## 1 Introduction  
Alzheimer's disease (AD) is a progressive neurodegenerative disease which results from neuronal cellular pathology associated with the dysregulation of protein and lipid metabolism, oxidative stress, and inflammation [1]. The discovery of relatively robust AD-specific biomarker signatures in blood has recently been reported by several research groups [2; 3; 4]. It was indicated that the diagnostic strategy could significantly benefit from combining several serum biomarkers because of the multiplicity of pathophysiological processes comprised in AD [5; 6; 7]. Efforts to find reliable biomarkers for AD in peripheral blood resulted in the documentation of several promising novel blood biomarker panels including protein signatures, [2, 4, 8], lipid and metabolite profiles [9], oxidative stress species [10], and proinflammatory cytokines [11]. Intense multidisciplinary research has shown that Raman spectroscopy combined with advanced statistics can determine potential differences in molecular structure and biochemical composition of body fluids and tissues [12, 13, 14, 15, 16, 17, 18, 19]. Furthermore, it has emerged as a powerful technique for the detection of cancer [20, 21], diabetes [22, 23], preeclampsia [24], and atherosclerosis [25]. Most importantly, Raman spectroscopy has proven to be a useful tool for detecting characteristic biochemical changes in blood which occur due to neurodegenerative disorders such as AD [26, 27, 28] and Parkinson's disease (PD) [29]. This indicates that as a disease progresses within humans, changes within the composition of blood serum occurs, causing disease-specific alterations in the spectral signature obtained by Raman spectroscopy. Consequently, these changes of spectral biofluid features can be used for diagnostic purposes [30, 31]. However, conventional Raman spectroscopy has an inherently small cross-section of Raman scattering and the Raman signals from biological samples are usually weak [12]. Even further, it is only natural to assume that at the initial stages of AD, the biochemical changes reflected in blood will be extremely subtle [32]. To overcome these potential hurdles for introducing the technology to clinical settings, the approach requires high spectral quality, high levels of sensitivity and specificity, and advanced methods for data analysis. The phenomenon of surface-enhanced Raman scattering (SERS) provides all of the aforementioned qualities. Adsorption of a sample onto rough metal nanostructures leads to a significant enhancement of Raman scattering by several orders of magnitude (between \(10^{2}\)-\(10^{6}\)) [33]. Recent advances in theory and computational techniques have shown that a strong SERS effect can be achieved using silver or gold colloids [34]. SERS has proven itself to be a highly attractive bioassay platform for bioanalytical sensing which requires low detection levels and a high degree of specificity [34, 35]. SERS has been used in combination with well-validated multivariate statistical methods to address a variety of biomedical problems including detection of cancer, infectious diseases, and metabolic diseases [36, 37, 38, 23, 39]. The development of a reliable assay for rapid detection and differentiation of AD will dramatically improve the management of this deadly disease in terms of providing early diagnostic efficiency and accuracy, as well as aid in the search for a cure [32, 40]. As such, this study explores a SERS-based analysis of blood serum samples to develop a rapid and reliable laboratory test for AD diagnostics. The approach is based on probing the total biochemical composition of blood serum with Raman spectroscopy using silver nanoparticles (AgNPs) as a signal-enhancing substrate. The obtained SERS spectral signatures were analyzed by artificial neural networks (ANNs) for AD detection and differentiation. ANNs are a form of sophisticated statistical techniques which can be used for multivariate data analysis.