• Journal of Food Hygiene and Safety
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• v.29 no.4
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• pp.312-315
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• 2014

The present study demonstrated the development and validation of the method for the quantification of phenol in food using gas chromatography coupled with mass spectrometry (GC-MS). After spiking of internal standard (Phenol-$d_5$) to food, those samples were extracted with organic solvent mixture (acetone : dichloromethane = 1 : 1, v/v) using ultra sonic extractor and cleaned by gel permeation chromatography (GPC) technique. The amount of phenol was determined by GC/MS. To validate the developed method, we evaluated parameters were the selectivity, linearity, accuracy, precision, and recovery. To demonstrate the selectivity of the method, blank samples of rice, corn, and fish(mackerel) were prepared and subjected to GC-MS analysis. To verify the linearity of the method, six different standard concentrations of phenol at 0.01, 0.05, 0.1, 0.5, 1 and 2.5 mg/kg were evaluated. The correlation coefficient ($r^2$) of calibration curve was 0.9999. The recovery rate for phenol standard calculated by internal standard method were 82.2~101.5% for samples fortified with 0.25, 0.50, and 1.0 mg/kg, respectively. Also the repeatability and reproducibility for validation of precision were 0.2~5.5%. According to the result of the validation, this established method was suitable for AOAC guideline. The limit of detection (LOD) for phenol analysis were 0.03~0.1 mg/kg, and the limit of quantification (LOQ) were 0.1~0.3 mg/kg. Therefore, we established the optimal analysis method for determination of phenol in food using GPC and GC/MS.

• Korean Journal of Food Science and Technology
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• v.32 no.5
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• pp.991-996
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• 2000

Enzyme-linked immunosorbent assay was developed for the analysis of soy protein in foods. Competitive indirect ELISA (ciELISA) was established by using specific antibodies against the heat-stable acidic subunits (AS) of glycinin. Soy proteins in each sample used in this study were solublized in the presence of urea and DTT and boiled at $100^{\circ}C$ for 1hr and then were renatured with a cystine-containing solution. After these treatments, each isolated soy protein (ISP) heated at 60, 70, 80, $90^{\circ}C$ for 10 minutes showed almost the same curve as unheated one in the ciELISA. The detection limit of ISP was 0.3 ${\mu}g/mL$. Anti-AS antibodies have very low reactivities less than 0.1% toward non-meat proteins such as skim milk and casein and did not show any reactivities toward egg white powder and ovalbumin. When laboratory-made sausages containing ISP of $0.5{\sim}3%$ were assayed by ciELISA, the mean recovery was about 83% (C.V., 19%). In addition, the average content of soy protein in commercial sausages was 1.27%.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.1
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• pp.49-59
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• 2021

This study investigated the use of real-time PCR melting curves for the diagnosis of blaKPC and blaNDM genes among the most frequently detected carbapenemase-producing Enterobacteriaceae in Korea. As a means of addressing the shortcomings of phenotype tests and conventional PCR. The modified Hodge test confirmed positivity in 25 of 35 strains, and carbapenemase inhibition testing confirmed positivity in 14 strains by meropenem+PBA or meropenem+EDTA. PCR analysis showed amplification products in 25 strains of Klebsiella pneumoniae carbapenemases (KPC), 10 of K. pneumoniae, 5 of E. coli, 5 of A. baumannii, 4 of P. aeruginosa, and 1 of P. putida. New Delhi metallo β-lactamase (NDM) identified amplification products in 8 strains, that is, 2 K. pneumoniae, 3 E. coli, 1 P. aeruginosa, 1 E. cloacae, and 1 P. retgeri strains. Real-time PCR melting curve analysis confirmed amplification in 25 strains of KPC and 8 strains of NDM, and these results were 100% consistent with PCR results. In conclusion, our findings suggest early diagnosis of carbapenem resistant Enterobacteriaceae by real-time PCR offers a potential means of antibacterial management that can prevent and control nosocomial infection spread.

• Journal of Food Hygiene and Safety
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• v.36 no.6
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• pp.474-480
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• 2021

Sodium alginate is the sodium salt of alginic acid, commonly used as a food additive for stabilizing, thickening, and emulsifying properties. A relatively simple and universal analysis method is used to study sodium alginate due to the complex pretreatment process and extended analysis time required during the quantitative method. As for the equipment, HPLC-UVD and Unison US-Phenyl column were used for analysis. For the pretreatment condition, a shaking apparatus was used for extraction at 150 rpm for 180 minutes at room temperature. The calibration curve made from the standard sodium alginate solution in 5 concentration ranges showed that the linearity (R²) is 0.9999 on average. LOD and LOQ showed 3.96 mg/kg and 12.0 mg/kg, respectively. Furthermore, the average intraday and inter-day accuracy (%) and precision (RSD%) were 98.47-103.74% and 1.69-3.08% for seaweed jelly noodle samples and 99.95-105.76% and 0.59-3.63% for sherbet samples, respectively. The relative uncertainty value was appropriate for the CODEX standard with 1.5-7.9%. To evaluate the applicability of the method developed in this study, the sodium alginate concentrations of 103 products were quantified. The result showed that the detection rate is highest from starch vermicelli and instant fried noodles to sugar processed products.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.3
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• pp.179-191
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• 2022

Carbapenem-resistant Enterobacteriaceae (CRE) poses an increasing public health threat and has limited treatment options with high associated mortality. Genotypes of carbapenemase that threaten public health (bla_KPC, bla_NDM, bla_IMP, and bla_VIM) and bla_OXA-48-like genes were detected by phenotypic and molecular diagnosis, and related gene distribution patterns were investigated. Phenotypic testing using the modified Hodge test confirmed positivity in all 41 strains examined, and carbapenemase inhibitory testing using meropenem+phenyl boronic acid or meropenem+EDTA confirmed positivity in 18 and 8 strains, respectively. Polymerase chain reaction revealed the presence of amplification products in 28 strains of bla_KPC, 25 strains of bla_NDM, 5 strains of bla_IMP, 1 strain of bla_VIM, and 13 bla_OXA-48-like strains. In addition, 7 strains of bla_KPC+bla_NDM, 1 strain of bla_KPC+bla_IMP, 1 strain of bla_NDM+bla_OXA-48-like, 1 strain of bla_NDM+bla_VIM, 4 strains of bla_KPC+bla_NDM+bla_IMP, and 4 strains of bla_KPC+bla_NDM+bla_OXA-48-like were identified. Melting curve analysis using real-time PCR was wholly consistent with PCR results. The study shows genetic identification of highly specific CRE by real-time PCR could be used to provide early diagnoses and infection control, improve surveillance, and prevent the transmission of CRE.

• Analytical Science and Technology
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• v.22 no.3
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• pp.235-246
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• 2009

The analyses of minor and trace elements in glass debris were performed using LA-ICP-MS in order to identify manufacturers using real commercial samples. At first, a calibration curve was made using standard glass samples of NIST 610, 612, 614 and 616. $^{29}Si$ was used as an internal standard, and the ratios of metal/Si for each metal were compared with their concentrations. Based on elements in each sample and standard materials, 24 metals were quantified and the LOD in analysis, according to the blank sample, was in the range of 0.11 mg/kg (Ti)-4.91 mg/kg (Ca). Eleven samples from two manufacturers were collected and five sub-samples were taken from each sample for analysis. 15 elements (Co, Ce, Ca, Mn, Sr, Ba, Li, Rb, U, La, Th, Na, Al, Zr and Hf) were selected to identify manufacturers because some elements (Cu, Cr, Cd and Ni) were below the detection limit and some elements (Ti, Pr, Mg, Nb, Nd) were absent in the analysis of standards and others (Pb and Sn) had a problem of homogeneity. The attempts to identify manufacturers and the manufacturing period were performed through a triangular diagram. In the manufacturer discrimination by discriminant analysis, a canonical discriminant function was made based on Mn, Ce and Rb, and each sample could be identified.

• Analytical Science and Technology
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• v.21 no.4
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• pp.284-295
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• 2008

For plastics samples, a method using combustion ion chromatography was selected as a method for rapid low-cost analysis to test whether hazardous substances are contained or not. Using combustion ion chromatography, a verification test for F, Cl and Br compounds generated a linear calibration curve with a correlation coefficient of $r^2$ = 0.999~1.000 in the calibration range from 0.5 to 4.0 mg/kg. The detection limits were found to be 0.005~0.024 mg/kg and quantitative limits were found to be 0.014~0.073 mg/kg. The recoveries of combustion ion chromatography using certified reference material (CRM) were found to be 95.5~104.9%. Based on these results, a proficiency test was conducted together with several laboratories in and out of the country, to make comparative analysis of the results from each laboratory. As a result, the data supported the use of combustion ion chromatography as an effective analysis method to deal with regulations for halogen-free electronic products and for other hazardous substances in the electronic products.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.420-429
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• 2023

GC-MS/MS using liquid-liquid extraction (LLE) and C18 cartridges was used to identify and quantify levels of chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin and permethrin in bulk raw milk. A calibration curve spanning 10 ng/mL to 200 ng/mL was obtained with a satisfactory correlation coefficient of 0.99. The limits of detection (LOD) and limits of quantitation (LOQ) for chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin, and permethrin in the matrix ranged from 0.06 to 1.81 ng/mL and 0.19 to 6.04 ng/mL, respectively. The recoveries of 5 pesticides from spiked samples at 37.5-125 ng/mL ranged from 86.1 to 102.1%. The measurement of uncertainty of the GC-MS/MS method for these five pesticides was developed based on the analytical process and quantification. An analysis method that is easier and faster than the method specified in the Korean food standards codes for analyzing these five pesticides in raw material milk was developed. Moreover, the analytical method for chlorpyrifos, chlorpyrifos-methyl, cypermethrin, deltamethrin, and permethrin in bulk raw milk by GC-MS/MS was established.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1100-1109
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• 2021

Objective: To compare the diagnostic performance between Prostate Imaging-Reporting and Data System version 2.0 (PI-RADSv2.0) and version 2.1 (PI-RADSv2.1) for clinically significant prostate cancer (csPCa) in the peripheral zone (PZ). Materials and Methods: This retrospective study included 317 patients who underwent multiparametric magnetic resonance imaging and targeted biopsy for PZ lesions. Definition of csPCa was International Society of Urologic Pathology grade ≥ 2 cancer. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for csPCa were analyzed by two readers. The cancer detection rate (CDR) for csPCa was investigated according to the PI-RADS categories. Results: AUC of PI-RADSv2.1 (0.856 and 0.858 for reader 1 and 2 respectively) was higher than that of PI-RADSv2.0 (0.795 and 0.747 for reader 1 and 2 respectively) (both p < 0.001). Sensitivity, specificity, PPV, NPV, and accuracy for PI-RADSv2.0 vs. PI-RADSv2.1 were 93.2% vs. 88.3% (p = 0.023), 52.8% vs. 76.6% (p < 0.001), 48.7% vs. 64.5% (p < 0.001), 94.2% vs. 93.2% (p = 0.504), and 65.9% vs. 80.4% (p < 0.001) for reader 1, and 96.1% vs. 92.2% (p = 0.046), 34.1% vs. 72.4% (p < 0.001), 41.3% vs. 61.7% (p < 0.001), 94.8% vs. 95.1% (p = 0.869), and 54.3% vs. 78.9% (p < 0.001) for reader 2, respectively. CDRs of PI-RADS categories 1-2, 3, 4, and 5 for PI-RADSv2.0 vs. PI-RADSv2.1 were 5.9% vs. 5.9%, 5.8% vs. 12.5%, 39.8% vs. 56.2%, and 88.9% vs. 88.9% for reader 1; and 4.5% vs. 4.1%, 6.1% vs. 11.1%, 32.5% vs. 53.4%, and 85.0% vs. 86.8% for reader 2, respectively. Conclusion: Our data demonstrated improved AUC, specificity, PPV, accuracy, and CDRs of category 3 or 4 of PI-RADSv2.1, but decreased sensitivity, compared with PI-RADSv2.0, for csPCa in PZ.

• Korean Journal of Radiology
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• v.23 no.5
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• pp.505-516
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• 2022

Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. Results: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). Conclusion: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.