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Evaluation of tomato maturity by electronic nose

文献类型： 外文期刊

作者： Gomez, Antihus Hernandez 1 ; Hu, Guixian 2 ; Wang, Jun 2 ; Pereira, Annia Garcia 2 ;

作者机构： 1.Zhejiang Univ, Dept Agr Engn, Hangzhou 310029, Peoples R China

2.Zhejiang Univ, Dept Agr Engn, Hangzhou 310029, Peoples R China; Havana Agr Univ, Agr Mech Fac, Havana, Cuba; Zhejiang Acad Agr Sci, Hangzhou 310021, Peoples R China

关键词： electronic nose;non-destructive method;monitoring;maturity;tomato

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2006 年 54 卷 1 期

页码：

收录情况： SCI

摘要： Over the past years, electronic nose (E-nose) technology opened has enhanced the possibility of exploiting information on behavior aroma to assess fruit ripening stage. The objective in this study was to evaluate the capacity of electronic nose to monitor the change in volatile production of ripeness states for tomato, using a specific electronic nose device with 10 different metal oxide sensors (portable E-nose, PEN 2). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different ripeness states (unripe, half-ripe, full-ripe and over-ripe). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. The results prove that the electronic nose PEN 2 could differentiate among the ripeness states of tomato. The electronic nose was able to detect a clearer difference in volatile profile of tomato when using LDA analysis than when using PCA analysis. Using LDA analysis, it was possible to differentiate and to classify the different tomato maturity states, and this method was able to classify 100% of the total samples in each respective group. Some sensors in E-nose have the highest influence in the current pattern file for electronic nose PEN 2. A subset of a few sensors in E-nose can be chosen to explain all the variance. This result could be used in further studies to optimize the number of sensors. (c) 2006 Elsevier B.V. All rights reserved.

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