Calibration and validation statistics of partial least-squares regression models.

<p>Calibrations were evaluated as follows (Saeys et al (2005): excellent (R²/r² ≥ 0.9, RPD ≥ 3.0), reliable quantitative predictions (R²/r² ≥ 0.75 and <0.9, RPD ≥ 2.0 and <3.0), differentiation between high and low values (R²/r² ≥ 0.65 and <0.75, RPD ≥ 1.5 and <2.0, unsuccessful (R²/r² <0.65, RPD <1.5).</p><p>Transf: transformations for regression analyses.</p><p>Abs: Log1/R (R = reflectance).</p><p>1D: first derivative.</p><p>R²: coefficient of multiple determination (calibration).</p><p>SEC: standard error of calibration.</p><p>SECV: standard error of cross validation.</p><p>r²: regression coefficient NIRS predicted vs. observed values.</p><p>SEP: standard error of prediction (validation).</p><p>RPD: ratio of SD of reference values (validation) to SEP.</p><p>RPIQ: ratio of the interquartile distance IQ ( = Q3–Q1) of reference values (validation) to SEP.</p><p>Corr. OM: Pearson correlation with organic matter content.</p

Exemplary observed vs. NIR predicted values for NaCl-extractable Ca and N-NH₄ and oxalate-extractable Fe and P.

<p><i>Open circles</i> calibration data set, <i>filled circles</i> external validation.</p

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

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