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Chemometric analysis of Mesoamerican obsidian sources

机译:中美洲黑曜石来源的化学计量分析

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We propose a combination of portable X-ray fluorescence (pXRF) and chemometrics to discriminate between Mesoamerican obsidian sources and to assign archaeological artifacts of unknown origin to their respective deposits using a procedure that does not require any type of calibration or reference standards. A set of 109 samples of known origin and a total of 257 samples of unknown origin were analyzed with a portable XRF spectrometer. The resultant spectra were used as spectral signatures for the chemometric data analysis. First, we applied spectral pre-treatment techniques, such as CluPA algorithm for peak alignment and the Savitzky-Golay and Extended Multiplicative Signal Correction for data smoothing and noise removal, combined with methods for the selection of a spectral range containing the variables with the most relevant information (iPLS and). The full spectrum of the obsidian samples was divided into 20 subintervals, fitting a local regression model (PLS) to each subinterval. The performance was evaluated by the Root Mean Square Error of Cross-Validation, the Root Mean Square Error of Prediction and the correlation coefficient. The selected spectral regions were then analyzed with ROBPCA algorithm for the discrimination of outliers and the projection of the observations in the PCA space. For the classification, we propose a robust procedure (RSIMCA) which is based on a ROBPCA method for high-dimensional data. The classification rules were obtained by using the orthogonal and the score distances, from which it is possible to distinguish samples that belong to a given group. Using the proposed methodology, we were able to provide evidence about which variables were meaningful for the classification and provided information about group membership or provenance. This approach proves to be a valid technique for the quantitative analysis of XRF spectra.
机译:我们建议结合便携式X射线荧光(pXRF)和化学计量学,以区分中美洲黑曜石源,并使用不需要任何类型的校准或参考标准的程序将未知来源的考古文物分配给它们各自的沉积物。使用便携式XRF光谱仪分析了109个已知来源的样品和257个未知来源的样品。所得光谱用作化学计量数据分析的光谱特征。首先,我们应用了光谱预处理技术,例如用于峰对齐的CluPA算法和用于数据平滑和噪声去除的Savitzky-Golay和扩展乘性信号校正,并结合了选择包含最大变量的光谱范围的方法相关信息(iPLS和)。黑曜石样品的整个光谱被分为20个子区间,每个子区间都拟合了局部回归模型(PLS)。通过交叉验证的均方根误差,预测的均方根误差和相关系数来评估性能。然后使用ROBPCA算法分析选定的光谱区域,以区分异常值和在PCA空间中观测值的投影。对于分类,我们提出了基于ROBPCA方法的高维数据鲁棒过程(RSIMCA)。通过使用正交和得分距离获得分类规则,从中可以区分属于给定组的样本。使用提出的方法,我们能够提供证据说明哪些变量对分类有意义,并提供有关组成员身份或出处的信息。这种方法被证明是对XRF光谱进行定量分析的有效技术。

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