## 2 Materials and Methods  
In this study, standard grade reagents purchased from Sigma Aldrich were used to prepare known concentration (i.e., 0, 2.5, 5, 10, 15 and 30 mg L\({}^{-1}\) of Ag, Cd, Cu, Pb and Zn) calibration solutions. Each respective heavy metal standard used in the calibration models was replicated 5 times. The salts used were lead nitrate (Pb(NO\({}_{3}\))\({}_{2}\)) for preparing lead; cadmium sulphate (3 CdSO\({}_{4}\), 8H\({}_{2}\)O) for cadmium; copper sulfate (CuSO\({}_{4}\), 5H\({}_{2}\)O) for copper; zinc sulphate (ZnSO\({}_{4}\), 7H\({}_{2}\)O) for zinc; and silver nitrate (AgNO\({}_{3}\)) for silver. A Thermo Scientific Nicolet iS50 FT-IR spectrometer was used to measure the standards for the absorbance calibration methods. The spectral analyses were done using the Attenuated Total Reflectance (ATR-FTIR) technique. Each metal ion calibration model was constructed from 35 spectral scans. A total of 175 spectral scans were run for all the models. Approximately a milliliter of each of the heavy metal solutions was pipetted and placed on the built-in diamond iS50 ATR sampling station. This direct measurement technique enables propagation of the infrared radiation to pass at an angle that allows internal reflectance from the crystal. The crystal was cleaned with alcohol (70% Acetone) and de-ionized water following each successive spectrum sampling. Prior to analysis, the spectrum for air was used as a background in the technique. In the study, spectra wavelength was obtained for the mid-infrared range (400-4000 cm\({}^{-1}\)). The relationship of the heavy metal concentrations and the spectral properties of the solutions was obtained with multivariate calibration methods. The spectral data were calibrated with TQ-Analyst 9 software (Thermo Fisher Scientific Inc., Madison, WI, USA, 2017). Quantitative analysis methods of Principal Component Analysis (PCA) and Partial Least Square (PLS) were used. The data was transformed by smoothing using the Savitzky-Golay filter algorithm with a third order polynomial. PLS and PCR methods were preferred due to their ability to use the intensities of all data points within specified regions in the analysis [21, 22, 23]. These techniques should include at least three calibration standards for each component being measured and additional validation standards. Although the two methods are similar, the PCR method can also use multiple regions of the spectrum. In addition, all of the components in a PCR method can be measured in each region. The PLS method is capable of quantifying sample components when the correlation between concentration and absorbance is very complex. Furthermore, overlapping component peaks can be reduced with the PLS method. It is more suitable in cases where chemical interactions between components cause peaks in the mixture spectrum to shift or broaden. It is also useful where additional components whose concentrations are unknown may be present in the sample mixture [21, 22, 23]. The PCA and PLS models were determined based on similar Cross-Validation (CV) following the methods by [PERSON] et al., [8] and [PERSON], [24]. The correct PCA and PLS models were characterized with a goodness-of-fit to the data used in the construction and to ensure good prediction capability for new variables. Linearity of the calibration models was expressed by the coefficient of determination (R\({}^{2}\)) and goodness of fit [25]. If R\({}^{2}\) is closer to or further from 1, the correlation is higher or lower, respectively.