• Journal of Food Hygiene and Safety
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• v.25 no.1
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• pp.36-42
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• 2010

The method for the determination of ethylenediamine (EDA) and hexamethylenediamine (HMDA) in food simulants was developed, and migration amounts of these compounds was monitored for 124 polyamide (PA) utensils. The diurethane derivatives of EDA and HMDA, which produced by reaction with ethyl chloroformate, were analyzed by using gas chromatograph (GC)/flame ionization detector (FID) and GC/mass spectrometer (MS). The developed method was validated with $0.3\;{\mu}g/mL$ of limit of detection (LOD) for EDA and $0.1\;{\mu}g/mL$ of LOD for HMDA, > 0.999 of linearity($r^2$) and > 88% of recovery. The EDA was detected 1.31 and $02.06\;{\mu}g/mL$ for 2 samples in water. The HMDA was detected $0.29\;-\;0.93\;{\mu}g/mL$ for 3 samples in 20% ethanol and $0.26\;-\;0.44\;{\mu}g/mL$ for 10 samples in n-heptane. These migration levels were below the specific migration limits (SML) of $12\;{\mu}g/mL$ and $2.4\;{\mu}g/mL$ for EDA and HMDA established in EU.

• Journal of Food Hygiene and Safety
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• v.32 no.5
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• pp.381-388
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• 2017

In this study, we investigated the levels of natural preservatives of benzoic acid, sorbic acid, and propionic acid in spices. The quantitative analysis was performed using high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) for benzoic acid and sorbic acid and gas chromatography-mass spectrometry (GC-MS) for propionic acid. The sample was extracted with ethanol using sonication, then centrifuged and evaporated to dryness and redissolved to 1 mL with ethanol to use for the instrumental analysis. The analytical method was validated based on linearity, recovery, limit of detection (LOD), and limit of quantification (LOQ). This method was suitable to determine low amounts of naturally occurring preservatives (benzoic acid, sorbic acid, and propionic acid) in various spices. Benzoic acid, sorbic acid, and propionic acid were found in 165 samples, 88 samples, and 398 samples, respectively from the total of 493 samples. The concentration of benzoic acid, sorbic acid, and propionic acid were ranged at ND-391.99 mg/L, ND-57.70 mg/L, and ND-188.21 mg/L in spices, respectively. The highest mean levels of benzoic acid, sorbic acid, and propionic acid were found in cinnamon (167.15 mg/L), basil leaves (22.79 mg/L), and white pepper (51.48 mg/L), respectively. The results in this study provide ranges of concentration regarding naturally occurring benzoic acid, sorbic acid, and propionic acid in spices. Moreover, the results may use to the case of consumer complaint or trade friction due to the inspection services of standard criteria for the preservatives of spices.

• Journal of Food Hygiene and Safety
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• v.33 no.5
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• pp.375-382
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• 2018

This study was conducted to analyze the microbial community and propionic acid production ability of natural microflora in the rice cakes. Genetic analysis of natural microflora in Jorangyi rice cake was performed to select propionic acid - producing bacteria. Selected propionic acid-producing bacteria were cultivated in TSB (tryptic soy broth) supplemented with glucose, and growth characteristics were analyzed by temperature and production of propionic acid was analyzed by gas chromatography (GC-FID). Linearity, detection limit, quantitative limit, and recovery rate were measured to verify propionic acid assay. A total of 98 microbial strains were detected from microflora of Joraengyi rice cake that grew after expiration of shelf life. Lactobacillus casei group accounted for 50.48% and Lactobacillus buchneri was 29.60%. Propionic acid - producing bacteria were Propionibacterium thoenii, P. cyclohexanicum, Propionibacterium_uc, P. jensenii, and P. freudenreichii. Natural bacteria and Lactobacillus spp. did not produce propionic acid during 14 days but P. cyclohexanicum, P. freudenreichii subsp. Shermanii, P. thoenii and P. jesenii produced $263.47{\mu}g/mL$, $338.90{\mu}g/mL$, $325.43{\mu}g/mL$ and $222.17{\mu}g/mL$ during 4 days and 2,462.02 and 2,904.78, 2,220.64, $3,519.17{\mu}g/mL$ during 14 days. As a result of this study, it was affirmed that the natural microflora of Joraengyi rice cake during storage can produce propionic acid from natural sources even if a high concentration of propionic acid is not intentionally added. Because of characteristics of rice cake composed of starch and glucose. This study will be used as a recognition criterion to detect natural preservatives such as propionic acid in starchy foods such as rice cakes and as reference standard safety management data.

• Korean Journal of Food Science and Technology
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• v.45 no.1
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• pp.20-24
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• 2013

Food irradiation has recently become one of the most successful techniques to preserve food with increased shelf life. This study aims to analyze hydrocarbons in almonds (Prunus amygosalus L.) and peanuts (Arachis hypogaea) induced by electron beam irradiation. The samples were irradiated at 0, 1, 3, 5 and 10 kGy by e-beam and using florisil column chromatography fat, and content was extracted. The induced hydrocarbons were identified using gas chromatography-mass spectrometry (GC/MS). The major hydrocarbons in both irradiated samples were 1,7-hexadecadiene ($C_{16:2}$) and 8-heptadecene ($C_{17:1}$) from oleic acid, 1,7,10-hexadecatriene ($C_{16:3}$) and 6,9-heptadecadiene ($C_{17:2}$) from linoleic acid and 1-tetradecene ($C_{14:1}$) and pentadecane ($C_{15:0}$) from palmitic acid. Concentrations of the hydrocarbons produced by e-beam were found to be depended upon the composition of fatty acid in both almonds and peanuts. The $C_{n-2}$ compound was found to be higher than $C_{n-1}$ compound in oleic acid and palmitic acid, while in case of linoleic acid, $C_{n-1}$compound was higher than $C_{n-2}$ compound. The radiation induced hydrocarbons were detected only in irradiated samples, with 1 kGy or above, and not in the non-irradiated ones. The production of 1,7-hexadecadiene ($C_{16:2}$), 8-heptadecene ($C_{17:1}$), 1,7,10-hexadecatriene ($C_{16:3}$) and 6,9-heptadecadiene ($C_{17:2}$), in high concentration gave enough information to suggest that these may be the possible marker compounds of electron beam irradiation in almonds and peanuts.

• Analytical Science and Technology
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• v.34 no.6
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• pp.259-266
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• 2021

Cannabis (Marijuana) is one of the most widely used drugs in the world, and its distribution has been controlled in South Korea since 1976. Identification of 11-nor-Δ⁹-carboxy-tetrahydrocannabinol (THCCOOH) in urine can provide important proof of cannabis use, and it is considered scientific evidence in the forensic field. In this study, we describe a simultaneous quantitative method for identifying THCCOOH and THCCOOH-glucuronide in urine, using simple liquid-liquid extraction (LLE), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). THCCOOH-D₃ and THCCOOH-glucuronide-D₃ were used as internal standards. Validation results of the matrix effect, as well as recovery, linearity, precision, accuracy, process efficiency, and stability were all satisfactory. No carryover, endogenous or exogenous interferences were observed. The limit of detection (LOD) of THCCOOH and THCCOOH-glucuronide were 0.3 and 0.2 ng/mL, respectively. The developed method was applied to 28 authentic human urine samples that tested positive in immunoassay screening and gas chromatography/mass spectrometry (GC/MS) tests. The ranges of concentrations of THCCOOH and THCCOOH-glucuronide in the samples were less than LOQ~266.90 ng/mL and 6.43~2133.03 ng/mL, respectively. The concentrations of THCCOOH-glucuronide were higher than those of THCCOOH in all samples. This method can be effectively and successfully applied for the confirmation of cannabinoid use in human urine samples in the forensic field.

• Analytical Science and Technology
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• v.22 no.6
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• pp.488-497
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• 2009

In this study, analytical methodology for several organic fatty acids (OFA: propionic acid (PA), butyric acid (BA), isovaleric acid (IA), and valeric acid (VA)) designated as new offensive odorants in Korea (as of year 2010) was investigated along with some odorous VOCs (styrene, toluene, xylene, methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, and isobutyl alcohol). For this purpose, working standards (WS) containing all of these 13 compounds were loaded into adsorption tube filled with Tenax TA, and analyzed by gas chromatography (GC) system thermal desorber interfaced with. The analytical sensitivities of organic fatty acids expressed in terms of detection limit (both in absolute mass (ng) and concentration (ppb)) were lower by 1.5-2 times than other compounds (PA: 0.24 ng (0.16 ppb), BA: 0.19 ng (0.11 ppb), IA: 0.15 ng (0.07 ppb), and VA: 0.28 ng (0.13 ppb)). The precision of BA, IA, and VA, if assessed in terms of relative standard error (RSE), maintained above 5%, while the precison of other compounds were below 5%. The reproducibility of analysis improved with the aid of internal standard calibration (PA: $1.1{\pm}0.4%$, BA: $10{\pm}0.46$, IA; $12{\pm}0.3%$, VA: $4{\pm}0.1%$), respectively. The results of this study showed that organic fatty acid can be analyzed using adsorption tube and thermal desorber in a more reliable way to replace alkali absorption method introduced in the odor prevention law of the Korea Ministry of Environment (KMOE).

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.