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Provenance classification of nephrite jades using multivariate LIBS: a comparative study
Analytical Methods ( IF 2.7 ) Pub Date : 2017-11-29 00:00:00 , DOI: 10.1039/c7ay02643a Jianlong Yu 1, 2, 3, 4, 5 , Zongyu Hou 1, 2, 3, 4, 5 , Sahar Sheta 1, 2, 3, 4, 5 , Jian Dong 5, 6, 7, 8 , Wen Han 5, 9, 10 , Taijin Lu 5, 9, 10 , Zhe Wang 1, 2, 3, 4, 5
Analytical Methods ( IF 2.7 ) Pub Date : 2017-11-29 00:00:00 , DOI: 10.1039/c7ay02643a Jianlong Yu 1, 2, 3, 4, 5 , Zongyu Hou 1, 2, 3, 4, 5 , Sahar Sheta 1, 2, 3, 4, 5 , Jian Dong 5, 6, 7, 8 , Wen Han 5, 9, 10 , Taijin Lu 5, 9, 10 , Zhe Wang 1, 2, 3, 4, 5
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Provenance classification of nephrite jades is important since the unit price of jade changes drastically with its geological origin. In the present work, a detailed comparison between commonly applied multivariate methods is conducted to classify nephrite samples from five different locations via their laser-induced breakdown spectroscopy (LIBS) spectra. Five multivariate methods including principal component analysis (PCA), one-step and pairwise partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to provide the classification information on the samples. PCA was used for rough classification while the classification performance of the other four methods was discussed in detail. The results show a training accuracy of 89.0%, 99.2%, 99.8% and 100%, and a testing accuracy of 76.8%, 97.8%, 92.8% and 99.3% for the PLS-DA (one-step), PLS-DA (pairwise), LDA and SVM algorithms respectively. The superior model nature and the selection of suitable characteristic lines for weight differences led to the high performance of SVM, showing an excellent applicability for the provenance classification of nephrite jades using LIBS spectra.
中文翻译：
利用多元LIBS对软玉的种源分类：一项比较研究
软玉的种源分类很重要，因为玉的单价随其地质来源而急剧变化。在本工作中，对常用的多变量方法进行了详细的比较，以通过以下方法对来自五个不同位置的软玉样品进行分类。他们的激光诱导击穿光谱（LIBS）光谱。应用五种多元方法，包括主成分分析（PCA），单步和成对的偏最小二乘判别分析（PLS-DA），线性判别分析（LDA）和支持向量机（SVM），以提供有关分类的信息。样品。使用PCA进行粗分类，同时详细讨论了其他四种方法的分类性能。结果显示，针对PLS-DA（一步式），PLS-DA（ （成对），LDA和SVM算法。出色的模型特性和针对重量差异选择合适的特征线，带来了SVM的高性能，
更新日期：2017-11-29
中文翻译：
利用多元LIBS对软玉的种源分类：一项比较研究
软玉的种源分类很重要，因为玉的单价随其地质来源而急剧变化。在本工作中，对常用的多变量方法进行了详细的比较，以通过以下方法对来自五个不同位置的软玉样品进行分类。他们的激光诱导击穿光谱（LIBS）光谱。应用五种多元方法，包括主成分分析（PCA），单步和成对的偏最小二乘判别分析（PLS-DA），线性判别分析（LDA）和支持向量机（SVM），以提供有关分类的信息。样品。使用PCA进行粗分类，同时详细讨论了其他四种方法的分类性能。结果显示，针对PLS-DA（一步式），PLS-DA（ （成对），LDA和SVM算法。出色的模型特性和针对重量差异选择合适的特征线，带来了SVM的高性能，
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