http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 박수지 ( Sue Jee Park ) (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
홍은정,박수지,이화정,이광근,노봉수,Hong, Eun-Jeung,Park, Sue-Jee,Lee, Hwa-Jung,Lee, Kwang-Geun,Noh, Bong-Soo 한국축산식품학회 2011 한국축산식품학회지 Vol.31 No.2
전자코를 이용하여 아카시아 꿀, 잡화 꿀, 밤 꿀, 유채꿀, 사양 꿀, 목청 꿀, 기타 꿀을 대상으로 밀원에 따른 차이를 구분하였다. 그 결과 꿀 종류에 따라 뚜렷이 구분 가능하였으며 각기 다른 꿀을 혼합하였을 때 혼합 비율에 따라서도 구분 가능하였다. 0, 5, 10, 15, 20, 25, 30%의 사양 꿀을 목청 꿀에 혼합하였을 때 혼합비율이 증가함에 따라 41, 42, 43, 45, 46, 47, 48, 54, 56, 58, 60 amu의 감응도 값이 크게 나타났으며 판별함수분석한 결과 사양 꿀의 혼합비율이 증가함에 따라 DF1값이 양의 방향에서 음의 방향으로 일정한 경향을 보이며 뚜렷이 구분되었다(DF1: $r^2$=0.9962, F=490.6 DF2: $r^2$=0.9128, F=19.44). 이를 통하여 DF1값과 목청 꿀에 사양 꿀의 혼합 농도에 대해 DF1=-0.106$^*$(사양 꿀의 농도)+0.426($r^2$=0.96)와 같은 높은 상관관계식을 얻었다. 아카시아 꿀의 경우에도 목청 꿀과 유사하게 사양 꿀의 혼합비율이 증가함에 따라 41, 43, 45, 46, 47, 56, 57, 58, 59, 60, 61, 70 amu의 감응도 값이 크게 나타났으며 DF1과 사양 꿀 혼합 비율간에 높은 상관관계를 보이며 일정한 경향을 나타냈고(DF1: $r^2$=0.9957, F=396.64 DF2: $r^2$=0.9447, F=29.3) DF1=-0.112$^*$(사양 꿀의 농도)+0.434($r^2$=0.968)의 상관관계식을 갖는다. Various honeys from different sources were analyzed using an electronic nose based on a mass spectrometer. Various honeys were separated with different mixing ratios. Wild honey and artificial honey were blended at ratios of 100:0, 95:5, 90:10, 85:15, 80:20, 75:25, and 70:30, respectively. Data obtained from the electronic nose were used for discriminant function analysis (DFA). The DFA plot indicated a significant separation of honey from different sources. As the concentration of artificial honey increased, the first discriminant function score (DF1) moved from positive to negative (DF1: $r^2$=0.9962, F=490.6; DF2: $r^2$=0.9128, F=19.44). Furthermore, when acacia honey was mixed with artificial honey and separated with the mixing ratios, the DF scores were: DF1: $r^2$=0.9957, F=396.64; DF2: $r^2$=0.9447, F=29.3. When artificial honey was added to wild honey, it was possible to predict the following equation; DF1= -0.106${\times}$(concentration of artificial honey)+0.426 ($r^2$= 0.96). For acacia honey, the DF1= -0.112${\times}$(concentration of artificial honey)+0.434 ($r^2$=0.968).
-
여러 가지 LED를 처리한 전지유, 무지방 우유, LTLT, UHT, HTST 처리 우유의 휘발성분 패턴 분석
김기화,홍은정,박수지,강지원,노봉수,Kim, Ki-Hwa,Hong, Eun-Jeung,Park, Sue-Jee,Kang, Jee-Won,Noh, Bong-Soo 한국축산식품학회 2011 한국축산식품학회지 Vol.31 No.4
본 연구는 우유의 지방산패가 특정 파장에 의해 변화되는 것을 토대로 어떠한 파장이 영향을 주는지 알아보기 위하여 전지유, 무지방 우유와 LTLT, HTST, UHT 처리한 우유를 대상으로 각기 다른 파장의 LED로 처리한 후 이들의 휘발성분 생성패턴을 6일 동안 저장하면서 MS-전자코로 분석하였다. 전자코를 통해 얻어진 데이터는 판별함수분석을 통해 분석하였다. 우유의 종류별로 전지유는 파란색 파장하에서 영향을 가장 많이 받는 것으로 나타났고 무지방은 빨강, 노랑색 파장 하에서 산패에 영향을 미쳤다. 전지유의 영향을 가장 많이 준 파란 빛 파장으로 LTLT, HTST, UHT처리한 우유의 변화 정도를 알아본 결과 LTLT 처리한 우유의 ${\Delta}DF1$값이 UHT처리한 우유와 HTST처리한 우유의 ${\Delta}DF1$값보다 크게 변화하는 것으로 보아 품질 변화가 많이 일어난 것으로 나타났다. LED 처리를 함에 따라 acetaldehyde, propanal, pentanal, hexanal, heptanal, nonanal, 3-methyl butanal, 2-methyl propanal, 2-butanone, 2-pentanone, 2-hexanone, 2-heptanaone and 2-nonanone 등에 해당하는 amu값에서의 감응도 값이 변화한 것으로 보아 이들 물질이 생성된 것으로 예상되었다. The objective of this study was to analyze the pattern recognition of volatile compounds from different types of milk under LED (Light Emitting Dioxide) irradiation for 6 d. Yellow, red, blue, dark, and fluorescent light were produced using LED equipment. A mass spectrometry-based electronic nose and DFA (discriminant function analysis) were used to determine the change in volatiles from different types of milk under LED irradiation. As the LED exposure time was increased, DF1 of whole milk changed considerably under blue light, while that of skim milk changed significantly under red and yellow light irradiation. Among the types of milk tested, the most light-induced oxidation sample was LTLT milk under blue light. The volatile compounds that were shown to increase due to LED treatment in the electronic nose analysis, which was based on MS, were mainly acetaldehyde, propanal, pentanal, hexanal, heptanal, nonanal, 3-methyl butanal, 2-methyl propanal, 2-butanone, 2-pentanone, 2-hexanone, and 2-heptanaone and 2-nonanone.
-
유사 식품인 오징어 젓갈과 한치 젓갈의 판별을 위한 MS-전자코 분석
홍은정 ( Eun Jeung Hong ),김기화 ( Ki Hwa Kim ),박수지 ( Sue Jee Park ),강지원 ( Jee Won Kang ),김동술 ( Dong Sul Kim ),이화정 ( Hwa Jung Lee ),김은정 ( Eun Jeong Kim ),이재황 ( Jae Hwang Lee ),김승희 ( Seung Hee Kim ),이광호 ( Kw 한국산업식품공학회 2011 산업 식품공학 Vol.15 No.2
In this study, discrimination of salted cuttle fish and salted mitra squid was carried out using electronic nose based on mass spectrometer. Intensities of each fragment from salted cuttlefish by electronic nose were completely different from those of salted mitra squid. Each sample was analyzed, and discriminant function analysis (DFA) was used for the discrimination of similar products. DFA plot indicated a significant separation of each salted cuttlefish and mitra squid (r2=0.8789, F=162.13). Electronic nose based on mass spectrometer could be used as an efficient method for discrimination of Economically Motivated Authentication (EMA) foods.
-
홍은정 ( Eun Jeung Hong ),김기화 ( Ki Hwa Kim ),박수지 ( Sue Jee Park ),김지은 ( Ji Eun Kim ),김동술 ( Dong Sul Kim ),이화정 ( Hwa Jung Lee ),김은정 ( Eun Jeong Kim ),이재황 ( Jae Hwang Lee ),김승희 ( Seung Hee Kim ),이광호 ( Kwan 한국산업식품공학회 2011 산업 식품공학 Vol.15 No.2
In this study, discrimination of shred of frozen and dried Alaska pollack, shred of dried pollack and shred of cod using electronic nose based on mass spectrometer was carried out. Intensities of each fragment from shred of frozen and dried Alaska pollack by e-nose were completely different from those of dried pollack and cod. Each sample was analyzed, and discriminant function analysis was used for the discrimination of similar products. DFA plot indicated a significant separation of each shred of frozen and dried Alaska pollack, shred of dried pollack and shred of cod (r2=0.7787, F=185.2). E-nose based on MS system could be used as an efficient method for discriminant of EMA foods.
-
질량분석기 기반 전자코를 이용한 LED조사 후 옥수수유의 향기패턴분석
김기화,박수지,노봉수 서울여자대학교 자연과학연구소 2011 자연과학연구논문집 Vol.23 No.-
The objective of this study was to analyze volatile compounds from corn oil under Light Emitting Diode (LED) irradiation for 12 weeks. Yellow, red, blue, and fluorescent light were treated by LED equipment. Storage in the dark was also conducted at the same time. A mass spectrometry-based electronic nose and discriminant function analysis (DFA) were used to monitor the changes of volatile compounds of corn oil under LED irradiation. As exposure time of LED treatment increased, volatile compounds of corn oil increased considerably under blue light followed by fluorescent, red, and yellow light. It showed the least changes under the dark condition. The volatile compounds from corn oil were identified as butanal, 2-butenal, pentanal, 2-pentenal, hexanal, 2-hexenal, heptanal, 2-heptenal, 2,4-heptadienal, octanal, 2-octenal, nonanal, 2-nonenal, decanal, 2-decenal, 2,4-decadienal, 2-undecenal, 2-pentylfuran, methyl 8-oxooctanoate, methyl 10-oxodecanoate, methyl 11-oxo-9-undecenoate,1-heptanol, 1-pentanol, 1-octen-3-ol, heptane, octane, and pentane. Key words:corn oil, lipid oxidation, light emitting dioxide, oxidative stability, electronic nose.
ScienceON
스콜라연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
