Partial least-squares regression (PLSR), using spectra from a handheld mid-infrared instrument (the ExoScan), was tested for the prediction of particle size distribution. Soils were sampled from agricultural sites in the Eyre Peninsula under field conditions and with varying degrees of soil preparation. Issues relevant to field sampling were identified, such as sample heterogeneity, micro-aggregate size and moisture content. The PLSR models for particle size distribution were derived with the varying degrees of preparation. Cross-validation of clay content in the as-received in situ soils resulted in low accuracy: coefficient of determination (R2) = 0.55 and root mean square error (RMSE) = 7%. This was improved by manual mixing, drying, sieving to < 2 mm and fine grinding, resulting in R2 values of 0.64, 0.75 and 0.81, and RMSE of 6%, 5% and 4% respectively; less improvement resulted for sand, with corresponding R2 values of 0.82, 0.88, 0.91 and 0.89, and RMSE of 10%, 8%, 6% and 7%. Predictions for silt remained poor. Where only archival benchtop calibration models were available, predictions of clay contents for spectra scanned with the handheld ExoScan spectrometer resulted in high error because of spectral intensity mismatch between benchtop and handheld spectra (R2 = 0.72, RMSE = 24.2% and bias = 21%). Pre-processing the benchtop spectra by piecewise direct standardisation resulted in more successful predictions (R2 = 0.73, RMSE = 6.7% and bias = –1.5%), confirming the advantage of piecewise direct standardisation for prediction from archival spectral libraries.

中文翻译：

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手持式中红外光谱预测粒度分布的可行性：土壤条件的影响和现有光谱库的利用

偏最小二乘回归 (PLSR) 使用来自手持式中红外仪器 (ExoScan) 的光谱进行测试，以预测粒度分布。在田间条件和不同程度的整地条件下，从艾尔半岛的农田取样土壤。确定了与现场采样相关的问题，例如样品异质性、微团聚体尺寸和水分含量。粒度分布的 PLSR 模型是通过不同程度的制备得到的。原位土壤中粘土含量的交叉验证导致准确性低：确定系数 (R2) = 0.55 和均方根误差 (RMSE) = 7%。这通过手动混合、干燥、筛分至 < 2 mm 和精细研磨得到改善，导致 R2 值为 0.64、0.75 和 0.81，RMSE 为 6%，分别为 5% 和 4%；砂的改善较小，相应的 R2 值为 0.82、0.88、0.91 和 0.89，RMSE 为 10%、8%、6% 和 7%。对淤泥的预测仍然很差。在只有存档台式校准模型可用的情况下，使用手持式 ExoScan 光谱仪扫描的光谱的粘土含量预测会导致高误差，因为台式和手持式光谱之间的光谱强度不匹配（R2 = 0.72，RMSE = 24.2% 和偏差 = 21%） . 通过分段直接标准化对台式光谱进行预处理导致更成功的预测（R2 = 0.73，RMSE = 6.7% 和偏差 = –1.5%），证实了分段直接标准化对档案光谱库预测的优势。和 RMSE 分别为 10%、8%、6% 和 7%。对淤泥的预测仍然很差。在只有存档台式校准模型可用的情况下，使用手持式 ExoScan 光谱仪扫描的光谱的粘土含量预测会导致高误差，因为台式和手持式光谱之间的光谱强度不匹配（R2 = 0.72，RMSE = 24.2% 和偏差 = 21%） . 通过分段直接标准化对台式光谱进行预处理导致更成功的预测（R2 = 0.73，RMSE = 6.7% 和偏差 = –1.5%），证实了分段直接标准化对档案光谱库预测的优势。和 RMSE 分别为 10%、8%、6% 和 7%。对淤泥的预测仍然很差。在只有存档台式校准模型可用的情况下，使用手持式 ExoScan 光谱仪扫描的光谱的粘土含量预测会导致高误差，因为台式和手持式光谱之间的光谱强度不匹配（R2 = 0.72，RMSE = 24.2% 和偏差 = 21%） . 通过分段直接标准化对台式光谱进行预处理可得到更成功的预测（R2 = 0.73，RMSE = 6.7% 和偏差 = –1.5%），证实了分段直接标准化对档案光谱库预测的优势。由于台式光谱和手持光谱之间的光谱强度不匹配（R2 = 0.72，RMSE = 24.2% 和偏差 = 21%），对使用手持式 ExoScan 光谱仪扫描的光谱的粘土含量的预测导致了很高的误差。通过分段直接标准化对台式光谱进行预处理可得到更成功的预测（R2 = 0.73，RMSE = 6.7% 和偏差 = –1.5%），证实了分段直接标准化对档案光谱库预测的优势。由于台式光谱和手持光谱之间的光谱强度不匹配（R2 = 0.72，RMSE = 24.2% 和偏差 = 21%），对使用手持式 ExoScan 光谱仪扫描的光谱的粘土含量的预测导致了很高的误差。通过分段直接标准化对台式光谱进行预处理可得到更成功的预测（R2 = 0.73，RMSE = 6.7% 和偏差 = –1.5%），证实了分段直接标准化对档案光谱库预测的优势。