Collection of soil spectral data in MIR range
Air-dried, crushed and 0.5 mm ground samples, mentioned earlier, were filled in the cups meant of the Bruker alpha Fourier Transformed MIR Spectrometer for recording spectral signatures. The FT-MIR was stabilized for two hours to increase the amplitude count to more that 9000 and corrections were made for the background during instrument calibration, before recording spectra in the near and middle MIR range. For the Vis-NIR range, a spectroradiometer (Model: FieldSpec3 FR; Analytical Spectral Devices Inc., USA). The raw spectra collected from the Vis-NIR range ASD FieldSpec® and OPUS file from and MIR-spectrometer respectively were subsequently pre-processed using R software (version 3.3.3, The R Foundation for Statistical Computing Platform). The most widely used pre-processing techniques is divided into two categories: scatter-correction methods and spectral derivatives. Since many workers are of the openion that the soil properties can be related to absorbance and reflectance and their first and second derivatives and it has been reported that the absorption features in reflectance spectra were enhanced by derivative spectroscopy (Tsai, 1998), the reflectance data was transformed to absorbance through the expression, absorbance=log10 (\(\frac{1}{\text{Reflectance}}\)).
First and second derivatives were obtained from reflectance and absorbance data. The spectral derivative method consists of first derivatives (FD) and second derivatives (SD) of the reflectance spectrum using the equation (1) and (2) respectively.
\begin{equation} \frac{\mathbf{\text{dR}}}{\mathbf{\text{dλ}}}\mathbf{=R}\left(\mathbf{\text{λi}}\right)\mathbf{-}\frac{\mathbf{R}\left(\mathbf{\lambda i-1}\right)}{\mathbf{\lambda i-\lambda i-1}}\mathbf{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ldots\ldots\ldots(1)\ }\nonumber \\ \end{equation}\begin{equation} \frac{\mathbf{d}^{\mathbf{2}}\mathbf{R}}{\mathbf{d}\mathbf{\lambda}^{\mathbf{2}}}\mathbf{=}\frac{\mathbf{d}}{\mathbf{d}\mathbf{\lambda}}\left(\frac{\mathbf{\text{dR}}}{\mathbf{d}\mathbf{\lambda}}\right)\mathbf{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ldots\ldots..(2)\ }\nonumber \\ \end{equation}
Where R, spectral reflection/absorbance; ʎi, ithwavelength / band. Further among scatter-correction methods, multiplicative scatter correction (MSC), standard normal variate (SNV) and other smoothing methods include averaging spectra, and median filters, first order derivative, second order derivative and the Savitzky–Golay transformation were used to reduce noise in spectral signals.