AIMC Topic: Food Microbiology
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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...
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...
Machine learning-enabled colorimetric sensors for foodborne pathogen detection.
Advances in food and nutrition research
Jun 29, 2024
In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for ...
Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.
Food research international (Ottawa, Ont.)
Jun 19, 2024
Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a major source. However, the prevalence of Salmonella in different retail chicken supply modes and the threat posed to consumers remains unclear. The preval...
Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning.
International journal of food microbiology
Jun 8, 2024
The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promot...
Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.
Veterinary research
Jun 5, 2024
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited dat...
Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions.
Food research international (Ottawa, Ont.)
May 10, 2024
Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and preven...
Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens.
Analytical chemistry
Apr 15, 2024
How timely identification and determination of pathogen species in pathogen-contaminated foods are responsible for rapid and accurate treatments for food safety accidents. Herein, we synthesize four aggregation-induced emissive nanosilicons with diff...
Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra.
Small (Weinheim an der Bergstrasse, Germany)
Apr 2, 2024
Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne b...
Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning.
Meat science
Mar 31, 2024
Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns...
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