Improving memory effect correction to achieve high-precision analysis of δ17 O, δ18 O, δ2 H, 17 O-excess and d-excess in water using cavity ring-down laser spectroscopy

A protocol for distilling animal body water from biological samples and measuring oxygen and hydrogen stable isotopes via cavity ring-down spectroscopy

The application of stable isotope analysis (SIA) to the fields of ecology and animal biology has rapidly expanded over the past three decades, particularly with regards to water analysis. SIA now provides the opportunity to monitor migration patterns, examine food webs, and assess habitat changes in current and past study systems. While carbon and nitrogen SIA of biological samples have become common, analyses of oxygen or hydrogen are used more sparingly despite their promising utility for tracing water sources and animal metabolism. Common ecological applications of oxygen or hydrogen SIA require injecting enriched isotope tracers. As such, methods for processing and analyzing biological samples are tailored for enriched tracer techniques, which require lower precision than other techniques given the large signal-to-noise ratio of the data. However, instrumentation advancements are creating new opportunities to expand the applications of high-throughput oxygen and hydrogen SIA. To support these applications, we update methods to distill and measure water derived from biological samples with consistent precision equal to, or better than, ± 0.1 ‰ for δ17O, ± 0.3 ‰ for δ18O, ± 1 ‰ for δ2H, ± 2 ‰ for d-excess, and ± 15 per meg for Δ17O.

A simple method for rapid removal of the memory effect in cavity ring-down spectroscopy water isotope measurements

RATIONALE

The accuracy determined in the routine analysis of water isotopes (δ17 O, δ18 O, δ2 H) using cavity ring-down spectroscopy is greatly affected by the memory effect (ME), a sample-to-sample carryover that biases measurements. This study aims to develop a simple method that rapidly removes the ME.

METHODS

We developed a method, designed for the Picarro L2140-i, that removes the ME by injecting small amounts of water with an extreme isotopic value ("kick") in the opposite direction of the ME. We conducted 11 experiments to identify the optimal kick for pairs of isotopically enriched and depleted samples. Once quantified, the optimal kick was used to create an ME-free, unbiased calibration curve, which was verified using international and internal lab standards.

RESULTS

Our kick method removes the ME very efficiently in half the time it takes for experiments without a kick. The optimal number of kick injections required to minimize stabilization time between standards of different compositions is three injections of δ2 H ≈ -1000‰ water per a 100‰ difference between standards. Three runs of routine measurements using the kick method resulted in uncertainties of 0.03‰, 0.2‰, and 5 permeg for δ18 O, δ2 H, and 17 O-excess, respectively.

CONCLUSIONS

This study demonstrates a new method for rapidly removing the ME. Our kick protocol is a readily available, cheap, and efficient approach to reduce instrumental bias and improve measurement accuracy.

Balancing precision and throughput of ..17O and .÷...17O analysis of natural waters by Cavity Ringdown Spectroscopy

δ ¹⁷O and Δ'¹⁷O are emerging tracers increasingly used in isotope hydrology, climatology, and biochemistry. Differentiating small relative abundance changes in the rare ¹⁷O isotope from the strong covariance with ¹⁸O imposes ultra-high precision requirements for this isotope analysis. Measurements of δ ¹⁷O by Cavity Ringdown Spectroscopy (CRDS) are attractive due to the ease of sample preparation, automated throughput, and avoidance of chemical conversions needed for isotope-ratio mass spectrometry. However, the CRDS approach requires trade-offs in measurement precision and uncertainty. In this protocol document, we present the following:•New analytical procedures and a software tool for conducting δ ¹⁷O and Δ'¹⁷O measurements by CRDS.•Outline a robust uncertainty framework for Δ'¹⁷O determinations.•Description of a CRDS performance framework for optimizing throughput, instrumental stability, and Δ'¹⁷O measurement precision and accuracy.

17 O-excess and d-excess of atmospheric water vapor measured by cavity ring-down spectrometry: Evidence of a matrix effect and implication for the calibration procedure

RATIONALE

Producing robust high-frequency time series of raw atmospheric water vapor isotope data using laser spectrometry requires accurate calibration. In particular, the chemical composition of the analyzed sample gas can cause isotope bias. This study assesses the matrix effect on calibrated δ¹⁷ O, δ¹⁸ O, δ² H, ¹⁷ O-excess, and d-excess values of atmospheric water vapor.

METHODS

A Picarro L2140-i cavity ring-down spectrometer with an autosampler and a vaporizer is used to analyze δ¹⁷ O, δ¹⁸ O, δ² H, ¹⁷ O-excess, and d-excess of two water standards. Isotope data obtained using synthetic air and dry ambient air as carrier gas at water mixing ratios ranging from 2000 to 30 000 ppmv are compared. Based on the results, atmospheric water vapor measurements are calibrated. The expected precision is estimated by Monte Carlo simulation.

RESULTS

The dry air source strongly impacts raw isotope values of the two water standards but has no effect on the mixing ratio dependency functions. When synthetic air is used, δ¹⁷ O, δ¹⁸ O, and ¹⁷ O-excess of calibrated atmospheric water vapor are overestimated by 0.6‰, 0.7‰, and 217 per meg, respectively, whereas δ² H and d-excess are underestimated by 1.5‰ and 7.3‰. Optimum precisions for the calibrated δ¹⁷ O, δ¹⁸ O, δ² H, ¹⁷ O-excess, and d-excess values and 12 min integration time are 0.02‰, 0.03‰, 0.4‰, 14 per meg, and 0.4‰, respectively.

CONCLUSIONS

Regarding the obtained results, recommendations for the calibration of atmospheric water vapor isotope measurements are presented. The necessity to use dry ambient air as dry air source when running the standards for calibration is pointed out as a prerequisite for accurate atmospheric water vapor ¹⁷ O-excess and d-excess measurements.

Faster and more precise isotopic water analysis of discrete samples by predicting the repetitions’ asymptote instead of averaging last values

Water stable isotope analysis using Cavity Ring-Down Spectroscopy (CRDS) has a strong between-sample memory effect. The classic approach to correct this memory effect is to inject the sample at least 6 times and ignore the first two to three injections. The average of the remaining injections is then used as measured value. This is in many cases insufficient to completely compensate the memory effect. We propose a simple approach to correct this memory effect by predicting the asymptote of consecutive repeated injections instead of averaging over them. The asymptote is predicted by fitting a y=ax+b relation to the sample repetitions and keeping b as measured value. This allows to save analysis time by doing less injections while gaining precision. We provide a Python program applying this method and describe the steps necessary to implement this method in any other programming language. We also show validation data comparing this method to the classical method of averaging over the last couple of injections. The validation suggests a gain in time of a factor two while gaining in precision at the same time. The method does not have any specific requirements for the order of analysis and can therefore also be applied to an existing set of analyzes in retrospect.•

We fit a simple y=ax+b relation to the sample repetitions of Picarro L2130-i isotopic water analyzer, in order to keep the asymptote (b) as measured value instead of using the average over the last couple of measurements.

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This allows a higher precision in the measured value with less repetitions of the injection saving precious time during analysis.

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We provide a sample code using Python, but generally this method is easy to implement in any automated data treatment protocol.

Comparison of isotope ratio mass spectrometry and cavity ring-down spectroscopy procedures and precision of the doubly labeled water method in different physiological specimens

RATIONALE

This study determines if saliva collection procedures for the doubly labeled water (DLW) method, used for measuring total energy expenditure (TEE), are comparable to urine and plasma collection. Both the cavity ring-down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS) analyses techniques are compared.

METHODS

Saliva specimens were collected from participants for the DLW method. The specimens were collected under different conditions: after consumption of tap water, after chewing gum, and during exposure to conditions of high and low relative humidity. The isotopes in saliva were compared with simultaneous plasma and urine collection. TEE calculated from saliva and analyzed using CRDS was compared to that of plasma analyzed using IRMS.

RESULTS

The within-individual variances were not significantly different between the saliva specimens (0.4‰) and plasma (0.3‰). After the oral dose of DLW, the saliva specimens displayed a shorter equilibration time to urine. When participants consumed 500 mL of tap water, the enrichment of saliva specimens reached a new plateau value faster than urine. Saliva collection exposed to high ambient humidity conditions was slightly less enriched as compared to low-humidity conditions while urine enrichment was unaffected. In contrast, whereas the within-individual effects of gum chewing during saliva collection on ¹⁸ O were unaffected, the abundance of ² H in saliva was slightly lower after chewing the gum. The within-individual difference between TEE calculated from saliva and that calculated from plasma analyzed using IRMS did not differ from zero, and the standard deviation was not different from that predicted by a propagation of error analysis based on analytical error alone.

CONCLUSIONS

Our findings support using saliva specimens for the DLW method. The analysis of plasma and urine, however, requires reducing the memory effect resulting from contaminants. Also, it should be performed in a manner that minimizes exposure to air where specimens may be exposed to evaporation or contamination from water vapor during sampling.