## 1 Introduction
Chemometrics enables exploring, analyzing, and modeling relationships among different water quality parameters by various statistical methods [13; 14; 15; 16; 17; 18; 19]. Generally, direct chemometrics models can only be used if all constituents' spectra are known and Beer-Lambert law is valid. However, wastewaters are complex mixtures of various soluble and insoluble organic and inorganic compounds interacting and transforming in time. Therefore, hidden isosbestic points and simple regression models can not completely describe the situation, especially if monitored substances chemically interfere with each other influencing the final spectra [19]. Moreover, specimens from'real-life' sources may contain substances other than those of interest, contributing to the absorbance. Therefore, the use of direct chemometrics methods for wastewater characterization is limited [5; 20]. Indirect chemometrics techniques, such as principal component analysis (PCA), multiple and multivariate regression, partial least square regression (PLS), and others, are based on estimating parameters from sample mixtures. Their are not dependent on the validity of Beer-Lambert law and systems of the even highly correlated spectra can be used for multi-component calibration [14; 18; 21; 22]. Applying these techniques, it is possible to determine the UV/Vis absorption limits for wastewater quality according to the limit values of chemical analytes. Therefore, in our study, chemometric techniques such as principal component analysis (pca) and partial least squares regression and projection to latent structures (PLS) were applied to analyze relationships between water quality chemical parameters (suspended solids (SS), electric conductivity (EC), dissolved organic carbon (DOC), chemical oxygen demand \(\mathrm{COD_{Cr}}\), biochemical oxygen demand (\(\mathrm{BOD_{5}}\))), and UV/Vis absorbance data. The aim was to establish a simple and reliable method for monitoring water quality which, sourced from the root vegetables washing and wastewater treatment processes within the on-farm packhouses. Statistical models can increase the importance and scope of the obtained spectral data [23]. Partial least squares regression (PLS) is a multidimensional statistical method used to predict standard values of aggregated water quality parameters such as total organic carbon (TOC), chemical oxygen demand (COD) and total suspended solids (TSS) [24; 25]. The use of regression algorithms must comply with certain assumptions, i.e., the data should be independent and have a normal distribution. Predictive abilities of PLS calibration models were evaluated in relation to the determination factor (R\({}^{2}\)) [26]. Different statistics were used to assess the PLS model and performance indicators were proposed to classify the developed models. It is essential to consider whether UV/Vis spectra can develop robust TSS and COD estimators, with statistical regression carried out on the off-line spectra in the collected samples. This affects the reliability of the in situ TSS and COD forecast provided by UV/Vis spectrophotometer. Therefore, it is important to test several statistical parameters to evaluate the PLS model [26].