摘要
本文研究了一种芒果储存期预测方法,使用智能电子鼻实验检测了存储于9天内的芒果样品,主成分分析法实现了不同贮存时间芒果样品的区分,采用阈值随机共振方法提取芒果品质特性信息,并以互相关系数极大值构建芒果储存期预测模型。预测实验结果表明该模型预测准确度为87.5%。该预测方法具有检测快速、准确性好、成本低等优势。
In this paper,a mango storage time predicting method utilizing electronic nose is proposed.The electronic nose responses to mango samples stored within 9 days are measured.Principal component analysis（PCA）method can distinguish mango samples of the different storage time.The aperiodic stochastic resonance method is used to extract the mango quality features,and the cross-correlation coefficient maximums are used to build mango storage time predicting model.The validating experiments results indicate that the predicting accuracy of the developed model is 87.5%.This method presents some advantages including rapid detection,good accuracy,and low cost.
出处
《传感技术学报》
CAS
CSCD
北大核心
2012年第9期1199-1203,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
芒果品质分析
非周期随机共振
互相关系数
electronic nose
mango quality analysis
aperiodic stochastic resonance
ross-correlation coefficient
