Correction algorithm for online continuous flow m>δm>¹³C and m>δm>¹⁸O carbonate and cellulose stable isotope analyses

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• 作者：M. N. Evans ; K. J. Selmer ; B. T. Breeden III ; A. S. Lopatka ; R. E. Plummer
• 刊名：Geochemistry, Geophysics, Geosystems
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：17
• 期：9
• 页码：3580-3588
• 全文大小：452.0KB
• ISSN：1525-2027

文摘

We describe an algorithm to correct for scale compression, runtime drift, and amplitude effects in carbonate and cellulose oxygen and carbon isotopic analyses made on two online continuous flow isotope ratio mass spectrometry (CF-IRMS) systems using gas chromatographic (GC) separation. We validate the algorithm by correcting measurements of samples of known isotopic composition which are not used to estimate the corrections. For carbonate m>δm>¹³C (m>δm>¹⁸O) data, median precision of validation estimates for two reference materials and two calibrated working standards is 0.05‰ (0.07‰); median bias is 0.04‰ (0.02‰) over a range of 49.2‰ (24.3‰). For m>αm>-cellulose m>δm>¹³C (m>δm>¹⁸O) data, median precision of validation estimates for one reference material and five working standards is 0.11‰ (0.27‰); median bias is 0.13‰ (−0.10‰) over a range of 16.1‰ (19.1‰). These results are within the 5th–95th percentile range of subsequent routine runtime validation exercises in which one working standard is used to calibrate the other. Analysis of the relative importance of correction steps suggests that drift and scale-compression corrections are most reliable and valuable. If validation precisions are not already small, routine cross-validated precision estimates are improved by up to 50% (80%). The results suggest that correction for systematic error may enable these particular CF-IRMS systems to produce m>δm>¹³C and m>δm>¹⁸O carbonate and cellulose isotopic analyses with higher validated precision, accuracy, and throughput than is typically reported for these systems. The correction scheme may be used in support of replication-intensive research projects in paleoclimatology and other data-intensive applications within the geosciences.