A lanthanide-encoding inductively coupled plasma mass spectrometric approach allows for the first time counting and recognizing single bacterial cells, improving diagnosis and understanding of cell-to-cell variability.

Counting and Recognizing Single Bacterial Cells by a Lanthanide-Encoding Inductively Coupled Plasma Mass Spectrometric Approach.

Convolutional Neural Networks deep learning approach outperforms handcrafted features and conventional reference techniques in bacterial colony counting in Digital Microbiology Imaging, making it a preferred solution for clinical microbiology laboratories.

Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging

The standard plate count method is an indirect measurement of live bacteria, while spectrophotometric analysis measures all bacteria (cell biomass), both dead and alive.

Counting Bacteria.

RAPiD is a fast, accurate method for counting viable bacteria in microbiological applications, enabling food quality monitoring and clinical diagnosis in less than 3 hours.

Simple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection.

The automated colony counting system with a novel segmentation algorithm accurately counts bacterial colonies on agar plates, improving accuracy and performance in bacterial colony counting.

Automated Counting of Bacterial Colony Forming Units on Agar Plates

Our fully automatic bacterial colony counter can count and classify colonies using digital camera images, offering improved precision and recall while reducing labor and time in biomedical assays.

An automated bacterial colony counting and classification system

NICE, a low-cost, high-throughput, automated counting system for bacterial colonies, accurately correlates with manual counting results and is free from NIST.

Low‐cost, high‐throughput, automated counting of bacterial colonies

The AB2-AuNP method, using gold nanoparticles conjugated with an aptamer, provides a rapid, on-site method for detecting bacteria in water samples, with sensitivity as low as 100 CFU mL-1.

A rapid total bacterial count method using gold nanoparticles conjugated with an aptamer for water quality assessment.

The proposed method, based on polyaniline/bacteria thin film, is a simple and rapid method for bacterial counting, with acceptable stability, reproducibility, and switchable ability.

Bacteria counting method based on polyaniline/bacteria thin film.

Convolutional neural networks (cCNN) can accurately classify and count bacterial cell sub-populations, providing a 10.2 times more consistent and 3.8 times faster alternative to manual microscopy.

Automated classification of bacterial cell sub-populations with convolutional neural networks

Our rapid bacterial counting method using photothermal assembling (PTA) takes 90 seconds and provides accurate estimates of initial bacterial concentrations, benefiting medical care and food safety.

Development of a rapid bacterial counting method based on photothermal assembling

This study developed a simple, fast, and cost-effective method for bacterial counting using gold nanoparticles, enabling routine detection of bacteria in water, milk, and fruit juice samples.

Counting bacteria using functionalized gold nanoparticles as the light-scattering reporter.

The developed label array method effectively counts and differentiates bacterial species, offering a simple, cost-effective, and user-friendly alternative for applications in medicine, water quality monitoring, and the food industry.

Luminometric Label Array for Counting and Differentiation of Bacteria.

This study developed an image-processing based bacteria colony counter in MATLAB, enabling efficient detection and counting of bacteria colonies in medical microbiology applications.

Counting Bacteria Colonies Based on Image Processing Methods

Dip-stick microfluidic strips can provide rapid and simple label-free counting of bacterial colonies and bacteriophage plaques, offering a new method for microbial analysis.

Label-free 1D microfluidic dipstick counting of microbial colonies and bacteriophage plaques.

This microfluidic chip-based device effectively counts bacteria in aqueous solutions, enabling rapid and accurate determination of bacteria concentrations in microbiological and biological applications.

Counting bacteria on a microfluidic chip.

Microfluidic devices enable accurate bacterial cell counting using smartphone cameras, potentially enabling direct pathogen counting in clinical samples without agar plating.

Counting bacteria in microfluidic devices: Smartphone compatible 'dip-and-test' viable cell quantitation using resazurin amplified detection in microliter capillary arrays.

Automated bacterial colony counting on agar plates using MATLAB's image processing feature achieves 80% accuracy, making it reliable and effective for microbiology research and medical diagnostics.

Automated Bacterial Colony Counting on Agar Plate

This study presents a simple, rapid, and economical method for counting bacterial colony-forming units using microcolonies, comparable to traditional dilution plating, with potential applications in estimating bacterial numbers and determining mutation rates.

Microliter spotting and micro-colony observation: a rapid and simple approach for counting bacterial colony-forming units

These studies suggest various methods for bacterial counting, including advanced spectrometric techniques, deep learning approaches, automated systems, and rapid detection methods, each offering improvements in accuracy, speed, and efficiency.