A protocol for using ATR‐FTIR for pre‐screening ancient bone collagen prior to radiocarbon dating.
A protocol for using ATR‐FTIR for pre‐screening ancient bone collagen prior to radiocarbon dating.
复制标题
在放射性碳测年之前使用 ATR-FTIR 预筛选古代骨胶原的方案。
DOI：
10.1002/rcm.8720
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发表时间：
2020
影响因子：
2
通讯作者：
H.
中科院分区：
文献类型：
--
作者：
Naito;Y. I.;Yamane;M. and Kitagawa;H.
合理的骨胶原质量的预筛选对于降低考古研究中用加速器质谱仪测定放射性碳(14C)年代等分析的成本很重要。建立了一种基于衰减全反射(ATR)傅里叶变换红外光谱(FTIR)的预筛方法，用于评价古骨样品的化学成分和矿物学。方法在提取胶原蛋白之前，对不同来源和年龄的大宗骨进行ATR-FTIR测量。记录大鼠骨的含氮量百分比、重量百分比以及提取的有机物的碳氮量百分比。结果：(A)光谱数据的一阶导数比原始FTIR数据更适合于光谱数据的筛选，特别是在更宽的光谱范围内；(B)某些分类算法[如梯度助推机(GBM)]能够有效地预测骨骼样本的保存程度。结论该预筛选方法可以作为一种快速、简洁和廉价的预筛选工具，用于在胶原提取和随后的14C测年之前确定相对保存程度。通过积累骨骼的FTIR光谱数据，可以进一步提高基于机器学习技术的筛选能力。
RationalePre‐screening of bone collagen quality is important to reduce the cost for analyses such as radiocarbon (14C) dating with accelerator mass spectrometry in archaeological studies. We developed a pre‐screening protocol based on attenuated total reflection (ATR) Fourier‐transform infrared spectroscopy (FTIR) for assessing the chemical composition and mineralogy of ancient bone samples.MethodsATR‐FTIR measurements were performed on bulk bones of diverse origin and age before collagen extraction. The percentage nitrogen of bulk bones, as well as the weight percentage, and the percentage carbon and nitrogen of extracted organic matter were noted. Several machine learning algorithms were applied to the spectral data and compared for their efficacy in screening for well preserved collagen.ResultsThe results showed that (a) the first derivative of the spectral data was better suited to screening than the raw FTIR data, especially for a wider spectral range and (b) certain classification algorithms [e.g. gradient boosting machine (GBM)] were able to efficiently predict the degree of preservation in bone samples.ConclusionsThis pre‐screening protocol can serve as a fast, concise and inexpensive pre‐screening tool for determining relative degrees of preservation before collagen extraction and subsequent14C dating. The screening power based on the machine learning techniques can be further improved by accumulating the FTIR spectral data of bones.