## 3 Results and Discussion
A correlation coefficient of exactly \(+1\) or \(-1\) represents a perfectly linear (or inverse linear) relationship between the compared groups, while \(0\) indicates the absence of any linear dependency [44, 45]. We calculated the Pearson coefficient, according to Equation E1 (Supporting Information), between each individual spectrum of the series of SARS-CoV-2 and Influenza A virus and the average spectrum of the series of spectra of the negative control. The outcome of this calculation for every replicate and data set is displayed in Figure S5. As expected, the values for both the Influenza A virus and SARS-CoV-2 samples are very close to 1, expressing high spectral similarity. However, a trend appears when evaluating the average of the Influenza A virus and SARS-CoV-2 samples for each replicate and data set, as displayed in Figure 4a. Although there is significant replicate-to-replicate variability, when taking every replicate individually, the SARS-CoV-2 samples are always associated with a mean value closer to \(+1\) and tend to have a smaller standard deviation. Moreover, we calculated the normalized cross correlation coefficient according to Equation E2 (Supporting Information). This correlation coefficient is often used in image analysis, for example, to enable pattern recognition. In contrast to the Pearson coefficient, also referred to as the zero-mean normalized cross correlation coefficient, no zero-mean centering (subtracting the average intensity) is carried out. Therefore, it has been found to be more sensitive in pattern recognition applications [25]. The results for the single spectra are displayed in Figure S6, and the average values and standard deviations are summarized  
Figure 4: Display of the average value and standard deviation (sdv) of the (**a**) Pearson coefficient and (**b**) normalized cross correlation coefficient for each replicate (R) and data set. in Figure 4b. The results are similar to the Pearson coefficient. All of the values are close to +1 and the SARS-CoV-2 samples result in mean values systematically larger than those of the Influenza A virus. Overall, Figure 4 reveals that the NCC coefficient seems to be slightly more sensitive than the Pearson coefficient for detecting the subtle differences in the spectra, in agreement with the findings of [PERSON] and [PERSON], perhaps because the NCC coefficient is more stable with regard to noise effects [25]. Again, the standard deviation of the SARS-CoV-2 samples tends to be smaller than that of the Influenza A virus samples. The larger standard deviation associated with the Influenza A virus sample is likely attributed to the above-mentioned unspecific binding observed through the PCR analysis. Indeed, one expects the dispersion of these particles on the surface to be much less homogeneous than that of the well-defined immobilization of the SARS-CoV-2 viruses. Accordingly, by analyzing different regions of the sample, we have probably been investigating areas that contain larger or smaller quantities of those residues, resulting in a standard deviation that is markedly higher. Furthermore, it is noteworthy that the correlation coefficients for the SARS-CoV-2 samples are always closer to +1, indicating a greater similarity to the negative control. This seems counterintuitive, as one expects the Influenza A virus samples to resemble the negative control more closely because, in contrast to the SARS-CoV-2 samples, these virus particles do not bind to the ACE2 receptor on the magnetic beads. However, this result should not be overinterpreted as the spectra are virtually identical and no distinct features can be assigned to a specific sample type. Rather than interpreting the correlation coefficients in the classical way, we conclude that the differentiation is much more likely to be due to a different signal to noise ratio and/or may be related to the less homogenous microstructure of the sample. It is important to consider that the distribution of the correlation coefficient values for each type of sample is quite large.