Hachgenei Nico, Vaury Véronique, Nord Guillaume, Spadini Lorenzo, Duwig Céline
IGE, Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, Grenoble, France.
iEES, Sorbonne Univ., Paris, France.
MethodsX. 2022 Mar 3;9:101656. doi: 10.1016/j.mex.2022.101656. eCollection 2022.
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 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 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.•This allows a higher precision in the measured value with less repetitions of the injection saving precious time during analysis.•We provide a sample code using Python, but generally this method is easy to implement in any automated data treatment protocol.
使用腔衰荡光谱法（CRDS）进行水稳定同位素分析存在很强的样本间记忆效应。纠正这种记忆效应的经典方法是至少注入样本6次，并忽略前两到三次注入。然后将其余注入的平均值用作测量值。在许多情况下，这不足以完全补偿记忆效应。我们提出了一种简单的方法来纠正这种记忆效应，即通过预测连续重复注入的渐近线而不是对其求平均值。通过将一个关系拟合到样本重复次数并将b用作测量值来预测渐近线。这可以通过减少注入次数来节省分析时间，同时提高精度。我们提供了一个应用此方法的Python程序，并描述了在任何其他编程语言中实现此方法所需的步骤。我们还展示了将此方法与对最后几次注入求平均值的经典方法进行比较的验证数据。验证表明，在提高精度的同时，时间节省了一半。该方法对分析顺序没有任何特定要求，因此也可以追溯应用于现有的一组分析。•我们将一个简单的关系拟合到Picarro L2130-i同位素水分析仪的样本重复次数上，以便将渐近线（b）用作测量值，而不是使用最后几次测量的平均值。•这样可以在减少注入重复次数的情况下提高测量值的精度，从而在分析过程中节省宝贵的时间。•我们提供了一个使用Python的示例代码，但一般来说，这种方法在任何自动化数据处理协议中都很容易实现。
