To understand global soil organic matter (SOM) chemistry and its dynamics, we need tools to efficiently quantify SOM properties, for example, prediction models using mid-infrared spectra. However, the advantages of such models rely on their validity and accuracy. Recently, Hodgkins et al. (2018) developed models to quantitatively predict peat holocellulose and Klason lignin contents, two indicators of SOM stability and major fractions of organic matter. The models may help to understand large-scale SOM gradients and have been used in various studies.A research gap to fill is that these models have not been validated in detail yet. What are their limitations and how can we improve them? This study provides a validation with the aim to identify concrete steps to improve these models. As a first step, we provide several improvements using the original training data.The major limitation we identified is that the original training data are not representative for a range of diverse peat samples. This causes both biased estimates and extrapolation uncertainty under the original models. In addition, the original models can in practice produce unrealistic predictions (negative values or values >100 mass-%). Our improved models partly reduce the observed bias, have a better predictive performance for the training data, and avoid such unrealistic predictions. Finally, we provide a proof of concept that holocellulose contents can also be predicted for mineral-rich samples (e.g., peat with mineral admixtures or potentially mineral soils).A key step to improve the models will be to collect training data that are representative for SOM formed under various conditions. This study opens directions to develop operational models to predict SOM holocellulose and Klason lignin contents from mid-infrared spectra.

中文翻译：

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利用中红外光谱改进泥炭土壤有机质的全纤维素和 Klason 木质素含量预测模型

为了解全球土壤有机质 (SOM) 化学及其动力学，我们需要有效量化 SOM 特性的工具，例如使用中红外光谱的预测模型。然而，此类模型的优势取决于它们的有效性和准确性。最近，Hodgkins 等人。(2018)开发了模型来定量预测泥炭全纤维素和 Klason 木质素含量，这是 SOM 稳定性和有机质主要部分的两个指标。这些模型可能有助于理解大规模 SOM 梯度，并已用于各种研究。需要填补的研究空白是这些模型尚未得到详细验证。它们的局限性是什么？我们如何改进它们？本研究提供了验证，旨在确定改进这些模型的具体步骤。作为第一步，我们使用原始训练数据提供了一些改进。我们发现的主要限制是原始训练数据不能代表一系列不同的泥炭样本。这会导致原始模型下的估计偏差和外推不确定性。此外，>100 质量％)。我们改进的模型部分减少了观察到的偏差，对训练数据具有更好的预测性能，并避免了这种不切实际的预测。最后，我们提供了一个概念证明，即也可以预测富含矿物质的样品（例如，泥炭与矿物混合物或潜在矿物土壤）的全纤维素含量。改进模型的一个关键步骤是收集具有代表性的训练数据SOM 在各种条件下形成。本研究为开发操作模型以从中红外光谱预测 SOM 全纤维素和 Klason 木质素含量开辟了方向。