Sequential measurement of δ15 N, δ13 C and δ34 S values in archaeological bone collagen at the Scottish Universities Environmental Research Centre (SUERC): A new analytical frontier

Abstract

Rationale: The use of multi-isotopic analysis (δ¹⁵ N, δ¹³ C and δ³⁴ S values) of archaeological bone collagen to assist in the interpretation of diet, movement and mobility of prehistoric populations is gradually increasing, yet many researchers have traditionally avoided investigating sulphur due to its very low concentrations (<0.3%) in mammalian collagen. For this reason, and as a consequence of analytical detection limits, sulphur is usually measured separately from carbon and nitrogen, which leads to longer analytical times and higher costs.

Methods: A Thermo Scientific™ EA IsoLink™ isotope ratio mass spectrometry (IRMS) system, with the ability to rapidly heat a gas chromatography (GC) column and concentrate the sample gas online without cryo-trapping, was used at the Radiocarbon Laboratory at the Scottish Universities Environmental Research Centre (SUERC). Optimisation of the GC temperature and carrier gas flow rate in the elemental analyser resulted in improved signal-to-noise (S/N) ratio and sensitivity for SO₂ . This allowed for routine sequential N₂ , CO₂ and SO₂ measurements on small samples of bone collagen.

Results: Improvements in sample gas transfer to the mass spectrometer allows for sequential δ¹⁵ N, δ¹³ C and δ³⁴ S values to be measured in 1-1.5 mg samples of bone collagen. Moreover, the sensitivity and S/N ratio of the sample gas, especially SO₂ , is improved, resulting in precisions of ±0.15‰ for δ¹⁵ N values, ±0.1‰ for δ¹³ C values and ±0.3‰ for δ³⁴ S values. Previous instrumentation allowed for the analysis of ~30 unknown samples before undertaking maintenance; however, ~150 unknown samples can now be measured, meaning a 5-fold increase in sample throughput.

Conclusions: The ability to sequentially measure δ¹⁵ N, δ¹³ C and δ³⁴ S values rapidly in archaeological bone collagen is an attractive option to researchers who want to build larger, more succinct datasets for their sites of interest, at a much-reduced analytical cost and without destroying larger quantities of archaeological material.