PEN s s 200s 不同温度下 6 种贝类挥发性气味指纹 LDA WinMuster LDA LDA % DFA LDA 70% ~ % % % 90. 5

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1 ~ 0868 Journal of Nuclear Agricultural Sciences 为建立一种快速有效的识别贝类的科学方法, 运用电子鼻区分不同种类和不同加热温度的贝类, 建立一个包含所有种类和温度的贝类模型, 通过判别函数法对该模型进行验证结果表明, 电子鼻能够 区分不同种类以及不同加热温度的贝类 ; 建立的贝类气味指纹模型的成功率可达到 95% 以上可知利用电子鼻建立贝类的气味指纹模型是可行的该模型准确灵敏, 可为食品特别是水产品的快速检测提 供了依据 贝类 ; 电子鼻 ; 气味指纹 ; 模型识别 DOI /j. issn 样品处理 g min 0. 2g 30 min 电子鼻检测 AIRSENSE sxtydingyuan@ 163. com suxiurong@ nbu. edu. cn

2 PEN s s 200s 不同温度下 6 种贝类挥发性气味指纹 LDA WinMuster LDA LDA % DFA LDA 70% ~ % % % % % 85% 11 LDA 6 Fig. 1 1 LDA Linear discriminant analysis for different shellfish at different temperature conditions

3 同一温度下 6 种贝类挥发性气味指纹 LDA LDA LDA % % % LDA LDA Fig. 3 Linear discriminant analysis for different shellfish Fig LDA Linear discriminant analysis for different shellfish at the same temperature conditions

4 Table 1 1 Actual kind Euclidean Distance Mahalanobis distance Identification of shellfish by Electronic nose Identification method Correlation Discriminant function method % Moerella iridescens % % 5 cases were all 5 cases were all 5 cases were all % % identified as Moerella identified as Moerella identified as Moerella 5 cases were all identified as Moerella iridescens. Definiteness iridescens iridescens iridescens was % % % % %. Scapharca subcrenata Scallop Tegillarca granosa % % 5 cases were all 5 cases were all 5 cases were all % % % identified as Scapharca identified as Scapharca identified as Scapharca 5 cases were all identified as Scapharca subcrenata. subcrenata subcrenata subcrenata Definiteness was % % % % % % % 5 cases were all 5 cases were all 5 cases were all % % % identified as Scallop identified as Scallop identified as Scallop 5 cases were all identified as Scallop. Definiteness was % % % % % % % 5 cases were all 5 cases were all 5 cases were all % % % identified as Tegillarca granosa identified as Tegillarca granosa identified as Tegillarca granosa 5 cases were all identified as Tegillarca granosa Definiteness was % % % % % % Ruditapes philippina % % % % 5 cases were all 5 cases were all 5 cases were all 5 cases were all identified as Ruditapes philippina. identified as Ruditapes philippina identified as Ruditapes philippina identified as Ruditapes philippina Definiteness was % % % % % Cyclina sinensis % % 5 cases were all 5 cases were all 5 cases were all % % % identified as Cyclina identified as Cyclina identified as Cyclina 5 cases were all identified as Cyclina sinensis. Definiteness sinensis sinensis sinensis was % % % % % Fig. 4 4 Validation results of Moerella iridescens 2. 3 DFA 4 4 ~ 9

5 5 865 Fig. 5 5 Validation results of Scapharca subcrenata Fig. 6 6 Validation results of Scallop Fig. 7 7 Validation results of Tegillarca granosa DFA 95% DFA 1 3

6 Fig. 8 8 Validation results of Ruditapes philippina Fig. 9 9 Validation results of Cyclina sinensis Fig Validation results of Sinonovacula constricta

7 LDA DFA 6 95% 1. GC-MS J LDA array system J J J GC-MS 10 4 LDA J D. 4 John - Erik H Frank L Jens P W Annette V. Detection of rancidity in freeze stored turkey meat using acommercial gas-sensor. Sensors and Actuators Tiina R Hanna-Leena A Tiina R Eija S Maria S Raija. Application of an electronic nose for quality assessment of modified atmosphere packaged shrimp meat J. Food Control J J J J J J J S

8 868 Journal of Nuclear Agricultural Sciences ~ 0868 Establish an Identification Model for Shellfish with the Electronic Nose DING Yuan 1 XU Mao-qin 2 MIAO Fang-fang 1 CAI Jiang-jia 1 ZHOU Jin 1 ZHANG Chun-dan 1 LI Ye 1 SU Xiu-rong 1 1 School of Marine Sciences Ningbo University Ningbo Zhejiang Ningbo City College of Vocational Technology Ningbo Zhejiang Abstract Developing a scientific processing method to distinguish different shellfish effectively. Use the electronic nose to detect the different kinds and heating temperature of shellfish and establish an model for shellfish. In addition testing the Correctness of the model by discriminant function. The electronic nose is able to distinguish the different shellfish and different heating temperature and the flavor fingerprint model is confirmed accurately with higher than 95% success rate. With the establishment of the flavor fingerprint model electronic nose is able to quickly detect the species of different shellfish providing a basis testing for food especially aquatic products. Key words Shellfish Electronic nose Flavor fingerprint Model identification