## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on Methodology  
#### 4.2.1 Surface-Enhanced Raman Spectroscopy (SERS)  
The Raman spectra of bodily fluids, including serum and plasma, are very complex and show only subtle variations, which are sometimes difficult to distinguish. Sensitivity and signal enhancements can be achieved by integrating nanomaterials into Raman spectroscopy. Noble metal nanostructures, like Au and Ag, are considered the best options for surface enhancement in Raman spectroscopy. In 2018, [PERSON] et al. proposed a AgNP-based SERS methodology for HBV detection in 93 diseased and 94 healthy samples [32]. The serum samples were mixed with the AgNPs and dried naturally at room temperature prior to SERS acquisition. Biochemical changes in the serum were identified, and the PCA and LDA showed a sensitivity of 78.5% and 91.4% and a specificity of 75% and 83%. In 2020, [PERSON] et al. demonstrated a SERS-based study on HCV diagnosis and viral load quantification in infected serum samples using AgNPs as the substrates [11]. More robust features that were clear and intense and explained the changes linked with the disease better were identified using SERS as compared to using RS, and better differentiation of the viral load classes was seen in PCA. A PLSR model predicted the viral loads much better for the SERS data, showing an R\({}^{2}\) value of 0.92 and an RMSEP of 0.24 IU/L, as compared to RS, which yielded an R\({}^{2}\) value of 0.77 and an RMSEP of 0.41 IU/L for the same samples. In 2021, [PERSON] et al. reported on the spermine-coated AgNP-based SERS detection of a YMDD-motif mutant of HBV, known for its drug resistance [64]. Unique fingerprint spectra for the mutated DNA were obtained, incorporating spectral features related to adenine in the DNA strand of HBV.  
[PERSON] et al. reported on a different SERS substrate based on a Au-Ag nanocomplex decorated with ZnO nanopillars on paper for the discrimination of hepatocellular carcinoma (HCC), hepatitis B (HB), and normal control serum samples [33]. The SERS method demonstrated a tan enhancement factor of 1.02 \(\times\) 10\({}^{7}\), which was considered enough to sense hepatic disease. The method was incorporated with deep learning convolutional neural networks (CNNs), and a total of 90 samples were discriminated in 1 min with a prediction accuracy of 97.78%. [PERSON] et al. proposed AgNP-based label-free SERS discrimination of multiple types of samples, including 94 hepatitis B, 77 leukemia M5, 321 breast cancer, and 203 healthy control serum samples [87]. Reinforced by a deep learning (DL) model, the healthy and diseased samples were differentiated with 100% accuracy. In the external hold-out test set, the model demonstrated 98.6% diagnostic accuracy. A schematic illustration of this methodology is shown in Figure 6.  
[PERSON] et al. [32] and [PERSON] et al. [33] used dried samples, while all of the other studies used liquid samples for the Raman acquisitions. Higher accuracies of up to 97.78% were achieved using dried samples and incorporating deep learning CNN models. The Raman bands that were common among the reports included those at 725, 813, 890, 960, 1005, 1135, 1208, 1332, and 1655 cm\({}^{-1}\), corresponding to L-arginine, collagen, saccharide, valine, phenylalanine, tyrosine, nucleic acid, and amide I.