## 4 Experimental Studies  
In order to verify the proposed Autostereoscopy-Raman Spectrometry-based measurement methodology and its associated measurement system, which is shown in Figure 5. As for the sample preparation for the verification of the proposed method, we specifically construct a multi-layer-structure and put three different kinds of objects between the cover glass and the substrate glass slide as test samples, including non-woven fabric from shoe covers, non-woven fabric from masks, meltblown fabric and anti-static clothing fibers, as show in Figure 6. Figure 6a is the first sample, which contains anti-static clothing fibers, non-woven fabric from shoe covers, and non-woven fabric from masks. The depths of the three targets are 40 microns, 80 microns and 120 microns, respectively. Figure 6b is the second sample, we replaced the fiber of the second layer with meltblown fabric and changed the depth of the target. The new depths were 50 microns, 80 microns and 110 microns. The meltblown fabric very different from other fibers, as vividly shown in Figure 6b, which can further verify the robustness of our method. The design of the target sample is to simulate the pollutants attached between glass interlayers which is especially similar to the industrial manufactured sample. Figure 5: The Autostereoscopy-Raman Spectrometry-based measurement system. We simulated the contaminants in different thicknesses of glass interlayers by artificially locating them at different depths away from focal plane of the objective in order to provide the true value of depth, which can be used as reference data of experimental studies. To imitate the structure of the first sample, we artificially located the three targets at 40 microns, 80 microns and 120 microns away from the focal plane by moving the z axis, which has repeatability of 0.1 microns. For the second sample, the related offsets are 50 microns, 80 microns and 110 microns, respectively. Figure 7a,d,g are 2D images of anti-static clothing fibers, non-woven fibers from masks and non-woven fibers from shoe covers captured by a Nikon DS-Fi3 microscope camera under a 20\(\times\) microscope. Since some of the CCD pixels are invalid, we do a basic cropping of the image. Figure 7b,e,h are the elemental images of the above-mentioned fibers, and Figure 7c,f,i are the reconstructed images. Figure 8 shows the 2D images, EIs and reconstructed images of the second sample. By processing the data via the purposely developed reconstructed algorithm and image evaluation function, we will get the sharpness evaluation function graph as shown in Figure 2 to roughly locate the spatial position of the target. Usually, there will be multiple peaks regarding different depths. We traverse these peaks and collect the associated Raman spectrum. As shown in Figure 2, Raman spectrometry signals with high signal-to-noise ratio are often obtained at the noise peaks, but they are mistaken for focal depth. Raman spectrometry signal can provide additional information to rule out the false peaks as noise. Then, the proposed ARS system is able to get both accurate location information and material composition information simultaneously. Figure 6: Structure of the sample, (**a**) the first sample and (**b**) the second sample. As shown in Figure 9, the spatial positions of the six different types of fibers from the two samples are reconstructed by the autostereoscopic module of the proposed ARS system and the sharpness function from 0 \(\upmu\)m to 150 \(\upmu\)m are plotted accordingly. It can be seen that due to the calibration error of the autostereoscopic module, the calculated position is different from the actual position. The robustness of the main peak as a condition to determine the depth position is not high, and there are some other peaks around the main peak, and the peak of the correct spatial positions may be located among them.