Seong In Cho

Highly sensitive trace analysis of paraquat using a surface-enhanced Raman scattering microdroplet sensor

We report a rapid and highly sensitive trace analysis of paraquat (PQ) in water using a surface-enhanced Raman scattering (SERS)-based microdroplet sensor. Aqueous samples of PQ, silver nanoparticles, and NaCl as the aggregation agent were introduced into a microfluidic channel and were encapsulated by a continuous oil phase to form a microdroplet. PQ molecules were adsorbed onto particle surfaces in isolated droplets by passing through the winding part of the channel. Memory effects, caused by the precipitation of nanoparticle aggregates on channel walls, were removed because the aqueous droplets were completely isolated by a continuous oil phase. The limit of detection (LOD) of PQ in water, determined by the SERS-based microdroplet sensor, was estimated to be below 2×10(-9) M, and this low detection limit was enhanced by one to two orders of magnitude compared to conventional analytical methods.

Paper microfluidics for red wine tasting

A paper microfluidic chip was designed and fabricated to evaluate the taste of 10 different red wines using a set of chemical dyes. The digital camera of a smartphone captured the images, and its red-green-blue (RGB) pixel intensities were analyzed by principal component analysis (PCA). Using 8 dyes and 2 principal components (PCs), we were able to distinguish each wine by the grape variety and the oxidation status. Through comparing with the flavor map by human evaluation, PC1 seemed to represent the sweetness and PC2 the bodyness of red wine. This superior performance is attributed to: (1) careful selection of commercially available dyes through a series of linear correlation studies with the taste chemicals in red wines, (2) minimization of sample-to-sample variation by splitting a single sample into multiple wells on the paper microfluidics, and (3) filtration of particulate matter through paper fibers. The image processing and PCA procedure can eventually be implemented as a stand-alone smartphone application and can be adopted as an extremely low-cost, disposable, fully handheld, easy-to-use, yet sensitive and specific quality control method for appraising red wine or similar beverage products in resource-limited environments.

Colorimetric Sensor Array for White Wine Tasting

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

A Hybrid Robotic System for Harvesting Heavy Produce

Abstract Certain agricultural work requires a very stiff robot arm to reduce vibration and damage to products. However, the structure of conventional articulated robots tends to be too weak to manipulate heavy objects, such as melons and watermelons. The Stewart platform is a typical parallel robotic mechanism with very high stiffness, but this advantage is balanced by a small workspace and large installation space. This research proposes a new type of agricultural robot arm constructed by combining a parallel and a serial mechanism. This robot consists of a hybrid manipulator, a robot controller and an end-effector. The hybrid manipulator has serial joints with two extra degrees of freedom, making the workspace wider. It was also designed to harvest heavy fruits such as melons, even from behind obstacles such as leaves or tree branches. Position control results of the developed hybrid robot showed that it could be controlled within a ±1 mm and ±0.2° range of error.

Correlations between the Growth Period and Fresh Weight of Seed Sprouts and Pixel Counts of Leaf Area

【Purpose: This study was carried out to predict the growth period and fresh weight of sprouts grown in a cultivator designed to grow sprouts under optimal conditions. Methods: The temperature, light intensity, and amount of irrigation were controlled, and images of seed sprouts were acquired to predict the days of growth and weight from pixel counts of leaf area. Broccoli, clover, and radish sprouts were selected, and each sprout was cultivated in a 90-mm-diameter Petri dish under the same cultivating conditions. An image of each sprout was taken every 24 hours from the 4th day, and the whole cultivating period was 6 days, including 3 days in the dark. Images were processed by histogram inspection, binary images, image erosion, image dilation, and the overlay image process. The RGB range and ratio of leaves were adjusted to calculate the pixel counts for leaf area. Results: The correlation coefficients between the pixel count of leaf area and the growth period of sprouts were 0.91, 0.98, and 0.97 for broccoli, clover, and radish, respectively. Further, the correlation coefficients between the pixel count of leaf area and fresh weight were 0.90 for broccoli, 0.87 for clover, and 0.95 for radish. Conclusions: On the basis of these results, we suggest that the simple image acquisition system and processing algorithm can feasibly estimate the growth period and fresh weight of seed sprouts.】

Modeling of Solar Radiation Using Silicon Solar Module

Purpose: Short-circuit current of a solar module that is widely used as a power source for wireless environmental sensors is proportional to solar radiation although there are a lot of fac tors affecting the short-circuit current. The objective of this study is to develop a model for estimating solar radiation for using the solar module as a power source and an irradiance sensor. Methods: An experiment system collected data on the short-circuit curre nt and environmental factors (ambient temperature, cloud cover and solar radiation) during 65 days. Based on these data, two linear regression models and a non-linear regression model were developed and evaluated. Results: The best model was a linear regression model with short-circuit current, angle of incidence and cloud cover and its overall RMSE(Root Means Square Error) was 66.671   . The other linear model (RMSE 69.038   ) was also acceptable when the cloud cover data is not available.

Prediction of weight loss of low temperature storage tomato (Tiwai 250) by non-destructive firmness measurement

This study was conducted to investigate the weight loss, firmness, external color and vitamin C (VC) content of tomatoes (Lycopersicon esculentum) using non-destructive method to measure identical tomato samples during 15 days storage at low temperature and high humidity. Tomatoes were harvested at the light red stage, sorted, box packed and then stored in thermo-hygrostat (10±1°C, 90±10%RH). The quality changes in weight loss, firmness and external color were measured every 3 day interval. Weight loss was increased by 1.13±0.15%, but it may not be considered to affect quality. Surface color of fruit was changed, especially in lightness and hue angle value. The color values were analyzed by analysis of variance (ANOVA), and the results were significant (p<0.001). Firmness of fruit declined during storage, but it did not decrease in direct proportion. On the storage of day 15, firmness was decreased to 40% of initial state. At last, all the experiment data are summarized and the relationship between firmness and weight loss is analyzed to construct a linear regression mathematical model that can predict the weight loss with the firmness value measured by non-destructive method. This research result could be useful in helping tomato exporters and suppliers to get real-time quality factor by using proposed method and regression model.

Sensing Estrogen with Electrochemical Impedance Spectroscopy

This study demonstrates the application feasibility of electrochemical impedance spectroscopy (EIS) in measuring estrogen (17β-estradiol) in gas phase. The present biosensor gives a linear response (R 2 = 0.999) for 17β-estradiol vapor concentration from 3.7 ng/L to 3.7 × 10−4 ng/L with a limit of detection (3.7 × 10−4 ng/L). The results show that the fabricated biosensor demonstrates better detection limit of 17β-estradiol in gas phase than the previous report with GC-MS method. This estrogen biosensor has many potential applications for on-site detection of a variety of endocrine disrupting compounds (EDCs) in the gas phase.

Enzyme Immobilized Biosensor for L-Lactate

L-lactate has an important role on biochemical process such as producing ATP, causing cramps in muscle. The lactate consumption in various proposes is increasing in human life. It is produced by fermentation process with microorganism. Measuring L-lactate concentration by a rapid and simple method is a great need in clinical and food industries. Analysis of enzyme reaction on Llactate was performed with L-LDH (L-lactate dehydrogenase), and NAD+ (nicotinamide adenine dinucleotide). To avoid enzyme reverse reaction of L-LDH, GPT (glutamic-pyruvic transaminase) and L-glutamate were supplied. Substances were immobilized with polyacrylamide gel on tip of glass electrode. Calibration with L-lactate and output signal was performed. The relation between the Llactate (%) and the signal from the electrode (mV) was y=0.0719x-16.576. The calibration had a correlation coefficient of R2=0.9959 and R2=0.9919 when it was validated.

Color Evaluation of Lean Tissue and Fat of the Beef

Abstract This study was conducted to develop color evaluation of beef using machine vision system. Machine vision has an advantage to measure larger area than colorimeter and also can be measured another quality factors like distribution of fats. But machine vision measurement is affected by its system component. To measure the beef color with machine vision system, the effect of color balancing control was tested and calibration model of vision system was developed. Neural network for color calibration which learned reference color patches shows the high correlation with colorimeter in L*a*b* coordinates and has an adaptability at various measurement environment. But trained network shows very low correlation with colorimeter when measuring beef color.