AIMC Topic: Bacterial Typing Techniques

Clear Filters Showing 1 to 10 of 15 articles

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

Food chemistry Aug 20, 2024
This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of fo...
Enterococcus Bacterial Typing Techniques Support Vector Machine Supervised Machine Learning Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Machine Learning Algorithms
View on PubMed DOI

Classification of subspecies based on MALDI-TOF MS protein profiles using machine learning models.

Microbiology spectrum Aug 20, 2024
UNLABELLED: is an important bacterial species used as a starter culture for fermented foods; however, two subspecies within this species exhibit different properties in the foods. Matrix-assisted laser desorption/ionization-time of flight mass spect...
Machine Learning Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Fermented Foods Latilactobacillus sakei Bacterial Typing Techniques Support Vector Machine Food Microbiology Bacterial Proteins
View on PubMed DOI

Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification.

Future microbiology Jul 29, 2024
Assessing the visual accuracy of two large language models (LLMs) in microbial classification. GPT-4o and Gemini 1.5 Pro were evaluated in distinguishing Gram-positive from Gram-negative bacteria and classifying them as cocci or bacilli using 80 Gra...
Artificial Intelligence Staining and Labeling Bacteria Staphylococcus aureus Humans Bacterial Typing Techniques Gentian Violet Gram-Negative Bacteria Gram-Positive Bacteria Phenazines
View on PubMed DOI

Prediction of Clonal Complexes from Multilocus Variable Number Tandem Repeat Analysis Patterns Using a Machine Learning Approach.

Foodborne pathogens and disease Jul 4, 2024
Multilocus variable number tandem repeat analysis (MLVA) is a molecular subtyping technique that remains useful for those without the resources to access whole genome sequencing for the tracking and tracing of bacterial contaminants. Unlike technique...
Food Microbiology Listeria monocytogenes Multilocus Sequence Typing Bacterial Typing Techniques Minisatellite Repeats Machine Learning
View on PubMed DOI

Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

World journal of microbiology & biotechnology Jul 2, 2020
Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM c...
Polymerase Chain Reaction Salmonella Infections Bacterial Typing Techniques Chickens Serogroup Serotyping DNA Fingerprinting Nucleic Acid Denaturation Machine Learning Algorithms Animals Salmonella Feces Thailand Phylogeny Humans
View on PubMed DOI

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning.

Molecules (Basel, Switzerland) Apr 14, 2020
A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive cl...
Hyperspectral Imaging Bacterial Typing Techniques Models, Theoretical Support Vector Machine Colony Count, Microbial Machine Learning Algorithms
View on PubMed DOI

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks.

Applied microbiology and biotechnology Feb 12, 2020
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 co...
Food Microbiology Foodborne Diseases Microscopy Spectrum Analysis Bacterial Typing Techniques Machine Learning Neural Networks, Computer Algorithms Image Processing, Computer-Assisted
View on PubMed DOI

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of and Related Species.

Molecules (Basel, Switzerland) Dec 10, 2019
, the causative agent of glanders, and , the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in ...
Humans Cluster Analysis Workflow Machine Learning Spectrum Analysis, Raman Bacterial Typing Techniques Burkholderia mallei
View on PubMed DOI

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

Nature communications Oct 30, 2019
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from...
Single-Cell Analysis Humans Bacteria Staphylococcus aureus Bacterial Typing Techniques Logistic Models Neural Networks, Computer Methicillin-Resistant Staphylococcus aureus Proteus mirabilis Anti-Bacterial Agents Spectrum Analysis, Raman Candida Deep Learning Support Vector Machine Bacterial Infections Microbial Sensitivity Tests Principal Component Analysis Escherichia coli Klebsiella Pseudomonas aeruginosa Enterococcus Salmonella enterica Streptococcus
View on PubMed DOI

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jul 16, 2019
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...
Principal Component Analysis Optical Imaging Shiga-Toxigenic Escherichia coli Bacterial Typing Techniques Microscopy Humans Algorithms Deep Learning Image Processing, Computer-Assisted Food Microbiology Foodborne Diseases
View on PubMed DOI
Popular Topics
Recent Journals