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Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks
摘要： A high-throughput hyperspectral microscope imaging (HMI) technology with hybrid deep learning (DL) framework defined as “Fusion-Net” was proposed for rapid classification of foodborne bacteria at single-cell level. HMI technology is useful in single-cell characterization, providing spatial, spectral and combined spatial-spectral profiles with high resolution. However, direct analysis of these high-dimensional HMI data is challenging. In this work, HMI data were decomposed into three parts as morphological features, intensity images, and spectral profiles. Multiple advanced DL frameworks including long-short term memory (LSTM) network, deep residual network (ResNet), and one-dimensional convolutional neural network (1D-CNN) were utilized, achieving classification accuracies of 92.2 %, 93.8 %, and 96.2 %, respectively. Taking advantage of fusion strategy, individual DL framework was stacked to form “Fusion-Net” that processed these features simultaneously with improved classification accuracy of up to 98.4 %. Our study demonstrated the ability of DL frameworks to assist HMI technology in single-cell classification as a diagnostic tool for rapid detection of foodborne pathogens.
关键词： Machine learning,Hyperspectral microscopy,Data fusion,Rapid detection,Foodborne pathogen
更新于2025-09-23 15:19:57
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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networksa??
摘要： Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.
关键词： Convolutional neural network,Machine learning,Hyperspectral microscopy,Food safety,Foodborne pathogen,Rapid classification
更新于2025-09-23 15:19:57
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Application of Response Surface Methodology to Evaluate Photodynamic Inactivation Mediated by Eosin Y and 530 nm LED against Staphylococcus aureus
摘要： Photodynamic antimicrobial chemotherapy (PAC) is an efficient tool for inactivating microorganisms. This technique is a good approach to inactivate the foodborne microorganisms, which are responsible for one of the major public health concerns worldwide—the foodborne diseases. In this work, response surface methodology (RSM) was used to evaluate the interaction of Eosin Y (EOS) concentration and irradiation time on Staphylococcus aureus counts and a sequence of designed experiments to model the combined effect of each factor on the response. A second-order polynomial empirical model was developed to describe the relationship between EOS concentration and irradiation time. The results showed that the derived model could predict the combined influences of these factors on S. aureus counts. The agreement between predictions and experimental observations (R2 adj = 0.9159, p = 0.000034) was also observed. The significant terms in the model were the linear negative effect of photosensitizer (PS) concentration, followed by the linear negative effect of irradiation time, and the quadratic negative effect of PS concentration. The highest reductions in S. aureus counts were observed when applying a light dose of 9.98 J/cm2 (498 nM of EOS and 10 min. irradiation). The ability of the evaluated model to predict the photoinactivation of S. aureus was successfully validated. Therefore, the use of RSM combined with PAC is a promising approach to inactivate foodborne pathogens.
关键词： photodynamic inactivation,xanthene dye,foodborne pathogen,green LED light,mathematical model
更新于2025-09-23 15:19:57
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Direct culture-free electrochemical detection of cells in milk based on quantum dots-modified nanostructured dendrons
摘要： With regard to global food safety and preventing the spread of diseases caused by foodborne pathogens or their toxins, there is an increasing need for simple and rapid methods for the screening of such pathogens. We aimed to develop a fast and efficient biosensor for the screening of milk samples contaminated by Salmonella spp. and provide a quick and cost-effective method as an alternative to the time-consuming conventional cultivation- or PCR-based approaches. We exploited a simple but highly specific technique whereby bacterial cells were separated immunomagnetically, with subsequent reaction with conjugate; i.e. specific IgG molecule labelled with an electrochemically potent indicator. The unique structure of this indicator exploits the benefits of hyperbranched dendron molecules and heavy metal–derived quantum dots (QDs). Square-wave anodic stripping voltammetry (SWASV) using of screen-printed carbon electrodes with in situ formed Bi(III) film (BiSPCE) was used for the detection and quantification of metal ions released from the QDs (CdTe) after their acidic dissolution. The metal ion signals proportionally correlate with the amount of captured bacteria cells. By this method, the presence of Salmonella spp. was proven in 2.5 hours even in minimal number of bacterial cells (4 CFU) in 1 mL of the sample.
关键词： Salmonella spp.,Electrochemical immunosensor,Quantum dots,Stripping voltammetry,Foodborne pathogen,Nanostructured dendrons
更新于2025-09-19 17:13:59