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Real-Time Analysis of soil Total N and P with Near Infrared Reflectance Spectroscopy

机译:近红外反射光谱法实时分析近红外反射光谱法

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The tide salt clay in Zhejiang Province was selected as research object, and the theories of analyzing soil N and soil P with NIR spectroscopic techniques were explored. Six group samples were collected from a rice farm, after taking the samples to the lab, we added nutritional water of different height to the six groups, and then drying, rubbing. At last, one hundred twenty samples were got from six groups equably. Different preprocessing methods were carried on the spectrum data, such as standard normal variate (SNV), multivariate scatter correction (MSC) and smoothing of moving average. Different calibration models were established and the performance of these models were compared with different preprocessing methods. After comparison, smoothing of moving average was found to be the most appropriate spectral preprocessing method. 96 samples were randomly selected from 120 samples to be the calibration set, the remaining 24 samples were used as validation samples. Two discriminant analysis models were developed using partial least squares (PLS) method and least squares-support vector machine (LS-SVM) method respectively. The performances were validated by the samples in the validation set. The correlation coefficients (r) between the real values and predicted ones by discriminant analysis models using PLS were 0.9454(N)、0.9327(P) respectively, and using LS-SVM were 0.9503(N)、0.9547(P) respectively. The root mean standard error of prediction (RMSEP) were 0.9503(N)、0.9547(P) by PLS, 0.0378(N)、0.0101(P) by LS-SVM. The results showed that NIRS could be used to evaluate the soil N and soil P.
机译:探讨了浙江省潮盐黏土作为研究对象,探讨了用NIR光谱技术分析土壤N和土壤p的理论。从稻米农场收集六组样品,在将样品中取出实验室后,我们将不同高度的营养水添加到六组,然后干燥,摩擦。最后,一百二十个样本从六个组得出。在频谱数据上携带不同的预处理方法,例如标准正常变化(SNV),多变量散射校正(MSC)和移动平均平均平滑。建立了不同的校准模型,并将这些模型的性能与不同的预处理方法进行了比较。比较后,发现移动平均水平的平滑是最合适的光谱预处理方法。 96样品从120个样品随机选择待校准组,剩余的24个样品用作验证样品。使用部分最小二乘(PLS)方法和最小二乘 - 支持向量机(LS-SVM)方法开发了两个判别分析模型。验证集中的样本验证了性能。使用PLS的判别分析模型的实际值与预测的相关系数(R)分别为0.9454(n),0.9327(p),分别使用LS-SVM为0.9503(N),0.9547(P)。预测(RMSEP)的根平均标准误差由LS-SVM的PLS,0.0378(N),0.0101(P)为0.9503(N),0.9547(P)。结果表明,NIR可用于评估土壤N和土壤P.

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