## 3 Results and Discussion  
Both the Raman and SERS spectra of serum samples were measured. Figure 2 shows the comparison of the SERS spectrum of serum mixed with AgNPs to the regular Raman spectrum of blood serum. The observed dramatic increase in intensity of many vibrational bands within the SERS spectrum indicates that significant enhancement effects from the silver colloids was achieved. Serum samples were collected from three groups of subjects: AD (mild AD, \(n=10\) and moderate AD, \(n=10\)), other neurodegenerative dementias (OD, \(n=18\)), and healthy controls (HC, \(n=10\)). Supplementary Figure S1a shows the mean SERS serum spectrum for each group of subjects. Supplementary Figure S1b-f presents the difference spectrum and \(\pm 2\) standard deviations (STD) for each of the compared groups. Although there are visible differences in the serum spectral profiles of different groups, the differentiation of the groups solely based on visual inspection can be unreliable, as confirmed by the difference spectra lying within 2 STDs for each compared group. Because the differences between average spectra fall within 2 STDs of the mean, it is necessary to use multivariate statistical methods to fully understand the differentiation capabilities and significance of the observed spectral differences to be used for diagnostic purposes. Multivariate statistical methods allow for the capture and analysis of spectral variability by transforming hidden characteristic spectral features, which may be lost upon averaging of spectral datasets, into a discriminative algorithm. Further analysis demonstrated that high discrimination accuracy could be achieved by modeling nonlinear relationships between variables using ANNs. Figure 2: SERS spectrum (red) of the blood serum sample from a normal subject (HC) obtained by mixing the serum sample with AgNPs colloids in a 1:1 ratio, and the regular Raman spectrum (blue) of the same blood serum sample without the AgNPsN colloids. Spectra were recorded under the same conditions for comparison. It is important to note that a large number of features can lead to ANNs becoming overfit. Additionally, Raman spectra often contain spectral regions which may be non-informative. Therefore, GA was applied to optimize the generalization performance of the ANN models and to reduce the risk of overfitting. GA was used to select the subset of spectral variables which provide the most optimal information for discriminating between classes of donors in the training dataset, which consisted of 48 donors and 480 spectra. In general, more data leads to better accuracy due to a typically better quality dataset which has a closer domain and less noise. For GA, a single run was selected to have 71 individuals per population and 100 generations. A total of 300 runs were performed. After GA selected which spectral regions are the most informative, the Raman bands were identified and assigned to the biomolecules contributing to that vibrational mode (proteins, sugars, lipids, etc.). GA identified informative regions and bands of SERS serum spectra through comparison of the AD group to the HC group (Figure 3a) and the AD group to the OD group (Figure 3b). Spectral regions identified by GA as best influencing the differentiation capabilities are marked in red. Although GA revealed a greater number of significant regions between AD and OD SERS serum spectra than it did between AD and HC SERS serum spectra, the number of identified bands were very close. GA selected 9 regions (14 bands) and 5 regions (11 bands) between the AD to OD and AD to HC comparisons, respectively. As can be seen in Figure 3, the regions selected by GA in both comparisons are complimentary, and as such they selectively target complimentary spectral biomarkers expressed for the differentiation of these three classes, which can be very advantageous for statistical modeling. The region borders, vibrational band positions, and tentative band assignments for presumptive molecular contributions are given in Tables 2 and 3 for each comparison.