电子鼻对两种食源性致病菌在菌株水平上区分的研究

利用基于金属氧化物传感器阵列的电子鼻技术对五株单增李斯特菌和五株副溶血性弧菌的挥发性代谢产物进行研究,结合主成分分析（PCA）和聚类分析（CA）等化学计量学方法对电子鼻原始数据进行统计学分析。结果显示,电子鼻模式识别技术能够很好地将五株单增李斯特菌的挥发性代谢产物图谱进行区分,而在同一技术条件下对五株副溶血性弧菌的挥发性代谢产物图谱只能进行部分区分,利用向这五株副溶血性弧菌的培养液中添加NaCl至饱和的方法,可以将五株菌完全区分开来,区分效果显著增强。实验表明利用电子鼻结合主成分分析和聚类分析技术对食源性致病菌在菌株水平上的区分和鉴定具有一定的可行性,有望将该技术开发成为一种快速、简便、无损的食源性致病菌新型检测分型方法。

Recognition of two food-borne pathogens at strain level by using electronic nose technology

An electronic nose(E-nose)based on metal oxide sensor was used to detect the volatile metabolites from two pathogenic bacteria at the strain level. E-nose coupled with several chemometrics methods such as principal component analysis(PCA)and cluster analysis(CA)were used for statistical analysis. Qualitative discrimination results showed that the volatile metabolites of these five strains of Listeria monocytogenes(LM)could be distinguished by PCA pattern respectively,while the volatile metabolites of Vibrio...