Researchers from the School of Science at Indiana University-Purdue University Indianapolis (IUPUI) and the Bindley Bioscience Center at Purdue University have developed a novel approach to automated detection and classification of harmful bacteria in food.
The investigators have designed and implemented a sophisticated statistical approach that allows computers to improve their ability to detect the presence of bacterial contamination in tested samples. These formulas propel machine-learning, enabling the identification of known and unknown classes of food pathogens.
The study appears in the October issue of the journal Statistical Analysis and Data Mining.
"The sheer number of existing bacterial pathogens and their high mutation rate makes it extremely difficult to automate their detection," said M. Murat Dundar, Ph.D., assistant professor of computer science in the School of Science at IUPUI and the university's principal investigator of the study. "There are thousands of different bacteria subtypes and you can't collect enough subsets to add to a computer's memory so it can identify them when it sees them in the future. Unless we enable our equipment to modify detection and identification based on what it has already seen, we may miss discovering isolated or even major outbreaks."
To detect and identify colonies of pathogens such as listeria, staphylococcus, salmonella, vibrio and E. coli based on the optical properties of their colonies, the researchers used a prototype laser scanner, developed by Purdue University researchers. Without the new enhanced machine-learning approach, the light-scattering sensor used for classification of bacteria is unable to detect classes of pathogens not explicitly programmed into the system's identification procedure.
"We are very excited because this new machine-learning approach is a major step towards a fully automated identification of known and emerging pathogens in real time, hopefully circumventing full-blown, food-borne illness outbreaks in the near future. Ultimately we would like to see this deployed to tens of centers as part of a national bio-warning system," said Dundar.
"Our work is not based on any particular property of light scattering detection and therefore it can potentially be applied to other label-free techniques for classification of pathogenic bacteria, such as various forms of vibrational spectroscopy," added Bartek Rajwa, Ph.D., the Purdue principal investigator of the study.
Dundar and his colleagues believe this methodology can be expanded to the analysis of blood and other biological samples as well.
This study was supported by a grant from the National Institute of Allergy and Infectious Diseases.
Co-authors of "A Machine-Learning Approach to Detecting Unknown Bacterial Serovars" study in addition to Dundar and Rajwa are Ferit Akova, a graduate student at the School of Science at IUPUI, and Purdue University researchers V. Jo Davisson, E. Daniel Hirleman, Arun K. Bhunia, and J. Paul Robinson.
Cindy Fox Aisen | EurekAlert!
Study relating to materials testing Detecting damages in non-magnetic steel through magnetism
23.07.2018 | Technische Universität Kaiserslautern
Innovative genetic tests for children with developmental disorders and epilepsy
11.07.2018 | Christian-Albrechts-Universität zu Kiel
New design tool automatically creates nanostructure 3D-print templates for user-given colors
Scientists present work at prestigious SIGGRAPH conference
Most of the objects we see are colored by pigments, but using pigments has disadvantages: such colors can fade, industrial pigments are often toxic, and...
Scientists at the University of California, Los Angeles present new research on a curious cosmic phenomenon known as "whistlers" -- very low frequency packets...
Scientists develop first tool to use machine learning methods to compute flow around interactively designable 3D objects. Tool will be presented at this year’s prestigious SIGGRAPH conference.
When engineers or designers want to test the aerodynamic properties of the newly designed shape of a car, airplane, or other object, they would normally model...
Researchers from TU Graz and their industry partners have unveiled a world first: the prototype of a robot-controlled, high-speed combined charging system (CCS) for electric vehicles that enables series charging of cars in various parking positions.
Global demand for electric vehicles is forecast to rise sharply: by 2025, the number of new vehicle registrations is expected to reach 25 million per year....
Proteins must be folded correctly to fulfill their molecular functions in cells. Molecular assistants called chaperones help proteins exploit their inbuilt folding potential and reach the correct three-dimensional structure. Researchers at the Max Planck Institute of Biochemistry (MPIB) have demonstrated that actin, the most abundant protein in higher developed cells, does not have the inbuilt potential to fold and instead requires special assistance to fold into its active state. The chaperone TRiC uses a previously undescribed mechanism to perform actin folding. The study was recently published in the journal Cell.
Actin is the most abundant protein in highly developed cells and has diverse functions in processes like cell stabilization, cell division and muscle...
17.08.2018 | Event News
08.08.2018 | Event News
27.07.2018 | Event News
17.08.2018 | Physics and Astronomy
17.08.2018 | Information Technology
17.08.2018 | Life Sciences