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Prediction of soil cation exchange capacity using visible and near infrared spectroscopy

机译:可见和近红外光谱法预测土壤阳离子交换量

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This study was undertaken to investigate the application of visible and near infrared (vis -NIR) spectroscopy for determining soil cation exchange capacity (CEC) under laboratory and on-line field conditions. Measurements were conducted in two fields with clay texture in field 1 (F1) and clay-loam texture in field 2 (F2) both in Turkey. Partial least squares (PLS) regression analyses with full cross-validation were carried out to establish CEC models using three datasets of F1, F2 and F1 + F2. Analytically-measured, laboratory vis-NIR and on-line vis-NIR predicted maps were produced and compared statistically by kappa coefficient. Results of the CEC prediction using laboratory vis-NIR data gave good prediction results, with averaged r(2) values of 0.92 and 0.72, root mean squared errors of prediction (RMSEP) of 1.89 and 1.54 cmol kg(-1) and residual prediction deviations (RPD) of 3.69 and 1.89 for F1 and F2, respectively. Less successful predictions were obtained for the on-line measurement with r(2) of 0.75 and 0.7, RMSEP of 4.79 and 1.76 cmol kg(-1) and RPD of 1.45 and 1.56 for F1 and F2, respectively. Comparisons using kappa statistics test indicated a significant agreement (kappa = 0.69) between analytically-measured and laboratory vis-NIR predicted CEC maps of F1, while poorer agreement was found for F2 (kappa = 0.43). A moderate spatial similarity was also found between analytically-measured and on-line vis-NIR predicted CEC maps in F1 (kappa = 0.50) and F2 (kappa = 0.49). This study suggests that soil CEC can be satisfactorily analysed using vis-NIR spectroscopy under laboratory conditions and with somewhat less precision under on-line scanning conditions. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
机译:进行这项研究是为了研究可见光和近红外(vis -NIR)光谱法在实验室和在线现场条件下测定土壤阳离子交换能力(CEC)的应用。分别在土耳其的两个场中进行了测量,第一场(F1)为粘土质地,第二场(F2)为粘土壤土质地。使用F1,F2和F1 + F2的三个数据集进行具有完全交叉验证的偏最小二乘(PLS)回归分析,以建立CEC模型。生成分析测量的实验室vis-NIR和在线vis-NIR预测图,并通过kappa系数进行统计比较。使用实验室vis-NIR数据进行的CEC预测结果给出了良好的预测结果,平均r(2)值为0.92和0.72,预测的均方根误差(RMSEP)为1.89和1.54 cmol kg(-1),并且剩余预测为F1和F2的偏差(RPD)分别为3.69和1.89。对于F1和F2,r(2)分别为0.75和0.7,RMSEP为4.79和1.76 cmol kg(-1)以及RPD为1.45和1.56的在线测量,获得的预测不太成功。使用kappa统计检验进行的比较表明,F1的分析测量值和实验室vis-NIR预测的CEC图之间存在显着一致性(kappa = 0.69),而F2的一致性较差(kappa = 0.43)。在F1(kappa = 0.50)和F2(kappa = 0.49)中,分析测量和在线vis-NIR预测的CEC映射之间也发现了适度的空间相似性。这项研究表明,在实验室条件下使用可见近红外光谱可以令人满意地分析土壤CEC,而在在线扫描条件下,其准确度稍差一些。 (C)2016年。由Elsevier Ltd.出版。保留所有权利。

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