Giyoung Kim

A microfluidic nano-biosensor for the detection of pathogenic Salmonella.

Rapid detection of pathogenic Salmonella in food products is extremely important for protecting the public from salmonellosis. The objective of the present study was to explore the feasibility of using a microfluidic nano-biosensor to rapidly detect pathogenic Salmonella. Quantum dot nanoparticles were used to detect Salmonella cells. For selective detection of Salmonella, anti-Salmonella polyclonal antibodies were covalently immobilized onto the quantum dot surface. To separate and concentrate the cells from the sample, superparamagnetic particles and a microfluidic chip were used. A portable fluorometer was developed to measure the fluorescence signal from the quantum dot nanoparticles attached to Salmonella in the samples. The sensitivity for detection of pathogenic Salmonella was evaluated using serially diluted Salmonella Typhimurium in borate buffer and chicken extract. The fluorescence response of the nano-biosensor increased with increasing cell concentration. The detection limit of the sensor was 10(3) CFU/mL Salmonella in both borate buffer and food extract.

Performance Improvement of the One-Dot Lateral Flow Immunoassay for Aflatoxin B1 by Using a Smartphone-Based Reading System

This study was conducted to develop a simple, rapid, and accurate lateral flow immunoassay (LFIA) detection method for point-of-care diagnosis. The one-dot LFIA for aflatoxin B1 (AFB1) was based on the modified competitive binding format using competition between AFB1 and colloidal gold-AFB1-BSA conjugate for antibody binding sites in the test zone. A Smartphone-based reading system consisting of a Samsung Galaxy S2 Smartphone, a LFIA reader, and a Smartphone application for the image acquisition and data analysis. The detection limit of one-dot LFIA for AFB1 is 5 μg/kg. This method provided semi-quantitative analysis of AFB1 samples in the range of 5 to 1,000 μg/kg. Using combination of the one-dot LFIA and the Smartphone-based reading system, it is possible to conduct a more fast and accurate point-of-care diagnosis.

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.

Comparison of Whole-Cell SELEX Methods for the Identification of Staphylococcus Aureus-Specific DNA Aptamers

Whole-cell Systemic Evolution of Ligands by Exponential enrichment (SELEX) is the process by which aptamers specific to target cells are developed. Aptamers selected by whole-cell SELEX have high affinity and specificity for bacterial surface molecules and live bacterial targets. To identify DNA aptamers specific to Staphylococcus aureus, we applied our rapid whole-cell SELEX method to a single-stranded ssDNA library. To improve the specificity and selectivity of the aptamers, we designed, selected, and developed two categories of aptamers that were selected by two kinds of whole-cell SELEX, by mixing and combining FACS analysis and a counter-SELEX process. Using this approach, we have developed a biosensor system that employs a high affinity aptamer for detection of target bacteria. FAM-labeled aptamer sequences with high binding to S. aureus, as determined by fluorescence spectroscopic analysis, were identified, and aptamer A14, selected by the basic whole-cell SELEX using a once-off FACS analysis, and which had a high binding affinity and specificity, was chosen. The binding assay was evaluated using FACS analysis. Our study demonstrated the development of a set of whole-cell SELEX derived aptamers specific to S. aureus; this approach can be used in the identification of other bacteria.

Rapid Detection Kit for Salmonella typhimurium

This study was performed to develop a rapid test kit for pathogenic Salmonella in various samples. The rapid detection kit has been fabricated based on nitrocellulose lateral-flow strip. Colloidal gold and biotin conjugated Salmonella antibodies were used as a tag and a receptor, respectively. Manually spotted Salmonella antibody and Neutravidin on nitrocellulose membrane were used as test and control lines, respectively. Feasibility of the rapid kit to detect Salmonella typhimurium in samples were evaluated. The intensity of the color of the test line started to increase with the samples in which higher concentration of the cells were contained. The sensitivity of the sensor was cfu/mL Salmonella spiked in PBS. Also, the rapid test kit could detect cfu/mL of Salmonella in chicken meat extract.

Interdigitated microelectrode based impedance biosensor for detection of salmonella enteritidis in food samples

Salmonella enteritidis outbreaks continue to occur, and S. enteritidis-related outbreaks from various food sources have increased public awareness of this pathogen. Conventional methods for pathogens detection and identification are labor-intensive and take days to complete. Some immunological rapid assays are developed, but these assays still require prolonged enrichment steps. Recently developed biosensors have shown great potential for the rapid detection of foodborne pathogens. To develop the biosensor, an interdigitated microelectrode (IME) was fabricated by using semiconductor fabrication process. Anti-Salmonella antibodies were immobilized based on avidin-biotin binding on the surface of the IME to form an active sensing layer. To increase the sensitivity of the sensor, three types of sensors that have different electrode gap sizes (2 μm, 5 μm, 10 μm) were fabricated and tested. The impedimetric biosensor could detect 103 CFU/mL of Salmonella in pork meat extract with an incubation time of 5 minutes. This method may provide a simple, rapid and sensitive method to detect foodborne pathogens.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Detection of pathogenic Salmonella with nanobiosensors

Rapid detection methods are needed to identify pathogenic Salmonella spp. in food products to protect against outbreaks of salmonellosis. The objective of this study was to explore the feasibility of using quantum dot nanoparticles to rapidly detect pathogenic Salmonella. Selective detection was achieved with anti-Salmonella polyclonal antibodies immobilized by streptavidin–biotin binding or covalent binding to the quantum dot surface. Superparamagnetic particles were used to separate and concentrate cells from each sample. A portable fluorometer was developed to measure fluorescence signals from quantum dot nanoparticles adhered to Salmonella in the samples. The fluorometer was composed of a 415 nm UV LED, a bifurcated fiber, optical filters, and a silicon photomultiplier. Detection sensitivity was evaluated with serially diluted Salmonella typhimurium in phosphate-buffered saline containing 1% bovine serum albumin (PBS–BSA) or food extracts. Fluorescence of the nanobiosensor increased with increasing concentrations of bacteria. Salmonella detection limits were 1.4 × 103 CFU mL−1 in PBS–BSA and 4 × 103 CFU mL−1 in food extracts.

Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging

Purpose: Soybean growth is adversely affected by environmental stresses such as drought, extreme temperatures, and nutrient deficiency. The objective of this study was to develop a method for rapid measurement of drought stress in soybean plants using a hyperspectral fluorescence imaging technique. Methods: Hyperspectral fluorescence images were obtained using UV-A light with 365 nm excitation. Two soybean cultivars under drought stress were analyzed. A partial least square regression (PLSR) model was used to predict drought stress in soybeans. Results: Partial least square (PLS) images were obtained for the two soybean cultivars using the results of the developed model during the period of drought stress treatment. Analysis of the PLS images showed that the accuracy of drought stress discrimination in the two cultivars was 0.973 for an 8-day treatment group and 0.969 for a 6-day treatment group. Conclusions: These results validate the use of hyperspectral fluorescence images for assessing drought stress in soybeans.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples.