• Food Science and Biotechnology
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• v.16 no.3
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• pp.337-342
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• 2007

Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Fiber-optic biosensors have been used to rapidly detect pathogens because they can be very sensitive and are simple to operate. However, many fiber-optic biosensors rely on manual sensor handling and the sandwich assay, which require more effort and are less sensitive. To increase the simplicity of operation and detection sensitivity, a binding inhibition assay method for detecting Listeria monocytogenes in food samples was developed using an automated, fiber-optic-based immunosensor: RAPTOR (Research International, Monroe, WA, USA). For the assay, fiber-optic biosensors were developed by the immobilization of Listeria antibodies on polystyrene fiber waveguides through a biotin-avidin reaction. Developed fiber-optic biosensors were incorporated into the RAPTOR to evaluate the detection of L. monocytogenes in frankfurter samples. The binding inhibition method combined with RAPTOR was sensitive enough to detect L. monocytogenes ($5.4{\times}10^7\;CFU/mL$) in a frankfurter sample.

• The Plant Pathology Journal
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• v.39 no.5
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• pp.522-527
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• 2023

The occurrence of postharvest kiwifruit rot has caused great economic losses in major kiwifruit-producing countries. Several pathogens are involved in kiwifruit rot, notably Botryosphaeria dothidea, and Diaporthe species. In this study, a recombinase polymerase amplification (RPA) assay was developed for the rapid and sensitive detection of the pathogens responsible for posing significant threats to the kiwifruit industries. The RPA primer pairs tested in this study were highly specific for detection of B. dothidea and D. eres. The detection limits of our RPA assays were approximately two picograms of fungal genomic DNA. The optimal conditions for the RPA assays were determined to be at a temperature of 39℃ maintained for a minimum duration of 5 min. We were able to detect the pathogens from kiwifruit samples inoculated with a very small number of conidia. The RPA assays enabled specific, sensitive, and rapid detection of B. dothidea and D. eres, the primary pathogens responsible for kiwifruit rots in South Korea.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.143.2-143
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• 2003

A rapid and sensitive assay for the specific detection of plant viroids using reverse transcription-polymerase chain reaction(RT-PCR) has been developed already. The nested RT-PCR assay cloud be applied for the detection of apple scar skin viroid(ASSVd) from young leaves and other tissues. ASSVd has central conserved region(CCR), terminal left(T$\sub$L/) and terminal right(T$\sub$R/) domain. Primers were designed from these regions. Primer sets were successfully applicable for the amplification of full length or partial region of ASSVd by nested RT-PCR. Nested RT-PCR assay was more sensitive and accurate method to detect ASSVd from young trees during the early time of apple cultivation.

• Journal of Food Hygiene and Safety
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• v.11 no.2
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• pp.129-132
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• 1996

Recently, as concern about the residual antibiotics in milk increase, the detection methods of residual antibiotics used extensevely at the present time were investigated and compared to their properties and the detection limits of variable antibiotics. At first, comparactive tests of the detectable sensitivity of 4 test organisms, B. cereus, B. subtilis, M.luteus, B.stearothermophilus C-953, were performed by disc assay. As a result, B.stearothermophilus was the most sensitive strain of all other strains and showe the detect limit of 5-50 ppb for penlicillins (PCs). And also, B.subitilis was showed the more effective detection limit, 200-400 ppb, for aminoglycosides (AGs) and M.luteus was showed predominant sensitivity , 50-500 ppb for macrolides(MLs) and B.cereus was the most sensitive strain for tetracyclines (TCs) and showed the detection limit of 100-400 ppb. Therefore, each test strains were showed a different sensitivity in the detection of the different antibiotic families. When the detection limit of disc assay and other methods were compared, TTCmethod was less sensitive than other methods showing 5-50 ppb detectable lebel for PCs. Also, for the detection of other antibiotic families TTC method was showed the worst sensitivity and Delvo and Charm Farm tests were similar to the detectable properties of AGs and MLs. Although disc assay was showed the similar detection limit for PCs with Delvo and Charm Farm, it was more widely effective for the detection of kanamycin, erythromycin, chlortetracycline, doxycycline, verginiamycin and so on than Delvo or Charm Farm. CharmII test was showed the best sensitivity for the most of antibiotics except neomycin and gentamycin. But it was necessary that different tests must be performed to each antibiotic family and so it was regarded that the effectiveness of that method was low.

• Food Engineering Progress
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• v.21 no.3
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• pp.191-200
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• 2017

There have been great efforts to develop a rapid and sensitive detection method to monitor the presence of pathogenic bacteria in food. While a number of methods have been reported for bacterial detection with a detection limit to a single digit, most of them are suitable only for the bacteria in pure culture or buffered solution. On the other hand, foods are composed of highly complicated matrices containing carbohydrate, fat, protein, fibers, and many other components whose composition varies from one food to the other. Furthermore, many components in food interfere with the downstream detection process, which significantly affect the sensitivity and selectivity of the detection. Therefore, isolating and concentrating the target pathogenic bacteria from food matrices are of importance to enhance the detection power of the system. The present review provides an introduction to the representative sample preparation strategies to isolate target pathogenic bacteria from food sample. We further describe the nucleic acid-based detection methods, such as PCR, real-time PCR, NASBA, RCA, LCR, and LAMP. Nucleic acid-based methods are by far the most sensitive and effective for the detection of a low number of target pathogens whose performance is greatly improved by combining with the sample preparation methods.

• Journal of the Optical Society of Korea
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• v.10 no.3
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• pp.130-142
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• 2006

Lab-on-a-chip technology is attracting great interest because the miniaturization of reaction systems offers practical advantages over classical bench-top chemical systems. Rapid mixing of the fluids flowing through a microchannel is very important for various applications of microfluidic systems. In addition, highly sensitive on-chip detection techniques are essential for the in situ monitoring of chemical reactions because the detection volume in a channel is extremely small. Recently, a confocal surface enhanced Raman spectroscopic (SERS) technique, for the highly sensitive biological analysis in a microfluidic sensor, has been developed in our research group. Here, a highly precise quantitative measurement can be obtained if continuous flow and homogeneous mixing condition between analytes and silver nano-colloids are maintained. Recently, we also reported a new analytical method of DNA hybridization involving a PDMS microfluidic sensor using fluorescence energy transfer (FRET). This method overcomes many of the drawbacks of microarray chips, such as long hybridization times and inconvenient immobilization procedures. In this paper, our recent applications of the confocal Raman/fluorescence microscopic technology to a highly sensitive lab-on-a-chip detection will be reviewed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1155-1172
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• 2015

In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.

• Journal of Biosystems Engineering
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• v.31 no.2 s.115
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• pp.128-134
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• 2006

Frequent outbreaks of foodborne illness demand the need for rapid and sensitive methods for detection of these pathogens. Recent development of biosensor technology has a great potential to meet the need for rapid and sensitive pathogens detection from foods. An antibody-based fiber-optic biosensor and an automated reagents supply system to detect Listeria monocytogenes were developed. The biosensor for detection of Listeria monocytogenes in PBS and bacteria spiked food samples was evaluated. The automated reagents supply system eliminated cumbersome sample and detection antibody injection procedures that had been done manually. The biosensor could detect $10^4$ cfu/ml of Listeria monocytogenes in PBS. By using the fiber-optic biosensor, $2x10^8$ cfu/ml of Listeria monocytogenes in the food samples were detectable.

• The Plant Pathology Journal
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• v.36 no.5
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• pp.497-502
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• 2020

Barley yellow dwarf virus (BYDV) is an economically important plant pathogen that causes stunted growth, delayed heading, leaf yellowing, and purple leaf tip, thereby reducing the yields of cereal crops worldwide. In the present study, a reverse transcription recombinase polymerase amplification (RT-RPA) assay was developed for the detection of BYDV in oat leaf samples. The RT-RPA assay involved incubation at an isothermal temperature (42℃) and could be performed rapidly in 5 min. In addition, no cross-reactivity was observed to occur with other cereal-infecting viruses, and the method was 100 times more sensitive than conventional reverse transcription polymerase chain reaction. Furthermore, the assay was validated for the detection of BYDV in both field-collected oat leaves and viruliferous aphids. Thus, the RT-RPA assay developed in the present study represents a simple, rapid, sensitive, and reliable method for detecting BYDV in oats.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.