Abstract：
Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen - a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.
摘要：
考古材料的分析化学是现代考古调查的重要组成部分，仪器的改进使得以高时空频率生成数据成为可能。特别是,拉曼光谱成像可以通过简单的实施方式成功地应用于考古研究，通过分析其物质遗迹来研究过去的人类社会。该技术使得可以通过保持样品完整性来同时获得空间和光谱信息。然而,由于考古学中样本的固有复杂性(例如资历，脆弱,缺乏或完全没有关于其组成的任何信息)，化学解释可能很难乍一看。的确,由于化合物的守恒状态，可能会出现与意外化合物有关的光谱选择性的特定问题。此外，检测次要化合物变得具有挑战性，因为主要成分在获得的光谱中强加了它们的贡献。因此,在这种情况下，已经引入了相关的化学计量学方法，以表征考古标本-马赛克碎片的拉曼成像数据集中的不同光谱源。该碎片是在佩皮尼昂郊区的Ruscino考古挖掘中发掘的，法国。它可以追溯到oppidum时期。目的是从像素聚类分析中提取选择性光谱信息，以增强多元曲线分辨率和交替最小二乘（MCR-ALS）算法中的初始优化步骤。一种众所周知的信号解混技术。MCR-ALS的基本原理是所获得的光谱可以表示为所研究的化学系统中存在的所有单个组分的纯光谱的线性组合。有时很难通过算法获得想要的结果,特别是如果光谱或浓度分布的初始估计由于复杂信号而不准确，噪音或缺乏选择性，导致秩不足(即,对纯信号的总数的较差估计)。出于这个原因,一种创新的基于阈值的聚类算法，结合多个正交投影方法(OPA)，已经开发了改进矩阵秩调查，从而在优化之前改进MCR-ALS方法的初始化步骤。对考古马赛克的拉曼成像数据的有效分析在发现有关特定生物材料的重要化学信息中起着至关重要的作用。这种见解揭示了在oppidum时期砂浆制造的起源。
