Food Science of Animal Resources (한국축산식품학회지)

Korean Society for Food Science of Animal Resources (한국축산식품학회)
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Analysis of Various Honeys from Different Sources Using Electronic Nose

다른 밀원에서 기원한 꿀의 전자코 분석

  • Hong, Eun-Jeung (Department of Food Science and Technology, Seoul Women's University) ;
  • Park, Sue-Jee (Department of Food Science and Technology, Seoul Women's University) ;
  • Lee, Hwa-Jung (National Institute of Food and Drug Safety Evaluation) ;
  • Lee, Kwang-Geun (Department of Food Science and Technology, Dongguk University) ;
  • Noh, Bong-Soo (Department of Food Science and Technology, Seoul Women's University)
  • 홍은정 (서울여자대학교 식품공학과) ;
  • 박수지 (서울여자대학교 식품공학과) ;
  • 이화정 (식품의약품안전평가원 식품감시과학팀) ;
  • 이광근 (동국대학교 식품공학과) ;
  • 노봉수 (서울여자대학교 식품공학과)
  • Received : 2010.12.23
  • Accepted : 2011.03.25
  • Published : 2011.04.30
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Abstract

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).

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.

Keywords

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