• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.335-340
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• 2013

Purpose: This study was performed to develop a lateral flow strip sensor for the detection of pathogenic Escherichia coli O157:H7 in various samples. Also, feasibility of using an image analysis method to improve the interpretation of the strip sensor was evaluated. Methods: The lateral flow strip sensor has been fabricated based on nitrocellulose lateral-flow membrane. Colloidal gold and E. coli O157:H7 antibodies were used as a tag and a receptor, respectively. Manually spotted E. coli O157:H7 antibody and anti-mouse antibody on nitrocellulose membrane were used as test and control dots, respectively. Feasibility of the lateral flow strip sensor to detect E. coli O157:H7 were evaluated with serially diluted E. coli O157:H7 cells in PBS or food samples. Test results of the lateral flow strip sensor were measured with an image analysis method. Results: The intensity of the test dot started to increase with higher concentration of the cells were introduced. The sensitivities of the sensor were both $10^4$ CFU/mL Escherichia coli O157:H7 spiked in PBS and in chicken meat extract, respectively. Conclusions: The lateral flow strip sensor and image analysis method could detect E. coli O157:H7 in 20 min, which is significantly quicker than conventional plate counting method.

• Food Science of Animal Resources
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• v.31 no.6
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• pp.827-833
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• 2011

The objective of this study was to develop a fluorogenic real-time PCR-based assay for detecting and quantifying amounts of cow milk in cow/goat milk mixtures or goat milk products. In order to quantify the exact amount of cow milk in cow/goat raw milk mixtures and commercial goat milk products, it was necessary to achieve quantitative extraction of total genomic DNA from the raw milk matrix. Both mammalian-specific PCR and cow-specific PCR were performed. A cow-specific 252 bp band obtained from the raw cow milk and raw goat milk mixtures, commercial goat milk, and two goat milk powders was identified, along with the relationship between the cow milk amount and band intensity of the electrophoresis image. The detection threshold was found to be 0.1%. The expression of cow's 12S rRNA in the cow/goat milk mixtures, commercial goat milk, and two goat milk powders was identified. The expression quantity of the milk 12S rRNA increased with increasing ratios of the cow/goat milk mixtures. Using these calibrated relative expression levels as a standard curve in the cow/goat raw milk mixtures, the contents of cow milk were 1.8% in the commercial goat milk, 9.6% in goat milk powder A, and 11.6% in goat milk powder C. However, cow milk was not detected in goat milk powder B.

• Journal of Microbiology and Biotechnology
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• v.16 no.1
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• pp.25-31
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• 2006

Changes of CP4 5-enolpyruvylshikimate-3-phosphate synthase (CP4 EPSPS) in the glyphosate-tolerant Roundup Ready soybean were examined using purified CP4 EPSPS produced in cloned Escherichia coli as a control. CP4 EPSPS in genetically modified soybean was detected by twodimensional gel electrophoresis (2-DE) and identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) with databases. CP4 EPSPS in soybean products was resolved on 2-DE by first isoelectric focusing (IEF) based on its characteristic pI of 5.1, followed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) based on its molecular mass of 47.5 kDa. We quantified various percentages of soybean CP4 EPSPS. The quantitative analysis was performed using a 2D software program on artificial gels with spots varying in Gaussian volumes. These results suggested that 2-DE image analysis could be used for quantitative detection of GM soybean, unlike Western blotting.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.215-226
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• 2014

Recently, spectroscopy has emerged as a potential tool for quality evaluation of numerous food and agricultural products because it provides information regarding both spectral distribution and image features of the sample (i.e., hyperspectral imaging). Spectroscopic techniques reveal hidden information regarding the sample and do so in a non-destructive manner. This review describes the various approaches of spectroscopic modalities, especially hyperspectroscopy and vibrational spectroscopies (i.e., Raman spectroscopy and Fourier transform near infrared spectroscopy) combined with chemometrics for the non-destructive assessment of contaminations and defects in agro-food products.

• Journal of the Korean Society of Food Culture
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• v.19 no.2
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• pp.129-138
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• 2004

The changes in DNA damage were investigated during storage after irradiation. Beef, pork and chicken were irradiated at 1.0, 3.0 and 5.0 kGy and stored for 6 months at $-20^{\circ}C$. The comet assay was applied to the sample muscles at the beginning of irradiation and at the end of storage. Muscles were isolated, sliced, and the suspended cells were embedded in an agarose layer. After lysis of the cells, they were electrophoresed for 2 min. and then stained. DNA fragmentation in tissues caused by irradiation was quantified as tail length and tail moment (tail length ${\times}$ % DNA in tail) by comet image analyzing system. Right after irradiation, the differences in tail length between unirradiated and irradiated muscles were significant(p<0.05) in beef, pork and chicken. With increasing the increasing doses, statistically significant longer extension of the DNA from the nucleus toward anode was observed. Similarly even 6 months after irradiation, all the irradiated muscles significantly showed longer tail length than the unirradiated controls. The results represented as tail moment showed similar tendency to those of tail length, but the latter parameter was more sensitive than the former. These results indicate that the comet assay could be one of the simple methods of detecting irradiated muscles. Moreover, this method suggest that using comet assay, we were able to detect DNA damage differences even after 6 months after irradiation.

• Journal of the Korean Society of Food Culture
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• v.19 no.3
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• pp.251-258
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• 2004

The changes in DNA damage were investigated during storage after irradiation. Potato, garlic were irradiated at 0.05, 0.07, 0.1 and 0.15 kGy and stored for 3 months. Ginger was irradiated at 0.01, 0.02, 0.03, 0.04 and 0.05 kGy and stored for 1 month. The comet assay was applied to the sample immediately after irradiation and at the end of storage. Samples were isolated, grounded and the suspended cells were embedded in an agarose layer. After lysis of the cells, they were electrophoresed for 1 min. and then stained. DNA fragmentation in seeds caused by irradiation was quantified as tail length and tail moment (tail length ${\times}%$ DNA in tail) by comet image analyzing system. Right after irradiation, the differences in tail length between unirradiated and irradiated samples were significant(p<0.05) in potato, garlic and ginger. With increasing the irradiation doses, statistically significant longer extension of the DNA from the nucleus toward anode was observed. The results represented as tail moment showed similar tendency to those of tail length. Similarly in the stored samples, even 1 or 3 months after irradiation, all the irradiated samples significantly showed longer tail length than the unirradiated controls. These results indicate that the comet assay could be one of the simple methods of detecting irradiated samples. Moreover, the method could detect DNA damage even after 1 or 3 months after irradiation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.460-462
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• 2021

Food basically must have nutrition and safety. Recently, a number of symptoms of food poisoning occurred in a kindergarten in Ansan, where food safety was suspected. Therefore, the safety of food is more demanding. In this paper, we propose a method to inprove the detector to secure food safety. The proposed method is to learn through the network of convolution neural network (CNN) and Faster region-CNN (Faster R-CNN) and test the images of normal and foreign products. As a result of testing through a deep learning model, the method that used Faster R-CNN in parallel with the existing foreign body detector algorithm showed better detection rate than other methods.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Journal of Platform Technology
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• v.10 no.1
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• pp.20-28
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• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.

• 한국환경농학회:학술대회논문집
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• 2009.07a
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• pp.119-126
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• 2009

In recent years, research at the Environmental Microbial and Food Safety Laboratory (EMFSL), Agricultural Research Service (ARS) has focused on the development of novel image-based sensing technologies to address agro-food safety concerns, and transformation of these novel technologies into practical instrumentation for industrial implementations. The line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. We developed a newer line-scan hyperspectral imaging platform for high-speed inspection on high-throughput processing lines, capable of simultaneous multiple inspection algorithms for different agro-food safety problems such as poultry carcass inspection for wholesomeness and apple inspection for fecal contamination and defect detection. In addition, portable imaging devices were developed for in situ identification of contamination sites and for use by agrofood producer and processor operations for cleaning and sanitation inspection of food processing surfaces. The aim of this presentation is to illustrate recent advances in the above agro.food safety sensing technologies.