Colony Fingerprinting: Image-Based Pathogen Identification for High-Throughput Food Microbiological Testing
Authors: Tago Hikaru , Someya Takumi , Lim Tae Kyu , Harada Manabu , Yoshino Tomoko , Matsunaga Tadashi , Tanaka Tsuyoshi 
Published: December 2024

Introduction:

The contamination of foodborne pathogens in food products poses a substantial risk to public health. Precise detection and identification of pathogens is important for food safety. Conventional identification techniques rely on biochemical metabolic profiling and 16S ribosomal RNA sequencing. Recently, mass spectrometry-based approaches, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has been employed to distinguish pathogens. These methodologies require expensive reagents and equipment, expertise, and prolonged incubation. To overcome these issues, we introduce colony fingerprinting (CFP) that identify bacterial species based on image analysis of bacterial micro-colony without additional works [1,2]. CFP utilizes machine learning technology to distinguish bacterial species based on image characteristics, such as the shape or optical patterns. In this study, we newly developed a line image sensor-based CFP system [3]. The imaging unit offers practical throughput by covering the entire surface of the Petri dish used in the actual field. To demonstrate the efficacy of the system, we collected datasets of bacterial colony images using the system and trained a bacterial species identification model. The model enables the detection of Staphylococcus aureus inoculated in food samples with high accuracy in less than half the time required by conventional methods. This result indicates the usefulness of the CFP system for easy and rapid pathogen testing.

Materials and Methods:

The CFP system consisted of an imaging unit and an incubation unit (124 cm in width, 44 cm in height, 56 cm in depth). It captured colony images using a line image sensor consisting of 16,384 pixels (with a pixel size of 3.52 μm) under a collimated blue light-emitting diode emitting light (470 nm). The imaging area of this system for a single scan was 57.5 mm × 95 mm with a resolution of 44.2 μm. The incubation unit was equipped with an electric motor-driven Petri dish (92 mm) holder designed to be moved into the imaging unit during the culture process. To maintain the incubation temperature, the incubator unit employed an air heater based on a proportional-integral-differential controller. Bacteria were cultured on solid agar medium at the appropriate temperatures for durations ranging from 24 to 72 h. Less than 100 bacterial cells were inoculated onto a Petri dish for each species. In this study, fifteen bacterial species were analyzed, including four foodborne pathogens: S. aureus, Bacillus cereus, Escherichia coli, and Salmonella enterica.

Results & Discussion:

We monitored the growth of 1335 colonies (15 species × 89 colonies) at 5-min intervals to collect datasets for a machine learning. To extract image characteristics from colony images, colony regions were segmented using a trained fully convolutional network. Fifty-nine discrimination parameters were calculated to characterize the colony images in terms of luminance, texture, geometric details, and growth rate at 13 distinct growth stages (0.025 to 1.0 mm2) in an individual colony. The dataset was split into two subsets, with 80% of the data used to train the XGBoost classification model. The remaining 20% was allocated for validation to assess the accuracy of the model. The validation results show that an average accuracy of 92% was achieved when the colony size exceeded 0.3 mm2, and the accuracy increased to 96% at 0.6 mm2. This result suggests that the model constructed in this study is capable of identifying microbial species with high accuracy even in early stage of culturing.
To verify the generalization performance of this model, we conducted validations for the detection of S. aureus, which is a significant foodborne pathogen, in spiked milk samples. We monitored three Petri dishes inoculated with diluted milk samples and analysed image parameters from 239 colonies. As a result, 96% S. aureus were successfully detected when the colony size exceeded 0.3 mm2, requiring 10 hours of incubation. Our model enables the rapid detection of pathogenic bacteria compared with MALDI-TOF-MS. On the other hand, our system could not perfectly detect S. aureus, despite the distinct colony colors of the misidentified species. This limitation arises from the property of imaging unit that can only acquire grayscale data. To improve the discrimination accuracy, the line image sensor was replaced with one equipped with RGB elements. Consequently, misidentified species in grayscale were clearly discriminated. These results highlighted the potential of color image analysis for enhanced discrimination.

References:

[1] Maeda et al., PLOS ONE, 2017, 12(4), e0174723
[2] Maeda et al., Biosens Bioelectron, 2019, 146, 111747
[3] Tago et al., Biosens Bioelectron, 2024, 249, 116006