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Non-destructive distinction between geogenic and anthropogenic calcite by Raman spectroscopy combined with machine learning workflow
Analyst ( IF 3.6 ) Pub Date : 2023-05-30 , DOI: 10.1039/d3an00441d Sara Calandra 1, 2 , Claudia Conti 3 , Irene Centauro 1 , Emma Cantisani 4
Analyst ( IF 3.6 ) Pub Date : 2023-05-30 , DOI: 10.1039/d3an00441d Sara Calandra 1, 2 , Claudia Conti 3 , Irene Centauro 1 , Emma Cantisani 4
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Here, we demonstrate, for the first time, the possibility of distinguishing between geogenic and anthropogenic calcite in a non-destructive and effective way. Geogenic calcite derives from natural sedimentary and metamorphic rocks whereas anthropogenic calcite is formed artificially due to the carbonation process in mortars and plaster lime binders. Currently, their distinction is a major unaddressed issue although it is crucial across several fields such as 14C dating of historical mortars to avoid contamination with carbonate aggregates, investigating the origins of pigments, and studying the origins of sediments, to name a few. In this paper, we address this unmet need combining high-resolution micro-Raman spectroscopy with data mining and machine learning methods. This approach provides an effective means of obtaining robust and representative Raman datasets from which samples’ origins can be effectively deduced; moreover, a distinction between sedimentary and metamorphic calcite has been also highlighted. The samples, chemically identical, exhibit systematic and reliable differences in Raman band positions, band shape and intensity, which are likely related to the degree of structural order and polarization effects.
中文翻译：
拉曼光谱结合机器学习工作流程无损区分地质成因和人为方解石
在这里，我们首次展示了以非破坏性和有效方式区分地质成因和人为方解石的可能性。地质方解石来源于天然沉积岩和变质岩，而人为方解石是由于砂浆和石灰粘合剂中的碳化过程而人工形成的。目前，它们的区别是一个尚未解决的主要问题，尽管它在14等多个领域都至关重要对历史砂浆进行 C 测年以避免碳酸盐聚集体污染，调查颜料的来源，研究沉积物的来源，仅举几例。在本文中，我们解决了这一未满足的需求，将高分辨率显微拉曼光谱与数据挖掘和机器学习方法相结合。这种方法提供了一种获得稳健且具有代表性的拉曼数据集的有效方法，从中可以有效地推断出样本的来源；此外，还强调了沉积方解石和变质方解石之间的区别。化学性质相同的样品在拉曼谱带位置、谱带形状和强度方面表现出系统和可靠的差异，这可能与结构有序程度和偏振效应有关。
更新日期：2023-05-30
中文翻译：
拉曼光谱结合机器学习工作流程无损区分地质成因和人为方解石
在这里，我们首次展示了以非破坏性和有效方式区分地质成因和人为方解石的可能性。地质方解石来源于天然沉积岩和变质岩，而人为方解石是由于砂浆和石灰粘合剂中的碳化过程而人工形成的。目前，它们的区别是一个尚未解决的主要问题，尽管它在14等多个领域都至关重要对历史砂浆进行 C 测年以避免碳酸盐聚集体污染，调查颜料的来源，研究沉积物的来源，仅举几例。在本文中，我们解决了这一未满足的需求，将高分辨率显微拉曼光谱与数据挖掘和机器学习方法相结合。这种方法提供了一种获得稳健且具有代表性的拉曼数据集的有效方法，从中可以有效地推断出样本的来源；此外，还强调了沉积方解石和变质方解石之间的区别。化学性质相同的样品在拉曼谱带位置、谱带形状和强度方面表现出系统和可靠的差异，这可能与结构有序程度和偏振效应有关。
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