ATRP HOME
Page 1 - Researchers Validate Optical Biosensorís Detection Capability on Live Birds Experimentally Infected with the Avian Influenza Virus
Page 2 - Study Underway to Examine Which Poultry Processing Tasks May Contribute to Lower Back Injury
Page 2 - The Applicability of Using the NIOSH Lifting Equation in a Poultry Processing Plant
Page 3 - Integrating Food Safety Technologies Directly into Processing Equipment
Page 3 - New Study Provides Recommendations to Improve Product Tracing in Food Systems
Page 4 - Still Waiting on the SIP
Page 5 - Visit ATRP's New Booth Location, 3526 - Building B, at the 2010 International Poultry Expo
Page 5 - Mark Your Calendars: The 2010 National Safety Conference for the Poultry Industry
Page 6 - Poultry World Recognizes Volunteers
Page 7 - New Safety Issues Kit Now Available from the Poultry & Egg Institute

Researchers Validate Optical Biosensor’s Detection Capability on Live Birds Experimentally Infected with the Avian Influenza Virus

To detect the presence of the avian influenza virus (AI), commonly known as bird flu, the optical biosensor uses a concept known as waveguide interferometry to precisely determine how many virus particles attach to the receptors on the biosensorís surface (waveguide shown in inset photo on right).

To detect the presence of the avian influenza virus (AI), commonly known as bird flu, the optical biosensor uses a concept known as waveguide interferometry to precisely determine how many virus particles attach to the receptors on the biosensor’s surface (waveguide shown in inset photo on right).

Last fall, PoultryTech reported on the potential of using an optical biosensor developed by the Georgia Tech Research Institute (GTRI) for the detection of the avian influenza (AI) virus, commonly known as bird flu (PoultryTech, Vol. 20, No. 3, Fall 2008). The optical sensor boasts several advantages over traditional AI identification methods: it is low cost, easy to use, field-deployable, and provides rapid results — less than 30 minutes. Recently, GTRI researchers teamed with colleagues at the U.S. Department of Agriculture’s Southeast Poultry Research Laboratory to validate the sensor’s detection capabilities with experimentally infected live chickens.

To detect the presence of AI, the optical biosensor uses a concept known as waveguide interferometry to precisely determine how many virus particles attach to the receptors on the biosensor’s surface. For the validation tests, sensing assays were tested against low-pathogenic H5 and H7 avian influenza strains in a “field use” manner. Split samples (oropharyngeal swabs) were collected each day for seven days post inoculation from 100 experimentally infected four-week-old broiler chickens. These samples were tested using the optical biosensor and two other diagnostic methods: real-time reverse transcriptase polymerase chain reaction (RRT-PCR) and the Synbiotics dipstick immunoassay.

Sample results from each of the three methods were then evaluated and compared for detection sensitivity and specificity as well as other performance factors. Results of the evaluation are summarized in the table below. The RRT-PCR method was used as the standard for the comparison.

The optical biosensor offers sensitivities ranging from 69 percent to 99 percent and detection specificity ranging from 90 percent to 96 percent, depending on the strains tested and the bioreceptor used. The current field-usable dipstick-based method has only 1 percent to 33 percent detection sensitivity. The experiment results also indicated that most of the birds shed measurable levels of viruses on DPI1 (day post inoculation). Viruses were found by both the optical biosensor method and PCR starting at DPI1 and continued through DPI7 in some birds. Generally, the higher level of virus shedding was found in samples collected on DPI3 and DPI4, while samples collected on DPI7 contained lower amounts of the viruses.

Researchers believe that considering the optical biosensor’s performance (detection sensitivity and specificity), cost, portability, field-usability, and ease of use, it can be an excellent tool for AI surveillance and outbreak control.

“How fast we can identify the circulating viruses is critical to the implementation of timely and adequate prevention and control strategies,” says Dr. Jie Xu, GTRI senior research engineer and project director. “The development of the waveguide sensor-based technology as an AI diagnostic tool is a promising technology that could meet this goal. In addition, if the system is more affordable than current devices but retains sensitivity and selectivity performance comparable to current analytical techniques, then wide-scale deployment is possible providing protection to both human and animal populations.”

According to Xu, the optical biosensor technology also has the capability of simultaneous detection of a variety of different viral strains through multiple channels on the same waveguide chip. This capability provides several benefits: parallel processing of samples, rapid subtyping when strain is unknown, simplified sample preparation (separate samples for each test are not required), and increases in testing throughput.

The team plans to focus on improving overall sample throughput. The number of samples that need to be tested could be high when there is an influenza outbreak, explains Xu. In addition, fecal samples could be tested as well since the sensor-based technology is contaminant-proof and fecal samples are easier to obtain.

The research is being conducted in collaboration with Dr. David Suarez, the research leader of the Exotic and Emerging Avian Viral Diseases Unit at the Southeast Poultry Research Laboratory. Funding is being provided by the Georgia Tech Research Institute’s Agricultural Technology Research Program, the Georgia Research Alliance, and the U.S. Department of Agriculture.


Comparison Results of Detection Methods

Performance
Metric

 

RRT-PCR*

 

Optical
Biosensor

 

Dipstick
Immunoassay

Sensitivity

 

100%

 

69 - 99%

 

1 - 33%

Specificity

 

100%

 

90 - 96%

 

100%

Speed

 

~ 3 hours

 

< 30 minutes

 

15 minutes

Sample preparation

 

Required

 

Not required

 

Not required

Instrument cost

 

$50,000

 

$500 - 1,000

 

Not required

Cost per test

 

~ $20

 

$5 - 10

 

$10

Portable

 

No

 

Yes

 

Yes

Facility requirement

 

Yes

 

No

 

No

*RRT-PCR used as the standard for comparison

 

PoultryTech is published by the Agricultural Technology Research Program,
Food Processing Technology Division
of the Georgia Tech Research Institute.
Agricultural Technology Research Program – GTRI/FPTD, Atlanta, GA 30332-0823
Phone: (404) 894-3412 • FAX: (404) 894-8051
Angela Colar - Editor - angela.colar@gtri.gatech.edu