Transferability of a large mid-infrared soil spectral library between two Fourier-transform infrared spectrometers

Jonathan Sanderman, Asa Gholizadeh, Zampela Pittaki-Chrysodonta, Jingyi Huang, José Lucas Safanelli, Richard Ferguson
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Abstract

Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) mid-infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low-cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC-KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky-Golay smoothing and first derivative preprocessing of the secondary spectra using a memory-based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC-KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.

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大型中红外土壤光谱库在两台FTIR光谱仪之间的可转移性
大型和公开可用的土壤光谱库,如美国农业部国家土壤调查中心-凯洛格土壤调查实验室(NSSC-KSSL)中红外(MIR)光谱库,是非常宝贵的资源,使世界各地的实验室能够快速低成本地估计许多土壤特性。土壤光谱数据广泛共享的一个限制是需要确保二级光谱仪收集的光谱与主要或参考库中的光谱兼容。为了克服这一限制,提出了各种光谱预处理和校准转移技术。我们测试了使用USDA NSSC-KSSL MIR库开发的模型到二级仪器的可转移性。对于土壤性质、总碳(TC)、pH和粘土含量,我们发现使用Savitzky-Golay平滑和基于记忆的学习化学计量学方法对二次光谱进行一阶导数预处理可以在独立测试集上获得良好的性能(性能与偏差比[RPD]分别为4.9、2.0和3.6)。我们测试了三种校准转移技术(直接标准化[DS]、分段直接标准化[PDS]和光谱空间变换[SST]),选择了不同大小的转移集来代表整个NSSC-KSSL库。在迁移方法中,SST的迁移样本数始终优于DS和PDS,其中50个迁移样本是迁移模型开发的最优数量。对于TC和pH,主要通过消除偏差,使用SST转移(RPD分别为7.7和2.2)提高了性能。校准转移不能改善对粘土的预测。这些发现表明,校准转移可能并不总是必要的,但用户应该使用在两个实验室扫描的一小组代表性样品进行测试以确认这一假设。
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