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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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Visible and near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is increasingly being used to quantify constituents of organic matter both in the lab and in situ. However, it is unknown if DRS can be utilized as a tool for determining crude ash content of solid cattle manure. Ash content is a significant contributor to the suitability and value of manure for use both as a biofuel and soil fertilizer, but conventional ash analysis is time-consuming and labor-intensive. In this study, we explored the feasibility of VisNIR-DRS for the rapid prediction of ash content in solid manure from beef feedyards in the southern High Plains. 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 (r 2 ), 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 r 2 of 0.94, an RMSD of 5% ash (d.b.), and an RPD of 4. The VisNIR-DRS model successfully predicted crude ash content within ± 5% of the observed ash content (d.b.) as determined by dry oxidation using the accepted ASTM standard E1755-01
机译:可见光和近红外(VisNIR,350-2500 nm)漫反射光谱(DRS)越来越多地用于实验室和现场量化有机物的成分。但是,尚不清楚是否可以将DRS用作确定固体牛粪中粗灰分含量的工具。灰分含量是粪肥适合用作生物燃料和土壤肥料的重要因素,但常规灰分分析既费时又费力。在这项研究中,我们探索了VisNIR-DRS用于快速预测南部高原平原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,然后将其用于校准VisNIR-DRS基于多元偏最小二乘回归和随机检验集预测灰分的统计模型验证。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(db),RPD为4。VisNIR-DRS模型成功地预测了粗灰分在观测值的±5%范围内灰分(db),采用公认的ASTM标准E1755-01通过干法氧化确定

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