## 4 Discussion
The spikes in the spectrum (see Figure 8), especially in the green and blue spectral ranges, are therefore responsible for the observed misclassifications because the utilized RGB photodiode has some overlapping regions with respect to the sensitivity of the three color channels, especially between the blue and green channels; please see [24] for further details. In order to resolve this issue, the most straightforward approach would be to adjust the output spectrum of the utilized LED by a variation of the applied current to the different dies of the LED to make the reflections from the 3M green foil more distinct again. In combination with our previous work of [15], we can, based on the achieved results, deduct some combinations of light sources with retroreflective foils that can be expected to render good results and combinations that are problematic. As described in [15], when the light source is a white LED, with only limited accentuation of the red spectral range, all types of retroreflective foils yield good results, with the exemption of the two utilized red foils. Therefore, we can follow that when the dominant light spectrum corresponds to a white LED with low accentuation of the red spectral range, only a single red foil should be used. In applications for which the spectral power distribution accentuates the red spectral range (as shown in this work), also the differentiation between different red foils can be achieved. Finally, from the results shown in this work, when the RGBW LED light is combined with the light from fluorescent tubes (at least for fluorescent tubes having spectral power distributions similar to that used in this study), we can deduce the strategy to either exclude the green foil or the blue foil. We also noticed that the ambient light has some effect on the reflections from the utilized mirror element, resulting in a deterioration of the uniqueness of the feature derived from the ME. Therefore, as a countermeasure, we will in the future investigate different geometrical buildups of the ME that better suit the application under ambient light. Nevertheless, we can argue that the 99.41% correct classifications still demonstrate the applicability of the present system in the case that ambient light in the utilized laboratory room is present. The results achieved under the presence of additional ambient light give reason for two conclusions. On the one hand, the spectral composition of the impinging light on the foils has an impact on the achievable classification results. Therefore, in an envisioned application, for example, in a factory, the ambient lighting conditions have to be taken into consideration and adapted if necessary. On the other hand, our work also shows that when the spectral composition of the light is known, the utilizable foils can be selected properly so that foils that tend to cause misclassifications under the known lighting conditions can be excluded. In order to perform the classification without and with ambient light, we generated two different models of the random forest. We chose this approach to illustrate that the random forest models can be successfully created and applied, even though the lighting conditions changed largely. Additionally, we also pointed out how our system can be used to determine which of the models should be applied preferably, based on performing the ambient light determination from the data for which it is known that the train does not cause meaningful reflections toward the VLS unit. In this work, we devised the approach to train two separate models, one for the setting when only the LED of the VLS unit is on and the second in case that the LED and the ambient light (fluorescent tubes) of the laboratory room are switched on. In Section 3.4, we showed that these two settings can be clearly distinguished from each other and that, consequently, the correct model can be chosen to perform the classification.