• Analytical Science and Technology
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• v.36 no.6
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• pp.291-299
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• 2023

The benzo[a]pyrene in edible oils is extracted using methods such as Liquid-liquid, soxhlet and ultrasound-assisted extraction. However these extraction methods have significant drawbacks, such as long extraction time and large amount of solvent usage. To overcome these drawbacks, this study attempted to improve the current complex benzo[a]pyrene analysis method by applying the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method that can be analyzed in a simple and short time. The QuEChERS method applied in this study includes extraction of benzo[a]pyrene into n-hexane saturated acetonitrile and n-hexane. After extraction and distribution using magnesium sulfate and sodium chloride, benzo[a]pyrene is analyzed by liquid chromatography with fluorescence detector (LC/FLR). As a result of method validation of the new method, the limit of detection (LOD) and quantification (LOQ) were 0.02 ㎍/kg and 0.05 ㎍/kg, respectively. The calibration curves were constructed using five levels (0.1~10 ㎍/kg) and coefficient (R²) was above 0.99. Mean recovery ratio was ranged from 74.5 to 79.3 % with a relative standard deviation (RSD) between 0.52 to 1.58 %. The accuracy and precision were 72.6~79.4 % and 0.14~7.20 %, respectively. All results satisfied the criteria ranges requested in the Food Safety Evaluation Department guidelines (2016) and AOAC official method of analysis (2023). Therefore, the analysis method presented in this study was a relatively simple pretreatment method compared to the existing analysis method, which reduced the analysis time and solvent use to 92 % and 96 %, respectively.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.361-372
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• 2023

Organotin pesticide is used as an acaricide in agriculture and may contaminate livestock products. This study aims to develop a rapid and straightforward analytical method for detecting organotin pesticides, specifically azocyclotin, cyhexatin, and fenbutatin oxide, in various livestock products, including beef, pork, chicken, egg, and milk, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The extraction process involved the use of 1% acetic acid in a mixture of acetonitrile and ethyl acetate (1:1). This was followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium chloride. The extracts were subsequently purified using octadecyl (C₁₈) and primary secondary amine (PSA), after which the supernatant was evaporated. Organotin pesticide recovery ranged from 75.7 to 115.3%, with a coefficient of variation (CV) below 25.3%. The results meet the criteria range of the Codex guidelines (CODEX CAC/GL 40). The analytical method in this study will be invaluable for the analysis of organotin pesticides in livestock products.

• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.390-401
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• 2023

Major components of lac coloring include laccaic acids A, B, C, and E. The Korean Food Additive Code regulates the use of lac coloring and prohibits its use in ten types of food products including natural food products. Since no commercial standards are available for laccaic acids A, B, C, and E, a standard for lac pigment itself was used to separate laccaic acids from the lac pigment molecule. A standard for each laccaic acid was then obtained by fractionation. To obtain pure lac pigment for use in food by High performance Liquid Chromatography Photo Diode Array (PDA), a C8 column yielded the best resolution among various tested columns and mobile phases. A qualitative analytical method using High Performance Liquid Chromatography (HPLC) Tandem Mass(LC-MS/MS) was developed. The conditions for fast and precise sample preparation begin with extraction using methanol and 0.3% ammonium phosphate, followed by concentration. The degree of precision observed for the analyses of ham, tomato juice and Red pepper paste was 0.3-13.1% (Relative Standard Deviation (RSD%)), degree of accuracy was 90.3-122.2% with r²=0.999 or above, and recovery rate was 91.6-114.9%. The limit of detection was 0.01-0.15 ㎍/mL, and the limits of quantitation ranged from 0.02 to 0.47 ㎍/mL. Lac pigment was not detected in 117 food products in the 10 food categories for which the use of lac pigment is banned. Multiple laccaic acids were detected in 105 food products in 6 food categories that are allowed to use lac color. Lac pigment concentrations range from 0.08 to 16.67 ㎍/mL.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.149-160
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• 2023

This study develops a time-varying system-based noncontact fabric sensor that can measure cerebral blood-flow signals to explore the possibility of brain blood-signal detection and emotional evaluation. The textile sensor was implemented as a coil-type sensor by combining 30 silver threads of 40 deniers and then embroidering it with the computer machine. For the cerebral blood-flow measurement experiment, subjects were asked to attach a coil-type sensor to the carotid artery area, wear an electrocardiogram (ECG) electrode and a respiration (RSP) measurement belt. In addition, Doppler ultrasonography was performed using an ultrasonic diagnostic device to measure the speed of blood flow. The subject was asked to wear Meta Quest 2, measure the blood-flow change signal when viewing the manipulated image visual stimulus, and fill out an emotional-evaluation questionnaire. The measurement results show that the textile-sensor-measured signal also changes with a change in the blood-flow rate signal measured using the Doppler ultrasonography. These findings verify that the cerebral blood-flow signal can be measured using a coil-type textile sensor. In addition, the HRV extracted from ECG and PLL signals (textile sensor signals) are calculated and compared for emotional evaluation. The comparison results show that for the change in the ratio because of the activation of the sympathetic and parasympathetic nervous systems due to visual stimulation, the values calculated using the textile sensor and ECG signals tend to be similar. In conclusion, a the proposed time-varying system-based coil-type textile sensor can be used to study changes in the cerebral blood flow and monitor emotions.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.61-71
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• 2024

In this study, we analyzed chlorogenic acid indicator components in preparation for the additional listing of green coffee bean extract in the Health Functional Food Code and optimized caffeine for simultaneous analysis. We extracted chlorogenic acid and caffeine using 30% methanol, phosphoric acid solution, and acetonitrile-containing phosphoric acid and analyzed them at 330 and 280 nm, respectively, using liquid chromatography. Our analysis validation results yielded a correlation coefficient (R²) revealing a significance level of at least 0.999 within the linear quantitative range. The chlorogenic acid and caffeine detection and quantification limits were 0.5 and 0.2 ㎍/mL and 1.4, and 0.4 ㎍/mL, respectively. We confirmed that the precision and accuracy results were suitable using the AOAC validation guidelines. Finally, we developed a simultaneous chlorogenic acid and caffeine analysis approach. In addition, we confirmed that our analysis approach could simultaneously quantify chlorogenic acid and caffeine by examining the applicability of each formulation through prototypes and distribution products. In conclusion, the results of this study demonstrated that the standardized analysis would expectably increase chlorogenic acidcontaining health functional food quality control reliability.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.118-127
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• 2024

This study investigated the levels of 345 pesticide residues in 50 herbal medicines sold in Incheon metropolitan city to determine their safety. Pesticide residues are harmful substances that can cause serious health problems owing to their toxicity and carcinogenicity. The analysis of pesticide residues in the samples was conducted using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method, known for its high analysis efficiency, to analyze a wide range of pesticides for which no standards have been set. The analysis was cross-validated with the pretreatment method outlined in the Korea Pharmacopoeia. Among the 50 samples encompassing 24 different herbs, 22 pesticide residues were detected in 24 samples, covering 7 distinct herbs, resulting in a detection rate of 48%. It is noteworthy that, except for two cases, all detected pesticides were those for which no standards were set. However, after conducting a risk evaluation considering the daily dosage of herb, it was determined that the levels of pesticide residues were within safe limits. Pesticides with high frequency within the same category of herbs were detected, indicating the necessity for continuous monitoring and regulation. In addition, comparative analysis using the pretreatment method outlined in the Korean Pharmacopoeia, yielded similar results, suggesting the possibility of analyzing pesticide residues in herbs using the QuEChERS method. The study emphasizes the importance of continuous monitoring of pesticide residues in herbs and the development of high-efficiency reliability analysis methods should continue to ensure consumer safety.

• Maritime Security
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• v.7 no.1
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• pp.31-60
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• 2023

This study proposes a new convergence weapon system that combines the covert placement and detection abilities of a self-propelled mobile mine with the rapid tracking and attack abilities of supercavitating rocket torpedoes. This innovative system has been designed to counter North Korea's new underwater weapon, 'Haeil'. The concept behind this convergence weapon system is to maximize the strengths and minimize the weaknesses of each weapon type. Self-propelled mobile mines, typically placed discreetly on the seabed or in the water, are designed to explode when a vessel or submarine passes near them. They are generally used to defend or control specific areas, like traditional sea mines, and can effectively limit enemy movement and guide them in a desired direction. The advantage that self-propelled mines have over traditional sea mines is their ability to move independently, ensuring the survivability of the platform responsible for placing the sea mines. This allows the mines to be discreetly placed even deeper into enemy lines, significantly reducing the time and cost of mine placement while ensuring the safety of the deployed platforms. However, to cause substantial damage to a target, the mine needs to detonate when the target is very close - typically within a few yards. This makes the timing of the explosion crucial. On the other hand, supercavitating rocket torpedoes are capable of traveling at groundbreaking speeds, many times faster than conventional torpedoes. This rapid movement leaves little room for the target to evade, a significant advantage. However, this comes with notable drawbacks - short range, high noise levels, and guidance issues. The high noise levels and short range is a serious disadvantage that can expose the platform that launched the torpedo. This research proposes the use of a convergence weapon system that leverages the strengths of both weapons while compensating for their weaknesses. This strategy can overcome the limitations of traditional underwater kill-chains, offering swift and precise responses. By adapting the weapon acquisition criteria from the Defense force development Service Order, the effectiveness of the proposed system was independently analyzed and proven in terms of underwater defense sustainability, survivability, and cost-efficiency. Furthermore, the utility of this system was demonstrated through simulated scenarios, revealing its potential to play a critical role in future underwater kill-chain scenarios. However, realizing this system presents significant technical challenges and requires further research.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.