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Aiho Kushida1, Rin Tsuchiya1, Tatsuya Tominaga2

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A new deep learning classifier accurately distinguishes and counts aerobic spore-forming bacteria colonies on food safety media. This AI tool aids in rapid food quality assessment and safety management for users of all skill levels.

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BacillusClassifierDeep learningReady-to-eat foodsSheet mediumSpore-forming bacteria

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Area of Science:

  • Microbiology
  • Food Science
  • Artificial Intelligence

Background:

  • Aerobic spore-forming bacteria are significant contributors to food spoilage and foodborne illnesses.
  • Distinguishing these bacteria from others typically relies on visual colony morphology on specialized media.
  • Accurate and efficient quantification is crucial for food safety and quality control.

Purpose of the Study:

  • To develop and validate a deep learning-based classifier for automated detection and enumeration of aerobic spore-forming bacterial colonies.
  • To assess the accuracy, versatility, and practical feasibility of the developed classifier in food safety applications.
  • To provide a rapid, objective method for quantifying spore-forming bacteria, complementing traditional methods.

Main Methods:

  • Development of a classifier using the YOLO (You Only Look Once) deep learning algorithm.
  • Training the classifier with images of Bacillus subtilis subsp. spizizenii (spore-forming) and Escherichia coli (non-spore-forming) colonies.
  • Evaluation of classifier performance on 60 images (4549 colonies) and testing its versatility across 46 strains from 34 species.
  • Assessing practical feasibility using 66 ready-to-eat food samples and comparing results with 16S rDNA sequencing.

Main Results:

  • The YOLO-based classifier achieved an accuracy exceeding 0.98 in initial performance evaluations.
  • Mean accuracy greater than 0.94 was observed when tested for versatility across diverse bacterial species.
  • The classifier demonstrated a precision approaching 0.91 when applied to ready-to-eat food samples, with rapid enumeration at approximately 133 msec per medium.
  • Discrimination power was found to be equivalent to manual visual inspection.

Conclusions:

  • The developed deep learning classifier effectively distinguishes and quantifies aerobic spore-forming bacteria colonies with high accuracy and speed.
  • This AI tool offers a practical, efficient, and objective solution for routine food safety monitoring and quality management.
  • The classifier's performance suggests its potential to assist users of varying expertise in rapidly assessing food safety and quality.