Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

• Published in: Biosensors & bioelectronics Vol. 249; p. 116006
• Main Authors: Tago, Hikaru, Maeda, Yoshiaki, Tanaka, Yusuke, Kohketsu, Hiroya, Lim, Tae-Kyu, Harada, Manabu, Yoshino, Tomoko, Matsunaga, Tadashi, Tanaka, Tsuyoshi
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.04.2024
• Subjects: Colony fingerprinting; Early detection; High-throughput testing; Line image sensor; Machine learning; Pathogenic bacteria; High-throughput testing; Line image sensor; Colony fingerprinting; Pathogenic bacteria; Early detection; Machine learning
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2024.116006

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications. •Rapid detection of pathogenic bacteria was demonstrated with colony fingerprinting.•High-throughput testing was achieved based on line image sensor.•Growth of bacterial colonies is quantified by image analysis.•Time-series changes in colony morphology contribute to discrimination.•Machine learning models trained on image parameters identify bacterial species.