Loss-on-ignition (LOI) is the most widely used measure of organic matter in lake sediments, a variable related to both climate and land-use change. The main drawback for conventional measurement methods is the processing time and hence high labor costs associated with high-resolution analyses. On the other hand, broad-based near infrared reflectance spectroscopy (NIRS) is a time and cost efficient method to measure organic carbon and organic matter content in lacustrine sediments once predictive methods are developed. NIRS-based predictive models are most robust when applied to sediments with properties that are already included in the calibration dataset. To test the potential for a broad applicability of NIRS models in samples foreign to the calibration model using linear corrections, sediment cores from six lakes (537 samples, LOI range 1.03–85%) were used as reference samples to develop a predictive model. The applicability of the model was assessed by sequentially removing one lake from the reference dataset, developing a new model and then validating it against the removed lake. Results indicated that NIRS has a high predictive power (RMSEP < 4.79) for LOI with the need for intercept and slope correction for new cores measured by NIRS. For studies involving many samples, NIRS is a cost and time-efficient method to estimate LOI on a range of lake sediments with only linear bias adjustments for different records.

中文翻译：

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近红外反射光谱 (NIRS) 测量多个湖泊中沉积物有机质的效果如何？

烧失量 (LOI) 是衡量湖泊沉积物中有机质的最广泛使用的指标，是与气候和土地利用变化相关的变量。传统测量方法的主要缺点是处理时间长，因此与高分辨率分析相关的劳动力成本很高。另一方面，一旦开发出预测方法，宽基近红外反射光谱 (NIRS) 是一种测量湖泊沉积物中有机碳和有机物含量的时间和成本效益高的方法。当应用于具有已包含在校准数据集中的特性的沉积物时，基于 NIRS 的预测模型最为稳健。为了测试 NIRS 模型在使用线性校正的校准模型以外的样本中广泛适用的潜力，来自六个湖泊的沉积物核心（537 个样本，LOI 范围 1. 03–85%) 被用作参考样本来开发预测模型。该模型的适用性是通过从参考数据集中依次移除一个湖泊、开发一个新模型然后针对移除的湖泊进行验证来评估的。结果表明，NIRS 对 LOI 具有很高的预测能力 (RMSEP < 4.79)，需要对 NIRS 测量的新岩心进行截距和斜率校正。对于涉及许多样本的研究，NIRS 是一种成本和时间效率高的方法，用于估计一系列湖泊沉积物的 LOI，只需对不同记录进行线性偏差调整。结果表明，NIRS 对 LOI 具有很高的预测能力（RMSEP < 4.79），需要对 NIRS 测量的新岩心进行截距和斜率校正。对于涉及许多样本的研究，NIRS 是一种成本和时间效率高的方法，用于估计一系列湖泊沉积物的 LOI，只需对不同记录进行线性偏差调整。结果表明，NIRS 对 LOI 具有很高的预测能力 (RMSEP < 4.79)，需要对 NIRS 测量的新岩心进行截距和斜率校正。对于涉及许多样本的研究，NIRS 是一种成本和时间效率高的方法，用于估计一系列湖泊沉积物的 LOI，只需对不同记录进行线性偏差调整。