# Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
## 4 Summary and Outlook  
In summary, a dual-mode hyperspectral detection technique combined with hybrid deep neural networks (DNNs) has been introduced in the present paper. It has the ability of quantitative analysis of mixed pathogenic samples, and has been used to detect a variety of bacteria including _S. aureus, E. coli, P. aeruginosa, and S. marcescens_ at the same time, successfully predicting their different mixing ratios. We have utilized our self-developed hyperspectral detection system to collect both transmission and fluorescence spectral data of the mixed bacterial samples, and established a mixed bacteria measurement network (MB-Net) for the prediction of mixed bacteria ratio according to the dual-mode spectral features. The MB-Net with hybrid DL architectures enhances the representation ability of features by stacking and fusing two DNN frameworks applied for transmission/fluorescence spectral feature processing, and the achieved average coefficient of determination (\(R^{2}\)) and RMSE on the validation set are 0.96 and 0.03, respectively. Furthermore, the hyperspectral detection system can also generate transmission hyperspectral images of mixed samples for calculation of the total concentration of mixed bacteria using cell counting methods. Combined with the predicted proportion of the mixed bacteria from the MB-Net, the detailed concentration of each bacterium in the mixed samples can be calculated. As implemented, our self-developed system has the advantages of low-cost, scalability, and convenient operation compared with commercial instruments. We explored the quantitative analysis methods of multiple mixed pathogens by using more advanced models and more \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Spectral Method**} & \multirow{2}{*}{**Statistics Analysis**} & \multicolumn{2}{c}{**The Number of**} & \multicolumn{1}{c}{**The Number of**} & \multirow{2}{*}{**Performance**} \\  & & & **Types** & & & \\ \hline
[17] & Infrared spectroscopy & PCA and LDA & 2 & 5 & 90\% (at the ratio of 9:1) \\ \hline
[18] & Transmission spectroscopy & PCA–MC & & & \\  & spectroscopy & BPNN & 2 & 11 & 0.9954 (\(R^{2}\)) \\ \hline
[8] & MM-IR spectroscopy & PCA and SIMCA & 2 & 7 & 100\% (recognition rate) \\ \hline
[20] & SERS & PLSR/ANNs & 3 & 66 & 0.95 (\(R^{2}\)) \\ \hline \multirow{3}{*}{Our} & Fluorescence and & & & & \\  & transmission & MB-Net & 4 & 84 & 0.96 (\(R^{2}\)) \\ \cline{1-1}  & hyperspectral detection & & & 0.03 (RMSE) \\ \hline \hline \end{tabular} PCA: Principal components analysis; LDA: linear discriminant analysis; PCA–MC: Principal component analysis–Monte Carlo; BPNN: back propagation neural network; MM-IR: multi-molecular infrared; SIMCA: soft independent modeling of class analogy; SERS: surface enhancement of Raman scattering; PLSR: partial least squares regression; ANNs: artificial neural networks.  
\end{table} 
Table 4: Comparison of researches on spectral measurement of mixed bacteria ratio. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & _S. marcescens_ **(Ratio)** & _P. aeruginosa_ **(Ratio)** & _S. aureus_ **(Ratio)** \\ \hline
1–7 & 0.1 & 0.1, 0.2,... \(\rightarrow\) 0.7 & 0.7, 0.6,... \(\rightarrow\) 0.1 \\
8–13 & 0.2 & 0.1, 0.2,... \(\rightarrow\) 0.6 & 0.6, 0.5,... \(\rightarrow\) 0.1 \\
14–18 & 0.3 & 0.1, 0.2,... \(\rightarrow\) 0.5 & 0.5, 0.4,... \(\rightarrow\) 0.1 \\
19–22 & 0.4 & 0.1, 0.2,... \(\rightarrow\) 0.4 & 0.4, 0.3,... \(\rightarrow\) 0.1 \\
23–25 & 0.5 & 0.1, 0.2, 0.3 & 0.3, 0.2, 0.1 \\
26, 27 & 0.6 & 0.1, 0.2 & 0.2, 0.1 \\
28 & 0.7 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The composition of the three mixed bacteria samples. comprehensive spectral data related to the bacterial properties, resulting in the rapid and effective analysis of mixed bacterial samples. This study lays the foundation for the spectral analysis of multiple mixed pathogens.