摘要
研究了一种基于电子鼻系统的香蕉储存时间鉴别方法。实验检测了不同储存时间的香蕉样品,主成分分析方法可以较好地区分不同储存时间的香蕉样品,同时检验了样品的微生物指标以探讨电子鼻响应与微生物指标之间的关系。随机共振信噪比谱不但可以区分香蕉样品,同时基于信噪比特征值建立的香蕉储存时间鉴别模型具有较高的预测准确率。该方法具有较好的实际应用价值。
Based on electronic nose,a predictive banana storage time method was been investigated. Experiments on banana samples of different storage time were conducted. The principle component analysis （ PCA ） method discriminated different samples successfully. The microbial index was examined to investigate the relationship between electronic nose responses and microbial measurement results. Stochastic resonance signal-to-noise ratio （SNR） spectrum could also distinguished different samples. The storage time predicting model based on SNR features presented high predicting accuracy. This method was of good application value.
出处
《传感技术学报》
CAS
CSCD
北大核心
2012年第5期566-570,共5页
Chinese Journal of Sensors and Actuators
基金
浙江省公益技术应用研究项目(2011C21051)
国家自然科学基金项目(81000645)
浙江省自然科学基金项目(Y1100150)
浙江省大学生科技创新活动计划项目(2010R408015)
浙江工商大学大学生创新项目(11-143
11-145
11-159)
关键词
电子鼻
香蕉储存时间
随机共振
信噪比分析
electronic nose
banana storage time
stochastic resonance
signal-to-noise ratio analysis
