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Division S-5—Pedology

Determination of Clay and Other Soil Properties by Near Infrared Spectroscopy

Steins Laboratorium, Ladelundvej 85, 6650 Brørup, Denmark

^* Corresponding author (lks{at}steins.dk).

The feasibility of using near infrared (NIR) spectroscopy for rapid non-destructive prediction of clay and other soil properties was investigated. Soil from all regions of Denmark was used to develop universal NIR spectroscopic calibrations. Samples were packed in a rectangular sample cell with a surface area of 60 cm² and the cell was moved during measurements using a transport module. Reflectance was measured in the 400- to 2500-nm range and instrument calibration was performed by partial least square (PLS) regression. The accuracy and robustness of NIR equations for determination of clay was dependent on the calibrated concentration range, the spectral regions used for calibration and the spectral pretreatment procedure. The optimal narrow range calibration for clay (1–26% clay) gave prediction errors of 1.7 to 1.9% while the prediction error for the optimal broad range calibration (3–74% clay) was estimated as 3.4%. The estimated true accuracy was 1.5 to 1.7% (1–26% clay). By comparison, the reproducibility standard deviation of the reference method was 1.3%. For C, silt, and sand, the prediction errors were 0.42, 4.6, and 5.5% respectively. The ratios between analyte variation range standard deviation and prediction error were 3.1 (1–26% clay), 4.7 (3–74% clay), 2.4 (C), 2.0 (silt), and 2.4 (sand). The results demonstrate that NIR spectroscopy is a potential technique for rapid and cost-effective determination of clay in soils. The technique may also be useful in prediction of other particle-size fractions. The investigated technique was less suitable for determination of total C in Danish soil samples.

Abbreviations: ANN, artificial neural networks • CD, critical differences • CV, cross validation • MLR, multiple linear regression • MSC, multiplicative scatter correction • NIR, near infrared • PLS, partial least square • RMSECV, root mean square error of cross validation • RMSEP, root mean square error of prediction • SD, standard deviation • SD_r, repeatability standard deviation • SD_t, total standard deviation • SEP, standard error of prediction • SEP_true, true prediction accuracy • SNVD, standard normal variate transformation combined with Detrend)

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