## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on Methodology
#### Microfluidics for Hepatitis Diagnosis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. No.** & **Type of Hepatitis** & \
\begin{tabular}{c} **Raman Spectroscopy** \\ **Variant** \\ \end{tabular} &
\begin{tabular}{c} **Sample Type** \\ **Sample Type** \\ \end{tabular} & **Main Findings** \\ \hline
[21] & HBV & SERS + PCA-PLSR & Blood serum &
\begin{tabular}{c} This predicted the viral loads of \\ blind/unknown samples with 99\% \\ accuracy. \\ \end{tabular} \\ \hline
[22; 23; 24] & HBV, HCV & \
\begin{tabular}{c} SERS + centrifugally \\ filtered devices \\ \end{tabular} & Blood serum &
\begin{tabular}{c} PLS-DA provided 98\% accuracy, and \\ the PLSR model correctly predicted \\ blind samples with an R\({}^{2}\) value of \\ 0.9085 and an RMSE of 0.30016 IU/L \\ \end{tabular} \\ \hline
[25] & HCV & SERS + biosensor & Extracted RNA &
\begin{tabular}{c} A LOD of 101.5 IU/mL and an \\ R\({}^{2}\) = 0.998 were achieved. \\ \end{tabular} \\ \hline
[26] & HCV & SERS + PLSR & Extracted RNA &
\begin{tabular}{c} The PLSR model achieved 99\% \\ accuracy in predicting the viral load. \\ \end{tabular} \\ \hline
[27] & HBV & SERS + PCA-PLS-DA & PCR products &
\begin{tabular}{c} PCA-PLS-DA achieved 89\% sensitivity \\ and 98\% specificity in classifying \\ viral loads. \\ \end{tabular} \\ \hline
[28] & HCV & SERS + PCA-PLSR & PCR products &
\begin{tabular}{c} PCA distinguished between healthy \\ and diseased samples, and PLSR \\ predicted the viral load with 99\% \\ accuracy. \\ \end{tabular} \\ \hline
[29] & HBV, HCV & SERS + PCA-PLS-DA & PCR products &
\begin{tabular}{c} Comparison of HBV DNA and HCV \\ RNA using SERS and PCA-PLS-DA at \\ 96\% sensitivity and 94\% specificity. \\ \end{tabular} \\ \hline
[69] & HCV & SERS + HCR & Nucleic acid &
\begin{tabular}{c} HCR-enhanced SERS detected HCV \\ nucleic acid with a limit of detection of 0.47 fM. \\ \end{tabular} \\ \hline
[30] & HBV & \
\begin{tabular}{c} RS + airPLS-PCA- \\ PSO-SVM \\ \end{tabular} & Blood serum &
\begin{tabular}{c} airPLS-PCA-PSO-SVM achieved \(-\)80\% \\ sensitivity and specificity. \\ \end{tabular} \\ \hline
[31] & HCV & RS + SVM & Blood serum &
\begin{tabular}{c} The SVM achieved 91.1\% accuracy in \\ discriminating between HCV \\ genotypes. \\ \end{tabular} \\ \hline
[32] & HBV & SERS & Blood serum &
\begin{tabular}{c} AgNP-based SERS showed 78.5\% \\ sensitivity and 75\% specificity. \\ \end{tabular} \\ \hline
[33] & HBV, HCC & SERS + CNN & Blood serum &
\begin{tabular}{c} The deep learning model classified \\ HBV, HCC, and the control with 97.78\% \\ accuracy. \\ \end{tabular} \\ \hline
[34] & HBV & RS + ResNet101 & Blood plasma &
\begin{tabular}{c} AI-based classification achieved \(>\)99\% \\ sensitivity and specificity. \\ \end{tabular} \\ \hline
[35] & HBV & \
\begin{tabular}{c} SERS-CRISPR \\ biosensor \\ \end{tabular} & Blood serum &
\begin{tabular}{c} The CRISPR-based biosensor detected \\ HBV DNA with a 0.1 pM detection \\ limit. \\ \end{tabular} \\ \hline
[36] & HBV & SERS + biosensor & Blood serum &
\begin{tabular}{c} The sandwich immunoassay detected the \\ HBsAg at 0.00576 IU/mL within 12 min. \\ \end{tabular} \\ \hline
[37] & HBV & \
\begin{tabular}{c} SERS + \\ immunoassay \\ biosensor \\ \end{tabular} & Antigen solution &
\begin{tabular}{c} This achieved a linear response range \\ from 0.5 ng/mL to 50 ng/mL and a \\ detection limit of 0.2 ng/mL. \\ \end{tabular} \\ \hline
[38] & HBV & \
\begin{tabular}{c} SERS + sandwich \\ assay \\ \end{tabular} & Blood serum &
\begin{tabular}{c} The temperature-responsive SERS \\ biosensor detected HBV DNA at 0.14 fM. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._