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Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data

机译:紫外-可见和红外光谱数据的多嵌段处理对意大利藏红花(Crocus sativus L.)的地理分类

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摘要

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).
机译:通过红外和紫外-可见分光光度法对在四个不同的意大利地区(意大利中部的三个和南部的一个)收获的一百一十四个藏红花样品进行了调查。两种不同的多块策略分别使用顺序和正交偏最小二乘线性判别分析(SO-PLS-LDA)和顺序和正交协方差选择线性判别分析(SO-CovSel-LDA)来同时处理两个数据块,根据样品的地理来源对样品进行分类。两种多块方法均提供了非常令人满意的结果。对每种模型进行了研究,以了解哪些光谱变量对样品的辨别作用最大,即对四个不同区域收获的藏红花的表征起了最大作用。 SO-PLS-LDA提供了最准确的解决方案,该解决方案仅对31个以上的三个测试样品进行了错误分类(在外部验证中)。

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