# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 1 Introduction
[14] used Mid-IR and the DRIFT mode in combination with artificial neural network (ANN) models to assess lead concentration in soil samples collected from an urban landfill agricultural site. Infrared spectroscopy has shown great potential for use in the analysis of different soil properties and contamination levels, but the problem of a choice of the optimal measurement methodology remains unresolved. The choice of the infrared range is important problem in the area of spectral soil analyses. Equally important question refers to the important issue of representativeness of samples for spectral analyses and comparison of the measurements in the laboratory after sample preparation or directly in the field (in-situ). Other issues relate to the choice of spectral techniques, the impact of the variation of the material studied on the results, or the choice of chemometric techniques to create a prediction model. It was indicated that spectral analysis in the mid infrared region enables one to obtain more accurate models than in the near infrared region [15]; however, visible and near infrared radiation (VIS-NIR) has been usually considered for application in on-line measurements. [PERSON] et al. [16] presented an application of a diffuse reflectance mode and PLS calibration model in the on-line measurement of moisture content, soil organic carbon, pH, and total nitrogen. The specially designed on-line measurement system was mounted onto a tractor, with spectral data collected while moving across the landscape with a velocity of 2 km/h. [PERSON] et al. [17] used near-infrared reflectance spectroscopy for the estimation of nitrogen and organic matter content in soil samples. The authors indicated that this is a useful rapid technique for in situ application, which can be combined with GIS. Additionally, [PERSON] et al. [18] used near-infrared spectroscopy for a rapid analysis of organic carbon and nitrogen within a single field. This method was useful for analyzing carbon content in a range of 0-40 mg/g, however, the authors reported that they did not succeed in determining the nitrogen concentration. On the other hand, recent technological development of semiconductor technologies contributes in the development of mid-infrared (mid-IR) portable spectrometers. [PERSON]. [19] compared the application of near and mid infrared in soil analyses and their related advantages and disadvantages. They also considered several questions concerning laboratory and on-site analysis, the effect of moisture and particle size on accuracy, and the effect of different soil types and compositions on calibration. He stated that DRIFT is not useful in on-site application but is advantageous in the laboratory after sample drying and grounding. [PERSON] et al. [20] described an exemplary procedure of sampling for application of mid-infrared diffuse reflectance Fourier transform (MIR-DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties. After removal of the debris from the surface (twigs, litter, and rocks), the samples were taken from the surface to a depth of 20 cm, then for analysis were dried at 40 \({}^{\circ}\)C, and ground by a mill and sieved to \(<\)100 \(\upmu\)m particle diameter. The authors collected 198 samples, and to obtain each representative sample, three subsamples were collected and mixed. Application of different techniques of mid infrared spectroscopy: diffuse reflectance spectroscopy, attenuated total reflectance spectroscopy, and Fourier transform infrared photoacoustic spectroscopy (PAS) for investigation of soil properties was analyzed and compared by [PERSON] et al. (2018). Raw spectra are often processed before use as an input into a chemometric model development using various techniques, such as absorbance transformation log (1/R), mean centering, detrend, standard normal variate (SNV), Savitzky-Golay smoothing, first or second derivatives, and multiplicative scatter correction (MSC) ([PERSON] et al., 2016; [PERSON] et al., 2017).