The rapid detection of pathogenic strains in food products is essential for the prevention of disease outbreaks. It has already been demonstrated that whole metagenome shotgun sequencing can be used to detect pathogens in food but, until recently, strain-level detection of pathogens has relied on whole metagenome assembly, which is a computationally demanding process. Here, we demonstrate that three short read alignment-based methods, MetaMLST, PanPhlAn, and StrainPhlAn, can accurately, and rapidly, identify pathogenic strains in spinach metagenomes which were intentionally spiked with Shiga toxin-producing Escherichia coli in a previous study. Subsequently, we employ the methods, in combination with other metagenomics approaches, to assess the safety of nunu, a traditional Ghanaian fermented milk product which is produced by the spontaneous fermentation of raw cow milk. We show that nunu samples are frequently contaminated with bacteria associated with the bovine gut, and worryingly, we detect putatively pathogenic E. coli and Klebsiella pneumoniae strains in a subset of nunu samples. Ultimately, our work establishes that short read alignment-based bioinformatics approaches are suitable food safety tools, and we describe a real-life example of their utilisation.