Soil Quality Assessment By Near Infrared Spectroscopy: Predicting Ph And Soil Organic Carbon

The main objective of this present study is to apply near infrared reflectance spectroscopy (NIRS) combined with multivariate analysis in predicting soil quality attributes rapidly and simultaneously. Those quality attributes are: pH and soil carbon organic (SOC). Near infrared spectra data, in form of absorbance spectrum were acquired for a total of 10 bulk soil samples amounted 55 g per bulk. Spectra data were acquired and recorded in wavenumbers range from 4000 to 10 000 cm-1 with number of scans is set to 64 scans respectively. On the other hand, actual soil quality attributes (pH and SOC) were measured by means of standard laboratory methods. Prediction models were developed and established using principal component regression (PCR) and partial least square regression (PLS) followed by leave one out cross validation (LOOCV) method. The results showed that all quality attributes can be predicted using NIRS in tandem with PCR and PLS with maximum correlation coefficient between predicted and measured parameters are: 0.97 for both pH and SOC quality parameters. Moreover, robustness index for pH and SOC prediction are 4.60 and 4.67 respectively, which referred as excellent prediction performance. It may conclude that NIRS can be used to assess soil quality attributes rapidly and simultaneously.

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