## 4 Results
Furthermore, each model is fitted with a different number of components and thresholds, with the levels that result in the best performance on test data being labeled as optimal. The RMSE value reflects the model's performance. We used a response surface methodology-based curve that results in optimal model parameter levels to achieve a systematic optimal level of model parameters. Figure 3 shows one of the cases from all fitted models. All reference and proposed methods predict ESR products by using the optimal threshold and optimal number of components. The RMSE over training and test data is computed, and the results are shown in Figure 4. The average RMSE on training data for predicting CO conversion (%) with cube morphology is around 1.04 and is 1.03 on test data. On training data, \(PLS_{C}\) has the lowest RMSE, whereas on test data, \(PLS_{VC}\) has the lowest RMSE. The average RMSE on training data for predicting CO\({}_{2}\) yield with cube morphology is around 1.035 on training data and 1.019 on test data. On training data, \(PLS_{C}\) has the lowest RMSE, whereas on test data, \(PLS_{WC}\) has the lowest RMSE. The average RMSE on training data for predicting H\({}_{2}\) conversion (%) with cube morphology is around 1.014 and is 1.002 on test data. On training data, \(PLS_{C}\) has the lowest RMSE, while on test data, \(PLS_{VC}\) has the lowest RMSE. The average RMSE on training data for predicting CO conversion (%) with polyhedra morphology is around 1.035 on training data and is 1.02 on test data. On training data \(PLS_{C}\) has least RMSE, while on test data \(PLS_{WC}\) has least RMSE. The average RMSE on training data for predicting CO\({}_{2}\) yield with polyhedra morphology  
Figure 2: The interpolation of CO conversion (%), CO\({}_{2}\) yield, and H\({}_{2}\) conversion (%) using a polynomial equation of degree two is demonstrated for cube morphology. is around 1.011 on training data and is 1.01 on test data. The training data all have similar RMSE, while the test data \(PLS_{VC}\) have the least RMSE. The average RMSE on training data for predicting H\({}_{2}\) conversion (%) with polyhedra morphology is around 1.035 on training data and is 1.022 on test data. On training data \(PLS_{C}\) has the least RMSE, while on the test data \(PLS_{WC}\) has the least RMSE. The average RMSE on training data for predicting CO conversion (%) with rod morphology is around 1.035 on training data and is 1.012 on test data. On training data \(PLS_{VC}\) has least RMSE, while on test data \(PLS_{WV}\) has the least RMSE. The average RMSE on training data for predicting CO\({}_{2}\) yield with rod morphology is around 1.05 on training data and is 1.01 on test data. On training data \(PLS_{C}\) has least RMSE. The average RMSE on training data for predicting H\({}_{2}\) conversion (%) with rod morphology is around 1.02 on training data and is 1.01 on test data. On training data \(PLS_{C}\) has least RMSE, while on the test data \(PLS_{WV}\) has the least RMSE. According to the comparison above, the Johnsen index-based \(PLS\) variants outperform the test data in predicting ESR products with different morphologies. Furthermore, it has been discovered that ESR product prediction on test data is superior to prediction on training data. Notably, the FTIR spectra used for modeling is baseline corrected. Baselines are frequently set based on a visual examination of their effect on specific spectra. It offers a more objective process for selecting baseline correction algorithms and their parameter values for use in statistical analysis. For good prediction, the spectra must be objective and reproducible if they are to be employed in a statistical analysis. We have used asymmetric least square (ASL) [30], where parameters are tuned for optimal ESR product prediction. For this, we used the original, and the corrected FTIR spectra used in polyhydra is presented here for illustration in Figure 5. The best model for each ESR product prediction is then used to determine the influential wavenumbers.