## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on the Sample Type
#### 4.1.2 Diagnosis Using Blood Serum  
Blood serum is the most common body fluid used for Raman-spectroscopy-based hepatitis diagnosis. [PERSON] et al. attempted to differentiate between the serum samples of 14 healthy and 10 HCV-infected individuals by recording Raman spectra in 2008 [54]. PCA was used for the main spectral differentiation, and a Mahalanobis distance algorithm was used for serological blood differentiation. Four differentiating features were identified between 1000 and 1348 cm\({}^{-1}\), and good discrimination between the healthy and diseased samples was seen in the PCA scatter plot. In 2017, [PERSON] et al. studied 152 blood serum samples, including healthy and ELISA-confirmed HCV-infected samples, using Raman spectroscopy and PLSR modeling [78]. Chitin-, trehalose-, ammonia-, and cytokine-linked spectral features were seen that could be biomarkers for inference about HCV positivity, while features linked with carbohydrate-binding proteins were proposed for inference about HCV negativity. PLSR modeling with cross-validation was applied to exploring the viral load values in the samples. Higher than 93% sensitivity, specificity, and accuracy were reported, and 30 unknown samples were tested for the prediction of HCV infection. [PERSON] et al. analyzed 227 serum samples, including 105 healthy and 122 clinically confirmed HCV-infected samples, in 2018 [68]. A proximity-based classification method combined with a PCA was applied to the Raman spectral data, denoted as RS-PCA-Prox. It was claimed that the RS-PCA-Prox analysis offered better differentiation based on the molecular spectral information as compared to that of linear- and ensemble-based classifiers, with almost 95% sensitivity, specificity, and accuracy. Ammonia-, chitin-, lectin-, lipid-, and viral-protein-related features were proposed as important biomarkers for the occurrence of HCV viral infections. In the same year, [PERSON] et al. analyzed 84 healthy and 119 HBV-infected blood serum samples using Raman spectroscopy combined with a pattern recognition technique [79]. Two different kernels of SVMs, the Gaussian radial basis function (RBF), and the polynomial function were applied to Raman spectral data for differentiation among healthy and HBV-positive samples. Furthermore, the performance of the kernels was tested using least squares and quadratic programming. Five-fold cross-validation was applied to validating the model. On average, the model was found to be 97% precise, accurate, and sensitive. [PERSON] et al. studied 68 serum samples, including 28 healthy and 40 HCV-infected samples, using Raman spectroscopy in 2019 and achieved good discrimination between healthy and diseased samples in their PCA scatter plot [55]. Applying an analysis of variance (ANOVA) and a \(t\)-test (\(p<0.001\)), eleven unique spectral features were observed from 676 to 1757 cm\({}^{-1}\). Six features were indicated that were only seen in the healthy samples. PCA-LDA with 10-fold jackknife cross-validation was used to achieve effective diagnostics. All of the samples were correctly discriminated by the proposed algorithm, although values for the specificity and sensitivity were not provided. [PERSON] et al. analyzed a large sample size of 1000, including 500 healthy and 500 HBV-positive samples, using Raman spectroscopy and an airPLS-PCA-PSO-SVM multivariate data analysis model [30]. The adaptive iterative weighted penalty least squares method (airPLS) was applied to remove the background, and PCA was applied to extract the spectra. SVM was then used for modeling and prediction, and particle swarm optimization (PSO) was used to optimize the SVM parameters instead of a traditional grid. Eight distinct spectral features were identified from 509 to 2305 cm\({}^{-1}\) for differentiating between healthy and diseased samples. The method was validated using a double-blind method, resulting in around 80% sensitivity and specificity.