## 3 Results and Discussion
### MEL Production Forecasting: Day 7  
After estimating the MEL production on day 4, the prediction of the concentration of the MELs was carried out on day 7, the last time point common to all of the experiments in the dataset. As shown in Table 4, the NN model demonstrates a superior performance, consistently outperforming the other models. The model achieved the lowest MSE using the feature subset selected by the ANOVA, with an MSE of 1.63 and an MAE of 1.1 g/L, meaning that the average deviation of the predictions from the experimental values was approximately \(1\)\(g_{MELs}/L\). Furthermore, an analysis of Table 5 reveals a prediction with an error greater than 2 g/L which only occurs for the model's prediction of the concentration of sample 19. This sample belonged to experiment 2, which was the only experiment where CW was used as the hydrophilic substrate source, making this condition under-represented in the dataset. As a result, the model had limited examples to learn from during training, which likely affected its ability to generalize to this specific condition. The RF models' performance declined when forecasting the MEL production for day 7, with the lowest MSE achieved being 9.48. Furthermore, the predictions made revealed that the RF model presented only seven distinct values. This lack of variability likely resulted from the RF models' decision trees grouping the testing samples into identical nodes, leading to limited differentiation between the predicted values. This may have occurred because the testing samples were assigned to the same leaf nodes when passing through the trees. The SVM models' performance for day 7 is similar to that for day 4, with high MSEs and a narrow prediction range. The model failed to find the optimal hyperplane for separating the data, even when tested with different kernels. The NN model achieved its best result for both days 4 and 7 when the ANOVA-derived feature subset was used. This method selects features based on significant differences between independent groups, more specifically by calculating the ratio of the variance between and within groups. This approach takes into consideration the target value when applied, which can enhance the NN's ability to learn the patterns within the data. The success of the neural network across this work can be attributed once again to its robustness and flexibility. Finally, Figure 4 reinforces the previous conclusions about the three models but also shows that the NN model struggles to forecast sample 14 and sample 32. Once again, the dataset used in this work comprises four different experiments with different initial conditions, such as different carbon and lipid sources. As a result, some of the initial conditions appear less frequently than others, which impacts the models' performance due to the limited number of examples available for learning during training. For example, sample 14's initial conditions were glucose for the hydrophilic carbon source and rapeseed oil for the hydrophobic source. Rapeseed oil was only used in 6 out of 47 samples, resulting in this hydrophobic source being under-represented in the learning set. The predictions for day 4 were more successful than those for day 7, likely due to the non-linear relationship between the variables and the different ways the MEL production depends on specific features at each time point. For day 4, the MEL production may have been more strongly influenced by a particular feature present in the dataset, simplifying the prediction task. In contrast, by day 7, the relationship between MEL production and the available features may have shifted or become more complex since MELs are secondary metabolites, produced mainly after the primary growth phase, reducing the models' ability to accurately capture the dynamics of this process. Finally, for the forecasting for day 4, the data used were evenly spaced, with a consistent two-day interval.