Foodborne diseases are a growing public health problem. In recent years, many rapid detection methods have been reported, but most of them are still in lab research and not practical for use in the field.
In this study, a portable and automatic biosensing instrument was designed and constructed for separation and detection of target pathogens in food samples using nanobead-based magnetic separation and quantum dots (QDs)-labeled fluorescence measurement. The instrument consisted of a laptop with LabVIEW software, a data acquisition card (DAQ), a fluorescent detector, micro-pumps, stepper motors, and 3D printed tube holders.
First, a sample in a syringe was mixed with magnetic nanobead-antibody (MNB-Ab) conjugates and then injected to a low binding reaction tube. After incubation and magnetic separation, target bacterial cells were captured and collected and the solution was pumped out. Then the QD-antibody (QD-Ab) conjugates were pumped into the reaction tube to form the MNB-Ab-cell-Ab-QD complexes that were then collected by magnetic separation and resuspended in PBS buffer solution through air pressure control.
Finally, the sample solution was pushed into the detection tube by an air pump and the fluorescence intensity was measured using a fluorescent detector. A virtual instrument (VI) was programmed using LabVIEW software to provide a platform for magnetic separation, fluorescent measurement, data processing, and control.The DAQ was used for data communication.
The results showed that the separation efficiency of this instrument was 78.3 ± 3.4% and 60.7 ± 4.2% for E. coliO157:H7 in pure culture and ground beef samples, respectively.The limit of detection was 3.98 × 103 and 6.46 × 104 CFU/mL in pure culture and ground beef samples, respectively. Sample preparation and detection could be finished in 2 hours. The instrument was portable and automatic with great potential to serve as a more effective tool for in-field/on-line detection of foodborne pathogenic bacteria in food products.
The goal of this study was to develop an automatic and portable biosensing instrument that could separate and detect target foodbornepathogen in the field using nanobead-based magnetic separation and quantum dot-labeled fluorescent measurement. And in this research, E .coliO157:H7 used as target bacteria.The specific objectives of this research were:
- To design and fabricate the integrated biosensing system containing solution delivery, magnetic separation, and fluorescence detection with virtual instrument.
- To evaluate the biosensing system for its magnetic separation efficiency using surface plating method.
- To evaluate the biosensing system for its specificity with tests on target
and non-target bacteria.
- To evaluate the biosensing system for its sensitivity with tests on different concentrations of target bacteria.
Escherichia coli O157:H7:
E. coliO157:H7 is an enterohemorrhagic serotype of the bacterium E.coli, and is one of the Shiga toxin-producing types of E. coli. E. coliO157:H7 was first recognized as a human pathogen in two outbreaks of hemorrhagic colitis in 1982 (Mead et al., 1998; Besser et al., 1999; Fernandez, 2008).
Consumption of contaminated foods or unclean water is the most frequent way to transmit E. coli O157:H7. Commonly, E. coliO157:H7 is associated with ground beef, but other foods also have high risk of carrying the pathogen, such as unpasteurized milk and milk products, unclean leafy green that came in contact with animal feces, and contaminated water (Mead et al., 1998; Besser et al., 1999). E. coliO157:H7 infection can cause vomiting, severe acute hemorrhagic diarrhea and Hemolytic Uremic Syndrome (HUS) (Mead et al., 1998; Besser et al., 1999; Fernandez, 2008).
Magnetic Separation of Bacteria in Food Samples:
Magnetic separation is a process in which magnetically susceptible material is extracted from a mixture using a magnetic force. Magnetic beads (MBs) have been widely used in biotechnology and biomedical research fields.
The magnetic beads have the capability to be coated in a variety of chemicals, proteins, or functional groups which can be used in a vast range of applications including separation and purification, molecular detection, cancer research and treatment, drug delivery, and enzymatic reactions.
Current Methods of Foodborne Pathogen Detection:
Conventional bacterial detection methods are based on specific microbiological media to isolate and enumerate target bacterial cells in foods. These methods are also known as “gold standard methods” because of they are the only one that will recover all the viable microbes from a sample.
Also, they can offer many advantages, such as high sensitivity, low cost, and they can provide both qualitative and quantitative information on the number and the nature of microorganisms present in the food sample.
Biosensors for Bacteria Detection:
The first biosensor was reported in 1962 by scientist Leland C. Clark with the development of enzyme electrodes for the detection of glucose (Mohanty and Kougianos, 2006). Since then, many researchers form different field have come together to develop more sophisticated, reliable, and mature bio sensing devices(Mohanty and Kougianos, 2006). In recent years, the development of the modern information and nano technology make it is possible to develop rapid, sensitive and real-time biosensors for food borne pathogens detection (Seo et al., 1998; Arora et l., 2011).
Commercial Biosensor Instruments for the Detection of Food Borne Pathogens:
Biosensor technologies have high potential in developing portable and automated equipment combining with intelligent instrumentation, electronics, and multi-variate signal processing methods. In recent years, several commercial biosensor instruments have been developed.
LabVIEW Software Applications:
Since LabVIEW is a powerful toolset for process control, data fitting and signal processing, and has fast, easy and friendly user interface construction(Elliott et al., 2007), it has widely used in research and industry in recent years.
MATERIALS AND METHODS
Biological and Chemical Reagents
The Millipore water purification system (Mill-Q, Bedford, MA) provided all the water used in this study. Phosphate buffered saline (PBS, 10X) was purchased from Sigma-Aldrich (St. Louis, MO) and diluted with Milli-Q (Mill-Q, Bedford, MA) water to 10 M (pH 7.4) for use in all tests.
Surface Plating Method
Stock culture of E. coliO157:H7 from -80 °C was grown in brain heart infusion broth (BHI) at 37 °C for 18 h. For enumeration, the culture was serially diluted in PBS and then 0.1 mL of proper dilution was plated on tryptic soy agar (TSA, BD Biosciences, Franklin Lakes, NJ). The colonies was counted to determine the number of viable cells in terms of colony forming units per milliliter (CFU/mL) after the plate was incubated at 37 °C for 22-24 h.
RESULTS AND DISCUSSION
The Biosensing System:
The biosensing system we developed consisted of three parts: mechanical device, electronic circuit and control software. LabVIEW was used to develop a system control software as a virtual instrument (VI) working with the DAQ card and circuit to control the pumps and platforms.
Also this software could control fluorescent measurement software. The laptop was where all of the measured data was processed and saved. The DAQ and circuit were used as a bridge for the communication between the laptop and the device.
The mechanical device consisted of pumps, stepper motors, platforms, a detect ion box and a magnet as shown in Figure 5.2. It could be divided into two parts, the top part for magnetic separation and incubation and the bottom part mainly for fluorescent detection.
There were six components in electronic circuit: power supply, DAQ connector, digital I/O expansion circuit, stepper motor driver circuit, micro pump control circuit, and status indicator. And the schematic diagram of each part. The circuit was designed in Protel DXP 2004 software (La jolia, CA).
Sequential Control Software:
LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a system-design platform and development environment for a visual programming language from National Instruments (Austin, TX). It can be used for building visual instruments (VIs) on a computer for measurement and control and is designed to use in almost all industry areas.
Unlike the traditional programming language, LabVIEW is a graphical programming language and a typical program is written by icons instead of lines of text. LabVIEW uses dataflow programming, where the flow of data determines execution instead of instructions that determine program execution in text-based programming languages.
CONCLUSIONS AND RECOMMENDATIONS
A prototype of the portable biosensing instrument was designed and fabricated for separation and detection of foodborne pathogens using nanobead-based magnetic separation and quantum dot-labeled fluorescent measurement. E. coli O157:H7 was used as a model pathogen in this study.
The instrument could be controlled by the virtual instrument developed in LabVIEW to conduct magnetic separation, washing, add quantum dots, incubation, data processing and storage automatically. The magnetic separation efficiency of this instrument was 78.3 ± 3.4% and 60.7 ± 4.2% for E. coli O157:H7 in pure culture and ground beef samples, respectively. The limit of detection for E. coliO157:H7 was 3.98 × 103 and 6.46 × 104 CFU/mL in pure culture and ground beef samples, respectively.
The detection could be finished in 2 hours for samples simultaneously. It showed great potential to be applied to the in-field detection of foodborne pathogens in agriculture and food.
Further work on this study could concentrate on the improvement of sensitivity. This could be done with the following approaches:
- Improve the mechanical precision of the whole device so that the solution could be pushed from top to bottom easier and more stable.
- Optimize the position of pumps to reduce the length of tubing for minimizing
the loss of quantum dots-antibody conjugates in solution.
- Optimize the volumes of nanobeads, antibodies, quantum dots, and sample solution need to be used for each test to minimize the cost.
- Optimize the magnetic separation time to reduce the chance of aggregation of magnetic nanobeads.
Source: University of Arkansas
Author: Zhuo Zhao