为提高基质辅助激光解析电离飞行时间质谱技术（MALDI-TOF MS）在李斯特氏菌属鉴定中的分辨能力，建立快速准确鉴定单增和英诺克李斯特氏菌的质谱学方法。通过采集79株单增和57株英诺克李斯特氏菌的指纹图谱，利用ClinPro tools软件对数据进行统计学分析，建立数学判别模型并验证其准确性。峰统计结果显示，两组数据峰强度差异显著的特征峰有16个，推测出单增李斯特氏菌生物标志物6个，英诺克李斯特氏菌10个，发现在单增李斯特氏菌中质量峰3985/7970 u和3972/7942 u是独立且连锁存在。基于遗传算法的判别模型交叉验证率和检测识别能力最强，分别为99.44%和100.00%，经验证准确率达到96%以上，可实现对单增和英诺克李斯特氏菌的快速准确鉴定。同时，利用Bruker Biotyper软件将以上菌株建库形成了实验室内部李斯特氏菌谱库，对8株未测李斯特氏菌进行搜库鉴定，匹配分数均高于商品化数据库，提升了MALDI-TOF MS对李斯特菌属的自动鉴定能力。

To improve the discrimination ability of MALDI-TOF MS in identifying Listeria species, the objective of this work was to establish a rapid and accurate MS method to discriminate L. monocytogenes and L. innocua. Protein mass spectra of 79 verified L. monocytogenes and 57 L. innocua strains were generated using MALDI-TOF MS and subsequently analyzed with ClinPro tools software for specific biomarker identification and discrimination model establishment. The results of peak statistic showed that 16 specific peaks with significant difference were identified, including 6 L. monocytogenes and 10 L. innocua specific biomarker candidates, respectively. It was found that the mass peaks 3985/7970 u and 3972/7942 u in Listeria monocytogenes were independent and linked. Furthermore, the discrimination model based on genetic algorithm presented better cross validation (99.44%) and recognition capability (100.00%), and correct classified rate could reach above 96%, which can obtain the rapid and accurate identification for L. monocytogenes and L. innocua. Meanwhile, main spectra of a defined collection of these strains were compiled using Bruker Biotyper software and added to an in-house reference library. Evaluation of this library with 8 untested Listeria strains yielded improved score values, which were higher than that of commercial database. Application of the in-house database can improve the automatic identification capability of MALDI-TOF MS for Listeria species.

湖北省食品质量安全监督检验研究院自主立项科研项目（ZZLX2018005）