News Column

Laser Tool Speeds Up Detection of Salmonella in Food Products

February 12, 2014



WEST LAFAYETTE, Ind., Feb. 12 -- Purdue University issued the following news release:

Purdue University researchers have developed a laser sensor that can identify Salmonella bacteria grown from food samples about three times faster than conventional detection methods.

Known as BARDOT (pronounced bar-DOH'), the machine scans bacteria colonies and generates a distinct black and white "fingerprint" by which they can be identified. BARDOT takes less than 24 hours to pinpoint Salmonella.

"BARDOT allows us to detect Salmonella much earlier and more easily than current methods," said Arun Bhunia, a professor of food science who collaborated with then-Purdue engineer Daniel Hirleman to create the machine. "This could ultimately help provide safer food to consumers."

Salmonella is a major foodborne pathogen that causes salmonellosis, a type of food poisoning with symptoms of diarrhea, fever and abdominal cramps. Salmonellosis can be fatal in young children, the elderly and those with compromised immune systems.

The U.S. Food and Drug Administration has a zero-tolerance policy for Salmonella in food products. If the bacteria is detected, the resulting product recalls can lead to significant financial loss and possible charges of criminal liability for the companies involved.

Current Salmonella detection methods can take 72 hours to yield results and often require artificial alteration of the bacteria colonies. But the BARDOT system identifies bacteria colonies by using light to illuminate their natural characteristics, preserving the colonies for later study. The machine can be operated with minimal training and used in locations with limited resources, Bhunia said.

BARDOT, short for "bacterial rapid detection using optical scatter technology," uses a red diode laser to scan bacteria colonies on an agar plate. When the light penetrates a colony, it produces a scatter pattern, a unique arrangement of rings and spokes that resembles the iris of an eye. The pattern is matched against a library of images to identify the type of bacteria.

To test BARDOT's ability to identify Salmonella, Bhunia and his fellow researchers grew bacteria from rinses of contaminated chicken, spinach and peanut butter on agar plates for about 16 hours. After the plates were covered with tiny spherical colonies of bacteria, they placed each plate inside BARDOT - which is about the size of a large microwave oven - and scanned the colonies.

BARDOT identified Salmonella bacteria with an accuracy of 95.9 percent. It also individually distinguished eight of the most prevalent Salmonella serovars - distinct variations within a species of bacteria. Identifying a particular serovar helps trace bacteria to the original source of contamination.

Atul Singh, postdoctoral research associate and first author of the study, said BARDOT could be an effective preliminary screening tool, especially for food processors testing a large number of samples.

"BARDOT screens quickly and inexpensively," he said. "If you get a positive result for Salmonella, you can do a follow-up test. This can help food processors make more informed decisions."

While many tools can only detect a single kind of bacteria, BARDOT picks out multiple types of disease-causing bacteria on a plate with a single scan, Bhunia said. In addition to Salmonella, BARDOT can identify Escherichia coli, Vibrio, Listeria, Bacillus and many more foodborne pathogens.

"That's the beauty of this system," Bhunia said. "It's so versatile. It can find organisms that you didn't even think about."

The paper was published in mBio and is available at http://mbio.asm.org/content/5/1/e01019-13

Lixia Liu, Brent Barrett, Mark Forster and Judith Lovchik of the Indiana State Department of Health also collaborated on the study.

The research was conducted using funds from the U.S. Department of Agriculture, the National Institute of Allergy and Infectious Diseases and the Center for Food Safety Engineering at Purdue.

Writer: Natalie van Hoose, 765-496-2050, nvanhoos@purdue.edu

Sources: Arun Bhunia, 765-494-5443, bhunia@purdue.edu

Atul Singh, 765-494-6236, aksingh@purdue.edu

BARDOT is commercially available through Advanced BioImaging Systems of West Lafayette, Ind. The company can be contacted at 765-807-0772.

ABSTRACT

Laser Optical Sensor, a Label-Free On-Plate Salmonella enterica Colony-Detection Tool

Atul K. Singh 1; Amanda M. Bettasso 1*; Euiwon Bae 2; Bartek Rajwa 3; Murat M. Dundar 4; Mark D. Forster 5; Lixia Liu 5; Brent Barrett 5; Judith Lovchik 5; J. Paul Robinson 3, 6; E. Daniel Hirleman 2*; Arun K. Bhunia 1, 7

1 Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, Indiana, USA

2 School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA

3 Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, USA

4 Computer & Information Science Department, Indiana University-Purdue University at Indianapolis, Indianapolis, Indiana, USA

5 Indiana State Department of Health, Indianapolis, Indiana, USA

6 Department of Basic Medical Sciences, Purdue University, West Lafayette, Indiana, USA

7 Department of Comparative Pathobiology, Purdue University, West Lafayette, Indiana, USA

*Present addresses: Amanda M. Bettasso: NewlyWeds Foods, Inc., Springdale, AR, USA; E. Daniel Hirleman: School of Engineering, University of California, Merced, California, USA

E-mail: bhunia@purdue.edu

We investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.9%) was obtained by using scatter signatures collected from colonies grown on XLT4. Further verification used a total of 36 serovars (the top 20 plus 16) comprising 123 strains with classification precision of 88 to100%. The similarities between the optical phenotypes of strains analyzed by BARDOT were in general agreement with the genotypes analyzed by pulsed-field gel electrophoresis (PFGE). BARDOT was evaluated for the real-time detection and identification of Salmonella colonies grown from inoculated (1.2102 CFU/30g) peanut butter, chicken breast, and spinach or from naturally contaminated meat. After a sequential enrichment in buffered peptone water and modified Rappaport Vassiliadis broth for 4 h each, followed by growth on XLT4 (~16 h), BARDOT detected S. Typhimurium with 84% accuracy in 24 h, returning results comparable to the USDA Food Safety and Inspection Service method, which requires ~72 h. BARDOT also detected Salmonella (90 to100% accuracy) in the presence of background microbiota from naturally contaminated meat, verified by 16S rRNA sequencing and PFGE. Prolonged residence (28 days) of Salmonella in peanut butter did not affect the bacterial ability to form colonies with consistent optical phenotypes. The study shows BARDOT's potential for non-destructive and high-throughput detection of Salmonella in food samples.

TNS 30TagarumaMar-140213-4636427 30TagarumaMar


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