## 3 Results and Discussion
### Multivariate Calibration  
The potential of vis-NIR reflectance spectroscopy to forecast the chemical parameters under investigation is summarised in Table 5.  
The best results were obtained by pre-processing the spectra prior to data analysis using one or more of the following procedures (Table 5): reflectance (R) to absorbance (A) transformation (A = log 1/R), multiplicative signal correction (MSC) [37], first derivative transformation, median and Savitzky-Golay filtering [41], mean centring. Multiplicative signal correction (MSC) corrects for light scattering variations. Both median and Savitzky-Golay filtering, which were carried out prior to the first derivative transformation, reduce the effects of spectral random noise, thereby providing smoother spectra.  
PLSR applied to the available sample set provided some good correlations between soil spectra and soil variables (Table 5). The best predictive models were for EOX, PCBs\({}_{18}\), Penta-CB, and Hepta-CB. Figure 6 displays scatterplots of the predicted vs. the measured values for these variables. Excellent models were calibrated for EOX (R\({}^{2}\)adj = 0.909; RPD = 3.40), PCBs\({}_{18}\) (0.911; 3.47), and for Hepta-CB (0.897; 3.24), while a very good model was calibrated for Penta-CB (0.798; 2.27). For all these four variables (Figure 6a,b,d,f), the measured vs. the PLRS predicted values approximated closely the 1:1 line, with slight underestimations at the higher values and slight overestimation at the lower values. The better approximation to the 1:1 line was for EOX, followed by PCBs\({}_{18}\) and Hepta-CB. The deviation from the 1:1 line was slightly higher for the Penta-CB.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Spectra Pre-Processing** & **R\({}^{2}\)** & **RMSE** & **RPD** & **F** \\ \hline EOX & MSC, SG, I der., mean c. & 0.909 & 0.481 & **3.40** & **4** \\ PCBs\({}_{18}\) & MSC, SG, I der., mean c & 0.911 & 0.594 & **3.47** & 3 \\ Tri-CB & & no models possible & & & \\ Tetra-CB & log 1/R & 0.449 & 0.470 & 1.40 & 3 \\  & log 1/R, med., I der. & 0.798 & 6.073 & **2.27** & 2 \\ Hexa-CB & log 1/R, mean c. & 0.576 & 3.739 & 1.59 & 2 \\  & MSC, med., I der., mean & & & & \\ Hepta-CB & c. & 0.897 & 4.190 & **3.24** & 2 \\ \hline \end{tabular} In bold are the best predictive models. F = number of the PLSR factors used in the model; RPD = Relative Percent Deviation; RMSE = Root Mean Squared Error.  
\end{table}
Table 5: Calibration statistics of PSLR models for PCB and EOX contents in the soil samples.  
Based on the RMSE and AIC values, four factors were necessary to calibrate the best model for EOX, three factors for PCBs18, and two factors for both Hepta-CB and Penta-CB. The optimal combination of pre-processing procedures included (i) MCS, Savitzky-Golay filtering, first derivative transformation, and mean centring of spectra for EOX and PCBs18; (ii) MCS, median filtering, first derivative transformation, and mean centring of spectra for Hepta-CB; and (iii) R to log 1/R transformation, median filtering, and first derivative transformation for Penta-CB.  
Predictive models were poor (R\({}^{2}\)\({}_{\text{adj}}\) = 0.449, RPD = 1.40) for Tetra-CB and fair (0.576, 1.59) for Hexa-CB (Figure 6c,e; Table 5). For these two variables, the PLSR prediction deviated noticeably from the 1:1 line (Figure 6c,e). It was possible to calibrate any model for Tri-CB.