## 4 Conclusions  
We have demonstrated how a simple magnetic bead-based sample preparation scheme can enable the differentiation of SARS-CoV-2 and Influenza A virus using conventional Raman spectroscopy. In contrast to several already existing approaches, our method does not need a plasmonically active SERS substrate, thereby making it easier to apply and less prone to reproducibility issues. By exploiting the specific interaction of the ACE2 receptor and the spike protein of SARS-CoV-2, we were able to selectively enrich the virus and subsequently acquire the Raman spectra of the bead-bound virus. As a \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) NCC Coefficient**} \\ \hline  & \begin{tabular}{c} **SARS-CoV-2** \\ **Mean \(\pm\) Sd** \\ \end{tabular} & \begin{tabular}{c} **Influenza A Virus** \\ **Mean \(\pm\) Sd** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Value** \\ **Mean \(\pm\) Sd** \\ \end{tabular} &
\begin{tabular}{c} **\(p\)-Value** \\ **Bold: Significant at** \\ **\(\alpha\) = 0.05** \\ \end{tabular} \\ \hline  & \multicolumn{4}{c}{Data Set 1} \\ \hline Replicate 1 & 0.977 \(\pm\) 0.028 & 0.951 \(\pm\) 0.104 & 0.0261 \\ Replicate 2 & 0.966 \(\pm\) 0.097 & 0.960 \(\pm\) 0.068 & 0.0889 \\ Replicate 3 & 0.986 \(\pm\) 0.018 & 0.970 \(\pm\) 0.040 & 0.0340 \\ Replicate 4 & 0.990 \(\pm\) 0.015 & 0.987 \(\pm\) 0.016 & 0.0154 \\ \hline  & \multicolumn{4}{c}{Data Set 2} \\ \hline Replicate 1 & 0.996 \(\pm\) 0.004 & 0.994 \(\pm\) 0.007 & 0.0258 \\ Replicate 2 & 0.988 \(\pm\) 0.013 & 0.974 \(\pm\) 0.048 & 0.0340 \\ Replicate 3 & 0.990 \(\pm\) 0.009 & 0.985 \(\pm\) 0.009 & 0.0340 \\ Replicate 4 & 0.987 \(\pm\) 0.058 & 0.983 \(\pm\) 0.088 & 0.0340 \\ \hline  & \multicolumn{4}{c}{(b) Pearson coefficient} \\ \hline  & \multicolumn{4}{c}{Data Set 1} \\ \hline Replicate 1 & 0.939 \(\pm\) 0.076 & 0.886 \(\pm\) 0.188 & 0.0682 \\ Replicate 2 & 0.928 \(\pm\) 0.140 & 0.902 \(\pm\) 0.154 & 0.0889 \\ Replicate 3 & 0.965 \(\pm\) 0.049 & 0.919 \(\pm\) 0.108 & 0.0198 \\ Replicate 4 & 0.973 \(\pm\) 0.038 & 0.965 \(\pm\) 0.043 & 0.0340 \\ \hline  & \multicolumn{4}{c}{Data Set 2} \\ \hline Replicate 1 & 0.988 \(\pm\) 0.010 & 0.984 \(\pm\) 0.018 & 0.0258 \\ Replicate 2 & 0.966 \(\pm\) 0.035 & 0.934 \(\pm\) 0.111 & 0.0340 \\ Replicate 3 & 0.973 \(\pm\) 0.038 & 0.957 \(\pm\) 0.025 & 0.122 \(\times\) 10\({}^{-7}\) \\ Replicate 4 & 0.970 \(\pm\) 0.105 & 0.969 \(\pm\) 0.118 & 0.9440 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average values and standard deviations and results of the Kolmogorov-Smirnov test for SARS-CoV-2 and Influenza A virus samples, for all replicates and for both data sets (a) based on the normalized cross correlation coefficient and (b) based on the Pearson coefficient. non-match control, we included Influenza A virus H1N1 in our study. As the spectral differences between the SARS-CoV-2-positive and -negative samples were subtle, 1D correlation analysis was used for achieving a successful differentiation. We calculated both the Pearson and NCC coefficients for quantifying the differences between samples and found that both coefficients perform very similarly, with the NCC coefficient being slightly more sensitive. The presented approach represents a first step towards virus identification using conventional Raman spectroscopy.