## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on Methodology
#### 4.2.2 Filter Devices/Centrifugal Devices/Screening Membranes  
High-molecular-weight proteins (immunoglobulins, fibrinogen, and albumin) dominate the composition of blood plasma and serum, and if these fractions (HMWFs) are removed from the serum using centrifugal filter devices/membranes, the remaining low-molecular-weight fractions (LMWFs) can enhance the diagnostic capabilities of Raman techniques for hepatitis. Most of the viral proteins are lower in weight and can be considered an LMWF. [PERSON] et al. proposed cellulose membrane filtration of the serum for SERS-based, non-invasive HBV diagnosis [88]. A total of 98 confirmed HBV samples and 89 healthy samples were filtered using a membrane and washed to remove unwanted components. The membrane, along with the proteins adsorbed onto its surface, was dissolved in acetic acid to make a gel. It was then mixed with AgNPs, and the mixture was dried on an aluminum plate for SERS measurements. Biochemical changes in the serum proteins were identified, and PCA-LDA showed 92.6% sensitivity and 77.5% specificity for differentiation between the healthy and diseased samples. Figure 7 shows a schematic of the process for this methodology.  
[PERSON] et al., in 2022, presented work applying this strategy; a total of 41 serum samples, including 30 clinically confirmed HBV-infected samples and 11 healthy samples, were centrifugally filtered using 50 kD sized filter membranes [24]. PCA of the acquired SERS data demonstrated clear differentiation among the diseased and healthy samples. PLS-DA provided 98% accuracy, and the PLSR model correctly predicted blind samples with an R\({}^{2}\) value of 0.9085 and an RMSEP of 0.30016 IU/L. A similar strategy was utilized for the diagnosis of hepatitis C. In this other work, 43 samples, including 28 clinically confirmed HCV-infected samples and 15 healthy samples, were centrifugally filtered using a filter size of 50 kD and analyzed using SERS [23]. Differentiation among healthy and low-,  
Figure 6: Schematic illustration of a high-throughput blood analysis based on a deep learning algorithm and a self-positioning super-hydrophobic SERS platform for non-invasive multi-disease screening. Serum samples from mixed groups, Normal, HBV, Leukemia M5 and Breast Cancer (**A**). High throughput Raman spectral acquisition (**B**), Raman data analysis by deep learning model (**C**). Reprinted with permission from [87]. Copyright, 2021, John Wiley and Sons, Ltd.  
medium-, and high-viral-load samples using a PCA was achieved, and 99% accuracy was claimed using PLSR quantitative modeling. In 2023, [PERSON] et al. presented a comparative SERS study of centrifugally filtered HBV- and HCV-infected samples. Again, a 50 kD filter was applied to separate the LMWF from 54 serum samples, including 22 HBV- and 22 HCV-infected samples. The PCA was successful in distinguishing between all three classes, while the PLS-DA was found to be 90% specific, 85% precise, and 83% accurate in the differentiation of the classes [22]. Figure 8 shows a schematic of the process for this methodology.  
[PERSON] et al. [88] used dried samples for Raman acquisition, while all of the other researchers used liquid samples. [PERSON] et al. and [PERSON] et al. claimed a higher differentiation accuracy using liquid samples. Some common spectral features reported for HBV are those at 853 cm\({}^{-1}\), 1003 cm\({}^{-1}\), 1170 cm\({}^{-1}\), and 1446 cm\({}^{-1}\), which correspond to tyrosine, phenylalanine, collagen, and C-H bending (lipids).  
Figure 8: Schematic illustration of SERS study of centrifugally filtered serum. Reprinted with permission from [22]. Copyright, 2023, Elsevier B.V.  
Figure 7: Schematic illustration of membrane protein purification-based SERS analysis of HBV samples. Recreated from [88] under the Creative Commons Attribution (CC BY) agreement.