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Yi Peng

Comparing predictive ability of laser-induced breakdown spectroscopy to visible near-infrared spectroscopy for soil property determination

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Soil organic carbon (SOC) and particle size fractions have a practical value for agronomy and the environment. Thus, alternative techniques to replace the expensive conventional analyses of soil are needed. Visible near-infrared reflectance spectroscopy (viseNIRS) has already shown potential for becoming an alternative method for soil analysis since it is faster and cheaper than conventional methods. Laser-induced breakdown spectroscopy (LIBS) is another cost-effective technique with potential for rapid analysis of elements present in the soil. In this study, the feasibility of using LIBS to determine SOC, clay, silt and sand contents of Danish agricultural soils was tested and compared with the viseNIRS method. First, country-scale Partial Least Squares (PLS) regression models on soils collected across Denmark (N ¼ 78) were built and validated using independent field samples (N ¼ 54). Secondly, the country-scale calibration data set was spiked with 14 representative samples from the fields and validated with the 54 field samples. Generated country-scale LIBS models exhibited similar and not significantly different (p > 0.05) results to viseNIRS for all soil properties except a significantly higher (p ¼ 0.0305) predictive ability for sand. Spiking improved the accuracy of most of the LIBS and viseNIRS models, indicating the importance of similarities between the calibration and the validation data sets. No significant differences (p > 0.05) were found between the LIBS and viseNIRS spiked country-scale models. Lower prediction errors for most properties were obtained using LIBS, rendering it an equally good or even a more accurate technique for soil properties determination than the well-established viseNIRS method.
Original languageEnglish
JournalBiosystems Engineering
Volume156
Pages (from-to)157-172
Number of pages16
ISSN1537-5110
DOIs
Publication statusPublished - 2017

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