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基于逐步判别分析和BP神经网络的电子.PDF
传 感 技 术 学 报
第 23 卷 第 10 期 Vol. 23 No. 10
2010 年 10 月 CHINESE JOURNAL OF SENSORS AND ACTUATORS Oct. 2010
23 Discrimination of Different Storage Time of Pork by Electronic Nose*
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HONG Xuezhen WANG Jun
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Department of Biosystems Engineering Zhejiang University Hangzhou 310029 China
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Abstract In this study a rapid detection method based on electronic nose for pork freshness was developed. An
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electronic nose E-nose PEN 2 was employed to classify pork groups with different storage time 0 ~ 7 d 42 sam-
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ples were tested everyday. The mass of each sample was 10 g and the headspace-generated time was 5 min. The
60 th data from the response of the E-nose was extracted for further analysis. After employing the Linear Discriminant
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Analysis LDA samples could be well classified according to their storage time. Stepwise Linear Discriminant A-
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nalysis Step-LDA and Back Propagation Neural Network BPNN were also employed to predict the storage time of
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the samples. The result showed that Step-LDA got 100% training accuracy with 97. 92% prediction accuracy and
BPNN got 94. 17% training accuracy with 93. 75% prediction accuracy. This study implied that electronic nose
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