Rock-Eval® (RE) is a thermal analysis technique increasingly used to characterise soil organic matter. To interpret the results, particularly when investigating differences between samples, it is necessary to know the expected ranges of analytical error associated with the RE measurements. Moreover, the RE analyzer is now at its seventh version (RE7) while most literature results were produced using the previous version (RE6). Thus, a characterization of the reproducibility of RE measurements is necessary. We measured the reproducibility of RE measurements using fifteen samples from French croplands and forests that were analysed on five different RE instruments, located in different laboratories and belonging to both generations RE6 and RE7. From each RE analysis, we extracted RE parameters commonly used for soil organic matter characterization and we performed the prediction of the active and stable soil organic carbon fractions using a machine learning model (PartySOC) that uses RE parameters. We obtained a measure of the expected relative errors in RE parameters and PartySOC predictions per instrument, across instruments of the same generation and across generations. We found that the parameters total organic carbon (TOC), mineral carbon (MinC) and R-index are well reproducible, even across the RE6 and RE7 generations. Instead, the hydrogen index (HI) and oxygen index (OI) are more sensitive to signal variations, even within the same generation, especially when TOC is low. The PartySOC predictions were well reproducible across RE6 instruments but not across RE generations. In the future, the results of this study will help discriminate relevant differences between soil samples characterised using RE thermal analysis.

Rock-Eval® (RE) 是一种热分析技术，越来越多地用于表征土壤有机质。为了解释结果，特别是在研究样品之间的差异时，有必要了解与 RE 测量相关的分析误差的预期范围。此外，RE 分析器现在处于第七版本 (RE7)，而大多数文献结果都是使用以前的版本 (RE6) 生成的。因此，有必要对 RE 测量的再现性进行表征。我们使用来自法国农田和森林的 15 个样本来测量 RE 测量的再现性，这些样本使用位于不同实验室且属于 RE6 和 RE7 代的五种不同 RE 仪器进行分析。从每次 RE 分析中，我们提取了常用于土壤有机质表征的 RE 参数，并使用使用 RE 参数的机器学习模型 (PartySOC) 进行了活性和稳定土壤有机碳分数的预测。我们获得了每台仪器、同一代和跨代仪器的 RE 参数和 PartySOC 预测的预期相对误差的度量。我们发现，即使在 RE6 和 RE7 代中，总有机碳 (TOC)、矿物碳 (MinC) 和 R 指数参数也具有良好的重现性。相反，氢指数 (HI) 和氧指数 (OI) 对信号变化更加敏感，即使在同一代内也是如此，尤其是当 TOC 较低时。PartySOC 的预测在 RE6 仪器中可以很好地重现，但在 RE 代之间则不然。将来，这项研究的结果将有助于区分使用稀土热分析表征的土壤样品之间的相关差异。