A new method for detecting mixed bacteria based on multi-wavelength transmission spectroscopy technology

Chi, Feng; Zhao, Nanjing; Yin, Guisheng; Gan, Tingting; Yang, Ruifang; Chen, Min; Jian-nan, Duan; Yu-xia, HU

doi:10.1016/j.saa.2021.120852

Cited by 4 publications

(2 citation statements)

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“…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]. Feng 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].…”

Section: Introductionmentioning

confidence: 99%

“…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]. Feng 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]. Zhang 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].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks

Zhu,

Luo,

2024

Applied Sciences

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Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of pathogenic bacteria in mixed samples. To acquire both transmission and fluorescence spectra regarding the mixed pathogens, we developed a dual-mode hyperspectral detection system with fine spectral resolution and wide wavelength range, which can also generate spatial images that can be used to calculate the total amount of mixed bacteria. The dual-mode spectra were regarded as mixed proportion characteristics and the input of the neural network for predicting the proportion of each bacterium present in the mixture. To better analyze the dual-mode spectral data, we customized a mixed bacteria measurement network (MB-Net) with hybrid DNNs architectures based on spectral feature fusion. Using the fusion strategy, two DNNs frameworks applied for transmission/fluorescence spectral feature processing were stacked to form the MB-Net that processes these features simultaneously, and the achieved average coefficient of determination (R2) and RMSE of validation set are 0.96 and 0.03, respectively. To the best of our knowledge, it is the first time of simultaneously detecting four types of mixed pathogenic bacteria using spectral detection technology, showing excellent potential in clinical practice.

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