Next, we compared the CRF and RRF-BP to further investigate the effectiveness of the RRF concept. The RMSE and R 2 -values of CRF/RRF-BP were 0.55/0.49 and 0.32/0.47 in adipose, 0.57/0.50 and 0.38/0.52 in brain, 0.42/0.37 and 0.69/0.76 in gut, 0.37/0.34 and 0.61/0.64 in heart, 0.40/0.34 and 0.64/0.75 in kidney, 0.52/0.46 and 0.38/0.52 in liver, 0.46/0.42 and 0.71/0.75 in lung, 0.34/0.34 and 0.67/0.70 in muscle, 0.33/0.29 and 0.37/0.51 in skin, respectively. This proves the superiority of RRF-BP over CRF or other methods. The similar performance of RRF and CRF shows the importance of selecting the best parameters, and it could be derived from the features of each tissue, which are similar to some other specific tissues. The RRF-BP addresses the issue of overfitting by selecting the best parameters to reduce the number of explanatory variables. However, it is important to note that we were unable to create a hold-out test dataset due to the lack of Kp data in all tissues. . Consequently, it should be noted that we are unable to demonstrate the generalizability of the RRF-BP method. To achieve this, we would need to obtain a larger experimental dataset.