Detection Of Bacteria In Food Research Articles

Erratum for Ma et al., "Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging".

Erratum for Ma et al., "Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging".

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E. coli at the microcolony stage after a 3-h cultivation. Integrating with phase-contrast microscopic imaging, YOLOv4 discriminated E. coli from seven other common foodborne bacterial species with an average precision of 94%. This approach also enabled the rapid quantification of E. coli concentrations over 3 orders of magnitude with an R2 of 0.995. For romaine lettuce spiked with E. coli (10 to 103 CFU/g), the trained YOLOv4 detector had a false-negative rate of less than 10%. This approach accelerates analysis and avoids manual result determination, which has the potential to be applied as a rapid and user-friendly bacterial sensing approach in food industries. IMPORTANCE A simple, cost-effective, and rapid method is desired to identify potential pathogen contamination in food products and thus prevent foodborne illnesses and outbreaks. This study combined artificial intelligence (AI) and optical imaging to detect bacteria at the microcolony stage within 3 h of inoculation. This approach eliminates the need for time-consuming culture-based colony isolation and resource-intensive molecular approaches for bacterial identification. The approach developed in this study is broadly applicable for the identification of diverse bacterial species. In addition, this approach can be implemented in resource-limited areas, as it does not require expensive instruments and significantly trained human resources. This AI-assisted detection not only achieves high accuracy in bacterial classification but also provides the potential for automated bacterial detection, reducing labor workloads in food industries, environmental monitoring, and clinical settings.

A System of Rapidly Detecting Escherichia Coli in Food Based on a Nanoprobe and Improved ATP Bioluminescence Technology.

Bacterial contamination is an important factor causing food security issues. Among the bacteria, Escherichia coli is one of the main pathogens of food-borne microorganisms. However, traditional bacterial detection approaches cannot meet the requirements of real-time and on-site detection. Thus, it is of great significance to develop a rapid and accurate detection of bacteria in food to ensure food safety and safeguard human health. The pathogen heat-treatment module was designed in this paper based on the techniques including nanoprobe, pathogen heat-treatment, graphene transparent electrode (GTE), and adenosine triphosphate (ATP) bioluminescence technology. The system mainly consists of two parts: one is the optical detection unit; the other is the data processing unit. And it can quickly and automatically detect the number of bacterial colonies in food such as milk etc. The system uses not only the probe to capture and enrich E. coli by antigen-antibody interaction but also the heat treatment to increase the amount of ATP released from bacterial cells within five minutes. To enhance the detecting accuracy and sensitivity, the electric field generated by GTE is adopted in the system to enrich ATP. Compared to the other conventional methods, the linear correlation coefficient of the system can be reached 0.975, and the system meets the design requirements. Under the optimal experimental conditions, the detection can be completed within 25 min, and the detectable concentration of bacteria is in the range of 3.1 × 101–106 CFU/mL. This system satisfies the demands of a fast and on-site inspection.

Rapid Detection of Bacteria in Food Using Static Headspace-Comprehensive Two-Dimensional Gas Chromatography (Hs-Gc×Gc)

Rapid Detection of Bacteria in Food Using Static Headspace-Comprehensive Two-Dimensional Gas Chromatography (Hs-Gc×Gc)

"Rapid and Practical Screening Method for the Detection of Colistin-Resistant Bacteria in Food"

"Rapid and Practical Screening Method for the Detection of Colistin-Resistant Bacteria in Food"

Detection of Escherichia coli O157:H7 Using Automated Immunomagnetic Separation and Enzyme-Based Colorimetric Assay.

The food industry requires rapid and simple detection methods for preventing harm from pathogenic bacteria. Until now, various technologies used to detect foodborne bacteria were time-consuming and laborious. Therefore, we have developed an automated immunomagnetic separation combined with a colorimetric assay for the rapid detection of E. coli O157:H7 in food samples. The colorimetric detection method using enzymatic reaction is fascinating because of its simplicity and rapidity and does not need sophisticated devices. Moreover, the proposed procedures for the detection of bacteria in food take less than 3 h including pre-enrichment, separation and detection steps. First, target-specific immunomagnetic beads were introduced to contaminated milk in a pre-enrichment step. Second, the pre-enriched sample solution containing target bacteria bound on immunomagnetic beads was injected into an automated pretreatment system. Subsequently, the immunomagnetic beads along with target bacteria were separated and concentrated into a recovery tube. Finally, released β-galactosidase from E. coli O157:H7 after lysis was reacted with chlorophenol red β-galactopyranoside (CPRG) used as a substrate and the colorimetric change of CPRG was determined by absorbance measuring or the naked eye. By the proposed approach in this study, we could detect 3 × 102 CFU/mL of E. coli O157:H7 from a milk sample within 3 h.

Colorimetric detection of Escherichia coli using engineered bacteriophage and an affinity reporter system.

Reporter phage systems have emerged as a promising technology for the detection of bacteria in foods and water. However, the sensitivity of these assays is often limited by the concentration of the expressed reporter as well as matrix interferences associated with the sample. In this study, bacteriophage T7 was engineered to overexpress mutated alkaline phosphatase fused to a carbohydrate-binding module (ALP*-CBM) following infection of E. coli to enable colorimetric detection in a model system. Magnetic cellulose particles were employed to separate and concentrate the overexpressed ALP*-CBM in bacterial lysate. Infection of E. coli with the engineered phage resulted in a limit of quantitation of 1.2 × 105 CFU, equating to 1.2 × 103 CFU/mL in 3.5h when using a colorimetric assay and 100mL sample volume. When employing an enrichment step, < 101CFU/mL could be visually detected from a 100mL sample volume within 8h. These results suggest that affinity tag modified enzymes coupled with a material support can provide a simple and effective means to improve signal sensitivity of phage-based assays. Graphical abstract.

‘Off–on’ switchable fluorescent probe for prompt and cost-efficient detection of bacteria

The rapid and straightforward detection of bacteria in food and human samples is becoming important, particularly in view of the development of point-of-care devices and lab-on-a-chip tools for prevention and treatment of bacterial infections.

Magnetoelastic sensors with hybrid films for bacteria detection in milk

Currently the detection of bacteria in food needs to be quick and accurate to avoid health problems. An alternative to enable the rapid detection of microorganisms in food is the use of magnetoelastic sensors. These sensors allow the detection and quantitation of bacteria via sensor mass variation. In addition to speed and simplicity, the great advantage is remote sensing, without the use of direct physical connections (wireless monitoring). The objective of this research is to develop a sensor for the detection of bacteria in milk using a magnetoelastic alloy (Metglas 2826MB3) coated with a hybrid film based on silicon alkoxide precursors (TEOS and MAP) without the use of antibodies. Resonance frequency variation were measured and the sensor responded to mass variation and allowed the detection of the presence of bacteria (Escherichia coli and Staphylococcus aureus) in different concentrations, with the sensor remaining intact throughout the test period.

Graphene-Based FET Detector forE. coliK12 Real-Time Monitoring and Its Theoretical Analysis

This paper presents a theoretical analysis for a graphene-based FET real-time detector of the target bacteriaE. coliK12. The motivation for this study is to design a sensor device for detection of bacteria in food and water in order to guarantee food safety. Graphene is chosen as our material for sensor design, which has outstanding electrical, physical, and optical performance. In our sensor structure, graphene-based solution gate field effect transistor (FET) is the device model; fabrication and functionalization protocol are presented together in this paper. What is more, a real-time signal display system is the accompanied equipment for our designed biosensor device. In this system, the sensor bias current signalIdswould change obviously when the target bacteria are attached to the sensor surface. And the bias currentIdsincreases when theE. coliconcentration increases. In the latter part, a theoretical interpretation of the sensor signal is to explain the bias currentIdsincreasing after theE. coliK12 attachment.

A Novel Magneto-Fluorescent Biosensor for the Detection of Pathogens in Food

Food safety has emerged as an important global issue. Illness from the pathogenic bacteria is a significant health concern. Pathogens are potentially harmful and it contaminate food and causes foodborne illness . It is very important for early detection of food borne pathogens. Conventional methods of detecting these pathogens eventhough sensitive, but still it is time consuming,complex and laborious. Biosensors have great potential for the detection of pathogenic bacteria in food. Biosensors are developed to rapidly detect the pathogens. In this paper, we use nanosized magnetite nanoparticles coated with chitosan, a polymer to capture and identify bacteria from contaminated food sample and eliminates the purpose of specific antibody coating. Amine group with magnetite nanoparticles (MNPs) specifically binds with the bacterial cell wall, on their surface of most pathogenic bacteria present in food. Rhodamine as a dye/marker which emits fluorescence in exposure to light used to detect the bacterial concentration in terms of light or fluorescent intensity and analog voltage using Magneto-Fluorescent biosensor. The developed biosensor could be able to detect bacteria in the limit of 10 CFU/ml and the time taken for measurement of a sample using biosensor would be less than 5 minutes.Hence the developed biosensor are non-specific and highly sensitive.

A simple cassette as point-of-care diagnostic device for naked-eye colorimetric bacteria detection

Effective pathogen detection is necessary for treatment of infectious diseases. Point of care (POC) devices have tremendously improved the global human heath. However, design criteria for sample processing POC devices for pathogen detection in limited infrastructure are challenging and can make a significant contribution to global health by providing rapid and sensitive detection of bacteria in food, water, and patient samples. In this paper, we demonstrate a novel portable POC diagnostic device that is simple to assemble for genetic detection of bacterial pathogens by isothermal DNA amplification. The device is fabricated with very low production cost, using simple methods and easy-to-access materials on a flexible ribbon polyethylene substrate. We showed that the device is capable of detection of 30 CFU mL(-1) of E. coli and 200 CFU mL(-1) of S. aureus in less than 1 hour. Through numerical simulations, we estimated that the device can be extended to high-throughput detection simultaneously performing a minimum of 36 analyses. This robust and sensitive detection device can be assembled and operated by non-specialist personnel, particularly for multiple bacterial pathogen detections in low-resource settings.

Nonlabeled quartz crystal microbalance biosensor for bacterial detection using carbohydrate and lectin recognitions.

High percentages of harmful microbes or their secreting toxins bind to specific carbohydrate sequences on human cells at the recognition and attachment sites. A number of studies also show that lectins react with specific structures of bacteria and fungi. In this report, we take advantage of the fact that a high percentage of microorganisms have both carbohydrate and lectin binding pockets at their surface. We demonstrate here for the first time that a carbohydrate nonlabeled mass sensor in combination with lectin-bacterial O-antigen recognition can be used for detection of high molecular weight bacterial targets with remarkably high sensitivity and enhanced specificity. A functional mannose self-assembled monolayer in combination with lectin concanavalin A (Con A) was used as molecular recognition elements for the detection of Escherichia coli W1485 using a quartz crytsal microbalance (QCM) as a transducer. The multivalent binding of Con A to the E. coli surface O-antigen favors the strong adhesion of E. coli to the mannose-modified QCM surface by forming bridges between these two. As a result, the contact area between cell and QCM surface that increases leads to rigid and strong attachment. Therefore, it enhances the binding between E. coli and the mannose. Our results show a significant improvement of the sensitivity and specificity of the carbohydrate QCM biosensor with a experimental detection limit of a few hundred bacterial cells. The linear range is from 7.5 x 10(2) to 7.5 x 10(7) cells/mL, which is four decades wider than the mannose-alone QCM sensor. The change of damping resistances for E. coli adhesion experiments was no more than 1.4%, suggesting that the bacterial attachment was rigid, rather than a viscoelastic behavior. Little nonspecific binding was observed for Staphylococcus aureus and other proteins (fetal bovine serum, Erythrina cristagalli lectin). Our approach not only overcomes the challenges of applying QCM technology for bacterial detection but also increases the binding of bacteria to their carbohydrate receptor through bacterial surface binding lectins that significantly enhanced specificity and sensitivity of QCM biosensors. Combining carbohydrate and lectin recognition events with an appropriate QCM transducer can yield sensor devices highly suitable for the fast, reversible, and straightforward on-line screening and detection of bacteria in food, water, and clinical and biodefense areas.

New Molecular Methods for the Detection of Bacteria in Food

In order to maintain food safety standards, conventional microbiological methods are still being used to detect bacteria and other organisms in food. However, these techniques are not ideal, as often it can be many days before results are known‐which may be of particular economic importance for those foods with a short shelf‐life. The introduction of newer technology, such as nucleic acid probe and related amplification technology in other fields, has transformed the detection of many organisms. The Polymerase Chain Reaction (PCR) allows nucleic acid probes, with their inherent specificity, to be used to detect organisms present in very low numbers within a short period of time. However, at present, in food microbiology, there are technical problems with using the PCR, as certain components in food interfere with the reaction. When these problems are resolved and with prospects for semi‐automation of the PCR technique, there should be enormous potential for the rapid detection of bacteria in foods, with consequent benefits to the food industry and to consumers.

Enumeration of bacteria in pure cultures and in foods using a commercial flow cytometer

Direct microscopic detection of bacteria in foods can suffer from interference by particulate matter. Flow cytometry enables multiparameter counting of bacteria based on light scattering by cells and fluorescence and this could facilitate the discrimination of bacteria from background material. Use of the Skatron Argus 100 flow cytometer enabled the counting of pure cultures of bacteria to <10 3 cfu ml −1 within a few minutes. Application of the technique to meat samples gave a good correlation with plate counts and enabled enumeration down to at least 10 5 cfu g −1 within minutes. However, with samples of pate´and milk, sensitivity was reduced.

DNA probes for infectious diseases

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