BRANNLP had a slightly higher predictive power than MLREM, with R 2 of 0.92 versus 0.87, and q 2 of 0.88 versus 0.81. Meanwhile, BRANNLP showed lower SEE value (Table ). For the prediction of Φ, BRANNLP and MLREM gave very similar results for the prediction of the test set (with SEP of 0.037 and 0.039, respectively). The experimental Φ values were less uniformly distributed than the other properties, which may affect the accuracy of the models. Clearly, the BRANNLP models for the CIE coordinates of the polymers were superior to those generated by the linear MLREM algorithm (Figures and). The test set SEP values for BRANNLP's prediction of CIE x was 0.059, which was nearly half of the SEP for the MLREM model (0.114). The SEP value for the test set predictions of the CIE y by the nonlinear BRANNLP model was 0.085, as opposed to 0.150 for the linear MLREM model. In general, the MLREM models were sparser or of similar sparsity compared to the BRANNLP models. MLREM used fewer descriptors and had smaller numbers of effective parameters (e.g., 9 in MLREM versus 19 in BRANNLP for predicting λem). Overall, both models were considered sufficiently reliable to provide useful estimates of the PL properties of polymers, with a caveat on Φ. MLREM and BRANNLP selected the descriptors most pertinent to the PL properties (Tables and). The model regression coefficients provided the sign and magnitude of the contributions of the sparse sets of descriptors to the respective PL property models (Figure ). Interpretation of the descriptors selected by sparse BRANNLP was more difficult due to the interactions between them and the nonlinearity of the QSPR response surface (the multidimensional surface describing the relationships between the descriptors and the modelled PL property).