# Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
## 1 Introduction  
Foodborne or waterborne bacterial pathogens can be easily transmitted and spread through the fecal-oral route, which may cause diarrhea, vomiting, fever, and other symptoms, and can be life-threatening in some severe cases [1, 2]. Bacterial infection has been a large and growing public health problem in recent decades [3]. To achieve efficient treatment of bacterial infections, bacterial species identification is one of the key processes, providing guidance for the correct use of antibiotics. Routine bacteria testing strategies such as culture-based methods using selective media [4], nucleic acid-based polymerase chain reaction (PCR) methods [5], and mass spectrometry [6], have been widely used in clinical target pathogen detection. However, these methods can be of high recurring cost or time-consuming, and limited to the identification of single bacterial species [7]. Actually, in many clinical or daily environments, multiple pathogenic bacteria often coexist together. Some pathogenic infections are caused by mixtures of multiple bacteria, resulting in similar symptoms as a single bacterial infection, such as vomiting, nausea, and warming, which can be hardly distinguished using traditional testing strategies [8]. Therefore, it is necessary to develop sensitive and effective methods for the identification and analysis of mixed bacteria. Studies on simultaneous detection of multiple mixed bacteria have been carried out in laboratory settings [9, 10]. [PERSON] et al. presented a bi-channel surface-enhanced Ramanscattering (SERS)-based lateral flow immunoassay (LFA) device for simultaneous detection of multiple pathogens in a single test [9]. [PERSON] et al. reported a newly SERS-label using Au-shell-coated graphene oxide nanosheets (GO@Au) for multiple determination of three major foodborne pathogens [10]. However, these detection devices are difficult to be widely used in ordinary laboratories due to their complex fabrication procedures, time-consuming nature, high cost, and the professional operation that is required. Hyperspectral detection, as a non-invasive, chemical-free technique (see e.g., [11]), can provide spectral information that serves as the bacteria's fingerprint, and has been widely utilized in pathogenic bacteria identification and classification, with the advantages of rapid detection, low production cost, and convenient operation [12; 13]. A few studies focused on combining hyperspectral detection with different machine learning (ML) frameworks, e.g., support vector machine (SVM) [14], fusion-net [15], and BI-Net [16], to increase the number of identified bacteria species and improve classification accuracy. These studies further confirmed the validity and capability of ML coupled with spectral analysis in rapid and high-accuracy discrimination of bacterial strains. Furthermore, such strategy has great potential of quantitative analysis of mixed bacterial samples, as ML algorithms provide opportunities for more effective spectral data analytics and processing [17; 18; 19; 20]. [PERSON] et al. developed a principal component analysis-Monte Carlo (PCA-MC) model and a neural network inversion model for spectral separation of two types of mixed bacteria and concentration estimation [18]. [PERSON] et al. established a multi-molecular infrared (MM-IR) spectroscopy system based on PCA and SIMCA for simultaneous detection of two mixed pathogenic bacteria in food [8]. Recently, our research group demonstrated a hyperspectral microscopic imaging system combined with PCA-SVM method for rapid identification of mixed bacteria to the single-cell level [19]. Achieving more comprehensive and sensitive detection of multiple bacteria in mixed samples with hyperspectral techniques combined with ML methods entails higher requirements for specific spectral data and appropriate ML frameworks. In this paper, we proposed a dual-mode hyperspectral detection method coupled with deep learning algorithms for simultaneous quantitative analysis of pathogenic bacteria in the mixed samples.