## 4 Results and Discussion  
Figure 2 shows the 3D printed structures here considered for the prosed unmixing analysis. The average size of the deposited strands was \(352.0\pm 5.8\) um for PCL-GO and \(409.6\pm 5.3\) um for PLA-GO, respectively; further data were previously reported in reference [1].  
Figure 3 shows the Raman spectra of GO powder, neat PCL, PCL-GO, PLA, and PLA-GO 3D samples. GO spectrum is characterized by the two typical peaks at about 1320 cm\({}^{-1}\) (D band) and 1580 cm\({}^{-1}\) (G band), which is clearly identifiable in both spectra acquired from the printed composites.  
Figure 2: Pictures of polycaprolactone-graphene oxide (**a**) and polylactic acid-graphene oxide (**b**) 3D printed structures.  
GO distribution was investigated by scanning a fixed area, randomly selected, on different locations on PCL- and PLA-based composites. Figure 4 shows representative maps considering the intensity peak at 1580 cm\({}^{-1}\).  
For each dataset (PCL-GO and PLA-GO), two different approaches have been carried out for the selection of the endmembers. In the first case, the endmembers have been selected manually from a spectral library, while in the second one the endmembers have been derived from data using the N-FINDR endmember extraction technique. The spectral library considered in this study is composed of a series of Raman measurements of pure materials. In particular, we selected three different measurements of GO powder, and three different measurements of PCL and PLA filaments, respectively.  
Two different metrics have been used to compare the performance of the proposed technique depending on the endmember extraction approach. Regarding the endmember extracted from the data, the assessment has been carried out using the Spectral Angle Distance (SAD) between each extracted endmember and the set of available GO spectral signatures **e** and PCL or PLA. The SAD measures the spectral angle formed by n-dimensional vectors and is defined as follows:  
\[\mathrm{SAD(X(i,j),X(r,s))}=\cos^{-1}\frac{X(i,j)\cdot X(r,s)}{||X(i,j)||X(r,s) ||} \tag{8}\]  
where low SAM values mean high spectral similarity between the compared signatures.  
A second metric for the evaluation of the unmixing algorithms is based on the assumption that a set of high-quality abundance maps and their corresponding endmembers may allow the reconstruction  
Figure 4: Raman maps of PCL-GO (**a**) and PLA-GO (**b**).  
Figure 3: Raman spectra of neat polycaprolactone and polycaprolactone-graphene oxide (**a**), and neat polylactic acid-graphene oxide (**b**). In both panels the Raman spectrum of graphene oxide is reported for comparison, the G band is clearly detectable in the two composites.  
of the original data with higher precision than a set of low-quality endmembers/abundance maps. Specifically, the metric used is the Root Mean Squared Error (RMSE) between the original and the reconstructed data, which can be defined as follows:  
\[\text{RMSE}\Big{(}\text{I}^{\text{(O)}},\text{I}^{\text{(R)}}\Big{)}=\frac{1}{ \text{s}\times\text{I}}\sum_{\text{i}=1}^{\text{s}}\sum_{\text{j}=1}^{\text{l} }\left(\frac{1}{\text{n}}\sum_{\text{k}=1}^{\text{n}}\left[\text{x}_{\text{k} }^{\text{(O)}}(\text{i},\text{j})-\text{x}_{\text{k}}^{\text{(R)}}(\text{i}, \text{j})\right]\right)^{1/2} \tag{9}\]  
where \(\text{I}^{\text{(O)}}\) is the original map, and \(\text{I}^{\text{(R)}}\) is a reconstructed version of \(\text{I}^{\text{(O)}}\), obtained using Equation (1) with the set of endmembers used for the unmixing process and their corresponding estimated fractional abundances.