# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 3 Results
### Qualitative Description of FT-IR Spectra
Moreover, the presence of metals in a soil usually does not result in the appearance of new peaks in the spectrum. Thus, the direct calibration and quantitative analysis of the metal concentration on the basis of the IR spectra is impossible. However, metals impact the IR spectra of soils and observed spectral changes can be used to measure metal concentration with the support of chemometric methods. The presence of complexes of functional groups, especially complexes containing oxygen with metal ions, causes frequency shifting and changes in the intensity and shape of bands, which can be used in the analysis of the metal concentration in soil samples. Metal bonding in organometallic compounds weakens the bonds and results in a lowering of the wavenumber value compared to the free groups. The crystal structure of the inorganic compounds can be affected in the presence of heavier cations, causing changes in the far IR. The PLS method enables one to investigate the relationship between the metal concentration in soil samples and the measured FT-IR spectra. Figure 4: Exemplary (**a**) near infrared (NIR) and (**b**) far infrared (FIR) spectra. Figure 6: The PLS model describing the relationship between the metal content in soil and spectral data measured using the DRIFT in the MIR. Figure 7: The PLS model describing the relationship between the metal content in soil and spectral data measured using the Near Integrated Sphere and DRIFT technique in the NIR. The constructed PLS models were analyzed in terms of fit and prediction abilities. The constructed models describing the relationship between the concentration and DRIFT spectra in the mid IR region (Figure 6) were characterized by a good fit for cobalt, chromium, mercury, and nickel. Unfortunately, the constructed PLS models for molybdenum, tin, arsenic, copper, and linear correlation were characterized by a weak fit and weak prediction abilities. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c} \hline \hline
**Metals** & **Unit** & **Range** & \begin{tabular}{c} **Number** \\ **of** \\ **Variables** \\ \end{tabular} & **RMS** & **RMSCV** & \
\begin{tabular}{c} **\%** \\ **RMS** \\ \end{tabular} &
\begin{tabular}{c} **\%** \\ **RMSCV** \\ \end{tabular} \\ \hline Co & mg/kg & [1.00,27.00] & 5 & 3.5826 & 4.1119 & 13.78 & 15.82 \\ Cr & mg/kg & [4.10,223.00] & 4 & 25.5573 & 31.5102 & 11.68 & 14.39 \\ Mo & mg/kg & [1.00,14.00] & 4 & 1.9542 & 2.2132 & 15.03 & 17.02 \\ Sn & mg/kg & [1.00,12.00] & 5 & 1.8397 & 2.1310 & 16.72 & 19.37 \\ Zn & mg/kg & [1.00,1530.00] & 5 & 183.5097 & 244.4817 & 12.00 & 15.99 \\ Ba & mg/kg & [22.00,2120.00] & 5 & 293.3098 & 351.6731 & 13.98 & 16.76 \\ Hg & mg/kg & [0.01,3.68] & 10 & 0.2729 & 0.3954 & 7.44 & 10.77 \\ As & mg/kg & [4.00,620.00] & 4 & 66.5027 & 73.4838 & 10.80 & 11.93 \\ Cd & mg/kg & [1.00,7.70] & 7 & 0.9147 & 1.1523 & 13.65 & 17.20 \\ Cu & mg/kg & [22.00,2840.00] & 4 & 550.7588 & 601.2048 & 19.54 & 21.33 \\ Ni & mg/kg & [3.10,67.00] & 5 & 6.0862 & 6.9554 & 9.52 & 10.88 \\ Pb & mg/kg & [18.00,2840.00] & 4 & 337.6865 & 368.6658 & 11.97 & 13.06 \\ Se & mg/kg & [2.00,7.00] & 6 & 0.6485 & 0.7706 & 12.97 & 15.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The root mean square error (RMS) and root mean square error of cross validation (RMSCV) values for the PLS model based on ATR data in the FIR.