电子鼻技术在区分酸羊奶发酵菌种中的应用

Discrimination of Lactic Acid Bacteria in Goat Yogurt Using Electronic Nose

利用电子鼻技术快速区分酸羊奶的发酵菌种。通过电子鼻采集不同酸羊奶挥发成分的响应值,然后利用主成分分析(principal component analysis,PCA)、Fisher线性判别分析(fisher linear discriminant analysis,FLDA)以及BP神经网络(back propagation neural network,BP-NN)分析进行判别,建立基于电子鼻技术区分酸羊奶发酵菌种的方法。结果表明,FLDA及PCA都能够区分出不同菌种发酵的酸羊奶,FLDA区分效果优于PCA。利用FLDA和BP-NN分析预测酸羊奶发酵菌种类别的正确率分别为100.0%和98.4%。因此,利用电子鼻快速区分酸羊奶的发酵菌种是可行的。

This study attempted to use an electronic nose(PEN3) to discrimina te the strains of lactic acid bacteria in goat yogurt samples. The volatile components emanating from goat yogurt samples were gathered by the electronic nose. Based on the data obtained, a method for discriminating the strains of lactic acid bacteria in goat yogurt was established through principal component analysis(P CA), Fisher linear discriminant analysis(FLDA) and BP neural network. The results showed that although both PCA and FLDA could discriminate differ ent species of lactic acid bacteria, FLDA was more effective than PCA. The correct prediction rates of FLDA and BP neural network were 100.0% and 98.4%, respectively. These res ults will be helpful for the application of electronic nose to discriminate the strains of lactic acid bacteria in goat yogurt samples.