Notterpek Ivy, Craig Oliver E, Garberi Pauline, Lucquin Alexandre, Théry-Parisot Isabelle, Abiven Samuel
BioArCh, Department of Archaeology, University of York, York, England.
Université Côte d'Azur, CNRS, CEPAM UMR, Nice, France.
PLoS One. 2025 May 14;20(5):e0321584. doi: 10.1371/journal.pone.0321584. eCollection 2025.
The benzene polycarboxylic acid (BPCA) method is a technique to characterise the aromaticity and aromatic condensation of pyrogenic carbon (PyC) in charred residues. As a molecular marker for polycondensed aromatic moieties, the analysis of BPCAs in archaeological contexts has great potential as a means of detecting and characterising charred residues where past fire traces are not evident. Despite the increased frequency of applications and significant developments since the method's inception, no central database of BPCA results for modern charcoal pyrolysed under controlled laboratory conditions exists. Limited sample sizes in previous research have restricted the ability to precisely quantify the effects of combustion temperature, precursor feedstocks, pyrolysis parameters (e.g., oxygen availability), and methodological aspects (e.g., chromatography) on resultant BPCA profiles. To remedy this, we present the BPChAr database, which contains a total of 236 BPCA results on modern lab-produced charcoal. Through statistical analyses of the gathered data, we quantify the relationship between combustion temperature and resultant BPCA profiles, and construct random forest models to predict combustion temperature in unknown samples. Our findings show that additional variables hypothesised to play a role in shaping BPCA results - such as precursor feedstock type, oxygen availability during pyrolysis, and chromatographic separation method - have statistically significant implications for resultant BPCA profiles. Our analysis nuances these observations, highlighting at what charring temperatures and for what variables these concomitant parameters should be factored into the interpretation of BPCA results. Random forest models are also developed to predict precursor feedstock (hardwoods, softwoods, and grasses) in unknown samples, though further work is required to refine the accuracy of this model. The BPChAr database constitutes a fundamental tool for modern PyC research, and provides a baseline for future work aimed at employing the BPCA method in palaeoenvironmental and archaeological research.
苯多元羧酸(BPCA)法是一种用于表征烧焦残渣中热解碳(PyC)的芳香性和芳香缩合的技术。作为多缩合芳香部分的分子标记,在考古背景下分析BPCA作为一种检测和表征过去火灾痕迹不明显的烧焦残渣的方法具有巨大潜力。尽管自该方法问世以来应用频率增加且有重大进展,但目前尚无关于在受控实验室条件下热解的现代木炭的BPCA结果的中央数据库。先前研究中的样本量有限,限制了精确量化燃烧温度、前驱体原料、热解参数(如氧气可用性)和方法学方面(如色谱法)对所得BPCA谱的影响的能力。为了弥补这一点,我们展示了BPChAr数据库,其中包含总共236个关于现代实验室生产木炭的BPCA结果。通过对收集数据的统计分析,我们量化了燃烧温度与所得BPCA谱之间的关系,并构建随机森林模型来预测未知样品中的燃烧温度。我们的研究结果表明,假设在塑造BPCA结果中起作用的其他变量——如前驱体原料类型、热解过程中的氧气可用性和色谱分离方法——对所得BPCA谱具有统计学上的显著影响。我们的分析细化了这些观察结果,突出了在何种炭化温度下以及对于哪些变量,这些伴随参数应纳入BPCA结果的解释中。还开发了随机森林模型来预测未知样品中的前驱体原料(硬木、软木和草),不过需要进一步开展工作来提高该模型的准确性。BPChAr数据库构成了现代PyC研究的基本工具,并为未来旨在将BPCA方法应用于古环境和考古研究的工作提供了基线。