## 2 Materials and Methods
### Parameters, Materials and Hardware
Concerning the utilized hardware, we reused our self-developed VLS unit as described in [14, 15], with the one exception that we changed the light source from a CREE MC-E white LED to a CREE MC-E RGBW LED [23]. Since the footprint on the PCB for the new light source is identical to the previously used LED, this adaption was doable without any major effort. The change of the light source and consequently the change in the spectral power distribution of the emitted light is one of the main solution approaches in order to enhance the number of correct classification results of the different retroreflective foils, especially in regard to the two red foils (3M red and Orafol red), which showed to be the most problematic ones in our previous work. We discuss this in detail in Section 2.6 in this manuscript. We supplied each of the dies of the new CREE MC-E RGBW LED separately with 3.1 V and 150 mA, using a laboratory power supply. Concerning the sensing device, a Kingbright KPS-5130 PD7C [24] RGB sensitive photodiode, the VLS unit remained unaltered. This photodiode has a common cathode and three anodes, which correspond to the different spectral ranges of red, green and blue. We hereafter refer to them as the red channel, green channel and blue channel. Each of these channels is interfaced with a separate transimpedance amplifier (TIA) that converts the photocurrent of the channel into a voltage signal, which consequently can be easily sampled by an analog-to-digital converter (ADC). Please note that due to the internal buildup of the photodiode and the TIAs, a lower voltage signal at the TIA output corresponds to a higher amount of impinging light in the respective spectral range. Consequently, a voltage value of zero at the output of a TIA correlates to a saturation of the respective channel of the photodiode. Furthermore, also the utilized reflectors over the LED (Ledil--CA10928_BOOM) as well as over the photodiode (Ledil--C11347_REGINA) were kept the same as in our previous works. Additionally, we utilized the same Keysight DSOS404A Digital Storage Oscilloscope, where each of the outputs of the three TIAs were connected to a separate channel of the oscilloscope for data acquisition. In order to be comparable with our previous results, we kept the internal sample rate of the oscilloscope at 5 Ms/s and performed a resampling step in GNU/Octave to emulate a lower sampling frequency of 100 kHz. The overall workflow of data acquisition, signal processing, feature generation and the final classification were done identically to [15]. When the triggering event (see later in Section 2.3) is initiated, the oscilloscope stores the acquired data 1 s before the triggering event and 1 s after the triggering event. This results in 10,000,000 samples for all three color channels, which are stored in a binary file format to form the respective dataset. In the following, these separate binary files are named as runs. An inbuilt function of the Keysight oscilloscope was used for the storing of the dataset to a file. These samples correspond in total to a time period of 2 s. The binary file format was chosen for easy import into the GNU/Octave program. Following the workflow described in Section 3, once all the defined files are generated and stored, they are transferred to a standard laptop for further processing. After import in GNU/Octave, the samples of the three color channels are immediately resampled, which reduces the data to 200,000 samples per color channel. After resampling, also the signal processing and feature generation can be performed in GNU/Octave. Finally, the implemented GNU/Octave script generates a CSV file storing the respective features. Identical to [15], we subsequently utilized the Orange Machine Learning Tool developed by the Bioinformatics Lab at the University of Ljubljana, Slovenia [25], for the generation and testing of the random forest model.