Abstract
Presented paper describes spectroscopic dataset and calibration models database of near infrared spectroscopy (NIRS) used to predict agricultural soil fertility properties. Near infrared spectra data in form of absorbance spectrum were acquired in wavelength range from 1000 to 2500 nm for a total of 40 bulk soil samples amounted of 10 g per each bulk. Soil fertility properties, presented as soil nitrogen (N), phosphorus (P). potassium (K), soil pH, magnesium (Mg) and calcium (Ca), were measured by means of wet chemical analysis. Calibration models, used to predict those soil fertility parameters were developed using two different regression algorithms namely principal component regression (PCR) and partial least square regression (PLSR) respectively. Prediction performance can be evaluated and justified by looking their statistical indicators: correlation of determination (R2), correlation coefficient (r), root mean square error (RMSE) and residual predictive deviation (RPD). Spectra data can also be corrected in order to improve and enhance prediction performance. Obtained NIRS dataset and models database can be used as a rapid and simultaneous method to determine agricultural soil fertility properties.
Highlights
Presented paper describes spectroscopic dataset and calibration models database of near infrared spectroscopy (NIRS) used to predict agricultural soil fertility properties
Near infrared spectra data in form of absorbance spectrum were acquired in wavelength range from 1000 to 2500 nm for a total of 40 bulk soil samples amounted of 10 g per each bulk
The light source of halogen lamp irradiated soil samples from down to up through a quartz window (1 cm of diameter), which was embedded in the top of the NIR instrument
Summary
Near infrared spectra data of soil samples were acquired and measured using a benchtop NIR instrument (Thermo Nicolet Antaris II) with an integrating sphere accessory. The instrument was controlled and configured under integrated software namely Thermo Integration® and Thermo Operation®. Specified tasks for spectra data acquisition were performed by establishing workflow using Thermo Integration software [10]. High resolution measurement with integrating sphere was chosen as a method for spectra acquisition. Sample labelling was required automatically prior to spectra data collection to differ soil samples respectively
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