## 4 Discussion
### Visualization of Tissue Water Content in Kidneys  
We demonstrated a pixel-by-pixel display of TWC values from the kidney ex vivo using HSI. This visualization offers the possibility of locally resolving tissue-relevant changes. Additionally, the HSI allows the simultaneous acquisition of further tissue parameters, such as the oxygen saturation of the tissue [25].  
A perspective relevance of this monitoring scenario, is that the physicians receive additional visual information about the organ, which, in the context of NMP, for example, could allow a better assessment of the organ quality and the indication for therapy adjustment.  
A limitation of our study is the usage of only 23 kidneys from German Landrace pigs, to predict the TWC in kidneys. Future research should include a higher amount of test subjects as well as pigsfrom other races or origin (e.g., laboratory surgery pigs) to ensure the general validity of our method. To show the effectiveness of the proposed approach, other multivariate data analysis methods could be considered. Furthermore, the presented algorithm could be transferred to other organs under the condition of a suitable calibration model.  
In summary, HSI in the wavelength range from 500 to 995 nm was used to predict the TWC in pig kidneys. After data preprocessing, PLSR prediction models were created and compared to choose the best model. Furthermore, the visualization of the spatial distribution of TWC was realized. Knowledge of tissue-specific parameters could provide a promising opportunity to investigate assessment tools of organ grafts during the preservation period.  
Conceptualization: [PERSON]; methodology: [PERSON]; software: [PERSON], J.L., and P.F.; validation: [PERSON], [PERSON], and P.F.; investigation: [PERSON], [PERSON], and P.F.; data curation: [PERSON], [PERSON], and [PERSON]; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; visualization: [PERSON]; supervision: [PERSON] and [PERSON]; funding acquisition: [PERSON] and C.T. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the European Social Fund and co-financed by tax funds based on the budget approved by the members of the Saxon State Parliament.  
The authors would like to thank [PERSON], [PERSON], and [PERSON] for the implementation of early versions (not presented in this manuscript) of segmentation algorithms. The authors highly appreciate the support of [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AOAC & Association of Official Analytical Chemists \\ ASTM & American Society for Testing and Materials \\ FOV & Field of View \\ HSI & Hyperspectral Imaging \\ MSC & Multiplicative Scatter Correction \\ NIR & Near-Infrared \\ NMP & Normothermic Machine Perfusion \\ PLSR & Partial Least Squares Regression \\ RMSECV & Root-Mean-Square Error resulted from Cross-Validation \\ RMSEP & Root-Mean-Square Error of Prediction \\ SG & Savitzky-Golay \\ SNV & Standard Normal Variate \\ TWC & Tissue Water Content \\ TWI & Tissue Water Index \\ VIS & Visible Light \\ \end{tabular}