鸡蛋贮藏时间和新鲜度的电子鼻检测

研制一套适合鸡蛋品质的电子鼻系统,对不同贮藏方式和时间的鸡蛋进行检测,并采用主成分分析(PCA)、线性判别函数分析(LDA)和BP神经网络(BPNN)、遗传优化神经网络(GANN)等进行模式识别.结果发现,不同贮藏时间的鸡蛋所对应的电子鼻传感器响应特性不同.PCA和LDA结果表明,两者都可以较好地区分不同贮藏方式和时间的鸡蛋,采用LDA的效果比PCA的效果好;采用BP神经网络和遗传优化神经网络方法,能较好地预测不同贮藏时间的鸡蛋,其中遗传优化神经网络的判断正确率高于标准BP网络;利用二次回归分析(QPSR)建立鸡蛋新鲜度指标的预测模型,模型的预测值和测试值的相关系数大于0.90.

Electronic nose technique potential monitoring for storage time and quality attribute of egg

An electronic nose (E nose) was developed to distinguish different stored eggs by using principal component analysis (PCA), linear discriminant analysis (LDA), BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E nose can distinguish eggs of different storage time under cool and room temperature storage by LDA and PCA. LDA preceded PCA. Better prediction values were obtained by GANN than by BPNN. Relationships were established between the E nose signal and egg quality indices by quadratic polynomial step regression (QPSR). The prediction models indicated good prediction performance with correlation coefficient between predicted and measured values greater than 0.90.