## 4 Discussion
### Partial Least Square Regression for Prediction of the TWC in Kidneys  
In this study, PLSR analysis was used to quantify the TWC by simulating an application during normothermic preservation of ex vivo kidneys. In medical application, PLSR has not yet been used to determine the TWC. Here, we proposed the suitability and effectiveness of this analytical tool to predict the TWC in kidney tissue during a non-invasive application. For a TWC range between 44.27% and 89.27%, physiological and non-physiological water-related tissue characteristics can be represented. Figure 9: TWC distribution map of a kidney during NMP. An important aspect of predictive model development is a suitable dataset. Two main aspects were considered. On the one hand, differences in the chemical composition of the samples, such as hemoglobin and fat, can affect both tissues scatter and spectral characteristics. For this reason, a calibration model was developed using kidney samples that properly reflect the application scenario. On the other hand, a similar distribution of TWCs between the training and test set is essential for model development. To predict all possible TWC values, the models' training input should cover the full value range. Additionally, the test set to which the model is applied requires a similar distribution of TWCs to reflect a realistic assessment of the models' performance. Here, we used PLSR for predictive model development. The reasons for using this method were, on the one hand, that it is a commonly used and reliable multivariate data analysis method for predictive model development [46]. On the other hand, in studies evaluating meat samples, the quantitative relationship between the spectral data of the samples and their reference TWC values was determined mainly by PLSR analysis [37; 47; 53]. Other methods whose performance has been tested exclusively against PLSR are multiple linear regression, random forest, or support vector machines regression. In this study, PLSR was chosen because it is a reliable analysis tool for data processing of spectra and showed a better performance compared to the methods mentioned above [38; 49; 52]. In this study, two categories of PLSR models were built. The spectra utilized for multivariate data analysis included both full-wavelength spectra and simplified spectra selected by \(\beta\)-coefficient analysis. In addition, various preprocessing techniques were tested to evaluate the effect on model performance for TWC prediction. Using the full spectral range, the best model based on absorbance spectra (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.969, RMSEP of 2.016) showed approximately the same performance as the model based on reflectance spectra (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.963, RMSEP of 2.155). The performance of the TWI calculation method for medical applications given in Equation (1) is not specified [27], which does not allow any comparison. Therefore, results from porcine meat analysis were considered as an alternative. Based on \(\text{R}_{\text{P}}\)\({}^{2}\) and RMSEP, the results were better or comparable to those of previous studies predicting TWC using HSI and PLSR in pork (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.91) [40], in pork longissimus dorsi muscles (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.952 and RMSEP of 1.396) [37], and in pork longissimus dorsi muscles during salting process (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.941 and RMSEP of 1.23) [49]. When using a simplified spectral range of 37 wavelengths, the performance of the models deteriorated moderately compared to the models developed with the full spectral range of 100 wavelengths. The best model based on absorbance spectra (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.925 and RMSEP of 3.075) showed a slightly worse performance than the best model based on reflectance spectra (\(\text{R}_{\text{P}}\)\({}^{2}\) of 0.941 and RMSEP of 3.202). One possibility for the deterioration of the results is the lack of information in the specified optimal wavelength range.