In mid‐infrared diffuse reflectance (MIR) soil spectroscopy, grinding is one major step that can have pronounced effects on spectra and model calibrations. The reported literature on the effects of fine grinding on spectroscopic model performance have been inconsistent, likely in part because of limitations in sample set and model calibrations in previous studies. This study was focused on answering the question whether fine grinding is necessary for MIR spectroscopy in order to minimize model uncertainty. The main goal of this study was to compare model performance with and without fine grinding for eight soil properties using two different modeling techniques: partial least squares regression (PLS) and artificial neural networks (ANN). Approximately 500 soil samples were extracted from a large MIR spectral library in the United States to obtain spectra at non‐fine ground (NG, <2 mm,) and fine‐ground (FG, <0.18 mm,) states. Performance of calibration models built using subsets of the 500 FG and 500 NG spectra were compared with models built using the entire FG spectral library (n > 40,000). All the model calibrations and validations were repeated 100 times to evaluate the uncertainty of the model performances. The results showed that PLS performed similar to ANN for the smaller dataset, but the best model performance was obtained with the FG full spectral library with ANN models. Predictions on the FG spectra always outperformed predictions on the NG spectra in terms of goodness‐of‐fit and variance of statistics. Overall, this study confirmed the importance of fine grinding to ensure the best MIR spectroscopic model performance.

中文翻译：

---

需要精细研磨以保持中红外漫反射光谱法在土壤性质评估中的高精度

在中红外漫反射（MIR）土壤光谱学中，研磨是可以对光谱和模型校准产生明显影响的主要步骤之一。关于精细研磨对光谱模型性能的影响的已报道文献不一致，这可能部分是由于先前研究中样品集和模型校准的局限性所致。这项研究的重点是回答是否需要对MIR光谱进行精细研磨以最小化模型不确定性的问题。这项研究的主要目标是使用两种不同的建模技术：偏最小二乘回归（PLS）和人工神经网络（ANN），比较有无精细研磨的八个土壤特性的模型性能。从美国的大型MIR光谱库中提取了大约500个土壤样品，以获得非精细地面（NG，<2 mm）和精细地面（FG，<0.18 mm）状态的光谱。将使用500 FG和500 NG光谱的子集构建的校准模型的性能与使用整个FG光谱库构建的模型的性能进行了比较（n  > 40,000）。所有模型校准和验证都重复了100次，以评估模型性能的不确定性。结果表明，对于较小的数据集，PLS的性能与ANN相似，但是使用具有ANN模型的FG全谱库可以获得最佳的模型性能。就拟合优度和统计方差而言，FG谱的预测总是优于NG谱的预测。总体而言，这项研究证实了精磨对于确保最佳MIR光谱模型性能的重要性。