Suthee MangmeeOnrapak ReamtongThareerat KalambahetiSittiruk RoytrakulPiengchan SonthayanonMahidol UniversityThailand National Center for Genetic Engineering and Biotechnology2020-03-262020-03-262020-07-01Food Control. Vol.113, (2020)095671352-s2.0-85080980666https://repository.li.mahidol.ac.th/handle/20.500.14594/53511© 2020 The Authors Rapid and reliable detection of non-typhoidal Salmonella (NTS) is essential for effective monitoring and controlling in broiler industries. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been reported as a sensitive and accurate method for microbial investigations at genus and species level while subspecies level is still obscure. Here, we developed a MALDI-TOF MS-based method to improve the simultaneous identification of species, subspecies, and serovars of NTS isolated from broiler samples in a Thai slaughtering and processing factory. Whole-cell peptide patterns from 142 NTS isolates were integrated with the commercial database for species and subspecies identification based on weighted pattern (subtyping MSP) matching. Serovar-specific peaks were searched and determined using the machine-learning analysis. The classification tree was created for detection of the five predominant NTS serovars (i.e., Albany, Agona, Typhimurium/I 4,[5],12:i:-, Altona, and Enteritidis). One hundred and forty-five NTS isolates were evaluated and yielded all accurate identification at species and subspecies level corresponding to conventional methods. Besides, the serovar classification was achieved with 99.3% accuracy when compared with serotyping. This method would be useful for large scale screening of NTS serovars in food industries where cost-effectiveness, rapid and highly accurate methods are required.Mahidol UniversityAgricultural and Biological SciencesBiochemistry, Genetics and Molecular BiologyArticleSCOPUS10.1016/j.foodcont.2020.107188