏差(RMSD)和预测与标准偏差之比(RPD)以评估预测模型。该预测模型的r 2 为0.94,RMSD为5%灰分(干基,db),RPD为4。VisNIR-DRS模型成功地预测了+或-范围内的粗灰分。使用公认的ASTM标准E1755-01通过干法氧化测得的灰分含量(db)的5%。

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