Tyler B Coplen1, Leonard I Wassenaar2. 1. U.S. Geological Survey, 431 National Center, Reston, VA, 20192, USA. 2. International Atomic Energy Agency, A-1400, Vienna, Austria.
Abstract
RATIONALE: Although laser absorption spectrometry (LAS) instrumentation is easy to use, its incorporation into laboratory operations is not easy, owing to extensive offline manipulation of comma-separated-values files for outlier detection, between-sample memory correction, nonlinearity (δ-variation with water amount) correction, drift correction, normalization to VSMOW-SLAP scales, and difficulty in performing long-term QA/QC audits. METHODS: A Microsoft Access relational-database application, LIMS (Laboratory Information Management System) for Lasers 2015, was developed. It automates LAS data corrections and manages clients, projects, samples, instrument-sample lists, and triple-isotope (δ(17)O, δ(18)O, and δ(2)H values) instrumental data for liquid-water samples. It enables users to (1) graphically evaluate sample injections for variable water yields and high isotope-delta variance; (2) correct for between-sample carryover, instrumental drift, and δ nonlinearity; and (3) normalize final results to VSMOW-SLAP scales. RESULTS: Cost-free LIMS for Lasers 2015 enables users to obtain improved δ(17)O, δ(18)O, and δ(2)H values with liquid-water LAS instruments, even those with under-performing syringes. For example, LAS δ(2) HVSMOW measurements of USGS50 Lake Kyoga (Uganda) water using an under-performing syringe having ±10 % variation in water concentration gave +31.7 ± 1.6 ‰ (2-σ standard deviation), compared with the reference value of +32.8 ± 0.4 ‰, after correction for variation in δ value with water concentration, between-sample memory, and normalization to the VSMOW-SLAP scale. CONCLUSIONS: LIMS for Lasers 2015 enables users to create systematic, well-founded instrument templates, import δ(2) H, δ(17) O, and δ(18) O results, evaluate performance with automatic graphical plots, correct for δ nonlinearity due to variable water concentration, correct for between-sample memory, adjust for drift, perform VSMOW-SLAP normalization, and perform long-term QA/QC audits easily. Published in 2015. This article is a U.S. Government work and is in the public domain in the USA.
RATIONALE: Although laser absorption spectrometry (LAS) instrumentation is easy to use, its incorporation into laboratory operations is not easy, owing to extensive offline manipulation of comma-separated-values files for outlier detection, between-sample memory correction, nonlinearity (δ-variation with water amount) correction, drift correction, normalization to VSMOW-SLAP scales, and difficulty in performing long-term QA/QC audits. METHODS: A Microsoft Access relational-database application, LIMS (Laboratory Information Management System) for Lasers 2015, was developed. It automates LAS data corrections and manages clients, projects, samples, instrument-sample lists, and triple-isotope (δ(17)O, δ(18)O, and δ(2)H values) instrumental data for liquid-water samples. It enables users to (1) graphically evaluate sample injections for variable water yields and high isotope-delta variance; (2) correct for between-sample carryover, instrumental drift, and δ nonlinearity; and (3) normalize final results to VSMOW-SLAP scales. RESULTS: Cost-free LIMS for Lasers 2015 enables users to obtain improved δ(17)O, δ(18)O, and δ(2)H values with liquid-water LAS instruments, even those with under-performing syringes. For example, LAS δ(2) HVSMOW measurements of USGS50 Lake Kyoga (Uganda) water using an under-performing syringe having ±10 % variation in water concentration gave +31.7 ± 1.6 ‰ (2-σ standard deviation), compared with the reference value of +32.8 ± 0.4 ‰, after correction for variation in δ value with water concentration, between-sample memory, and normalization to the VSMOW-SLAP scale. CONCLUSIONS: LIMS for Lasers 2015 enables users to create systematic, well-founded instrument templates, import δ(2) H, δ(17) O, and δ(18) O results, evaluate performance with automatic graphical plots, correct for δ nonlinearity due to variable water concentration, correct for between-sample memory, adjust for drift, perform VSMOW-SLAP normalization, and perform long-term QA/QC audits easily. Published in 2015. This article is a U.S. Government work and is in the public domain in the USA.
Authors: Iván Vargas-Cordero; Umberta Tinivella; Lucía Villar-Muñoz; Joaquim P Bento; Carolina Cárcamo; Diego López-Acevedo; Francisco Fernandoy; Alessandra Rivero; Marion San Juan Journal: Sci Rep Date: 2020-12-14 Impact factor: 4.379