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Assessing biochar stability indices using near infrared spectroscopy

机译:使用近红外光谱法评估生物炭稳定性指标

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摘要

New and rapid techniques for estimating the stable fraction of organic carbon (C-org) in biochar are needed for carbon (C) sequestration accounting. In this study, 25 biochar samples produced from different feedstocks and pyrolysis temperatures were scanned using visible near infrared (NIR) spectroscopy and analysed using standard laboratory methods for reference data. Principal component analysis and linear discriminant analysis of preprocessed spectral data were used to extract relevant information and discriminate among biochars, respectively. Partial least squares regression was used to build calibration models between preprocessed spectral data and reference data, and the accuracy of calibration models in predicting biochar properties was then tested using a cross-validation procedure. Biochar indices related to C stability, such as aromatic C, fixed C, atomic H/C-org ratio and the fraction of total C that is aromatic (fa) were successfully predicted (R-CV(2) 0.92-0.94, RPDCV 3.26-4.22) using the NIR spectroscopy technique (NIR bands, 780-2500 nm). Aromatic C and fixed C could be predicted independently from fa. Other biochar properties, such as C-org, H and O content, and atomic O/C-org ratio, were predicted with an accuracy ranging from moderate to very high (R-CV(2) 0.80-0.96, RPDCV 2.24-4.66). The study illustrates the potential of this rapid and low-cost technique for measuring biochar stability indices in routine analysis if accurate calibration models for each index are available.
机译:需要新的快速技术来估算生物碳中稳定的有机碳含量(C-org),以进行碳(C)的固存核算。在这项研究中,使用可见近红外(NIR)光谱扫描了由不同原料和热解温度产生的25种生物炭样品,并使用标准实验室方法进行了分析以作为参考数据。预处理光谱数据的主成分分析和线性判别分析分别用于提取相关信息并区分生物炭。使用偏最小二乘回归建立预处理光谱数据和参考数据之间的校准模型,然后使用交叉验证程序测试校准模型在预测生物炭特性中的准确性。已成功预测了与碳稳定性有关的生物炭指数,例如芳族C,固定C,原子H / C-org比和总C占芳族(fa)的比例(R-CV(2)0.92-0.94,RPDCV 3.26 -4.22)使用NIR光谱技术(NIR波段,780-2500 nm)。芳香C和固定C可以独立于fa预测。预测了其他生物炭特性,例如C-org,H和O含量以及原子O / C-org比,其准确性范围从中等到非常高(R-CV(2)0.80-0.96,RPDCV 2.24-4.66 )。这项研究表明,如果可获得每种指标的准确校准模型,那么这种快速低成本的技术在常规分析中测量生物炭稳定性指标的潜力。

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