Coagulase-positive Staphylococcus aureus is a foodborne pathogen considered one of the causes of food-related disease outbreaks. Like S. aureus, Staphylococcus capitis, Staphylococcus caprae, and S. epidermidis are opportunistic pathogens causing clinical infections and food contamination. The objective of our study was to develop a rapid, accurate, and monitoring technique to detect four Staphylococcus species in food. Four novel molecular targets (GntR family transcriptional regulator for S. aureus, phosphomannomutase for S. epidermidis, FAD-dependent urate hydroxylase for S. capitis, and Gram-positive signal peptide protein for S. caprae) were mined based on pan-genome analysis. Primers targeting molecular target genes showed 100% specificity for 100 non-target reference strains. The detection limit in pure cultures and artificially contaminated food samples was 10² colony-forming unit/mL for S. aureus, S. capitis, S. caprae, and S. epidermidis. Moreover, real-time polymerase chain reaction successfully detected strains isolated from various food matrices. Thus, our method allows an accurate and rapid monitoring of Staphylococcus species and may help control staphylococcal contamination of food.

Rapid detection of Salmonella serovars is important for the effective control and monitoring of food industries. In this study, we evaluate the application of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for the rapid detection of three serovars, Enteritidis, Typhimurium, and Thompson, that are epidemiologically important in Korea. All strains were identified at the genus level, with a mean score of 2.319 using the BioTyper database, and their protein patterns were confirmed to be similar by principal component analysis and main spectrum profile dendrograms. Specific peaks for the three serovars were identified by analyzing 65 reference strains representing 56 different serovars. Specific mass peaks at 3018 ± 1 and 6037 ± 1, 7184 ± 1, and 4925 ± 1 m/z were uniquely found in the reference strains of serovars Enteritidis, Typhimurium, and Thompson, respectively, and they showed that the three serovars can be differentiated from each other and 53 other serovars. We verified the reproducibility of these mass peaks in 132 isolates, and serovar classification was achieved with 100% accuracy when compared with conventional serotyping through antisera agglutination. Our method can rapidly detect a large number of strains; hence, it will be useful for the high-throughput screening of Salmonella serovars.

In recent years, Salmonella Infantis has become a predominant serovariant in clinical and poultry isolates, thereby imposing a substantial economic burden on both public health and the livestock industry. With the aim of coping with the steep increase in serovar Infantis prevalence, a polymerase chain reaction (PCR)-based rapid and accurate diagnostic assay was developed in this study through pangenome profiling of 60 Salmonella serovars. A gene marker, SIN_02055, was identified, which is present in the S. Infantis genome but not in the pangenome of the other serovars. Primers specific to SIN_02055 were used to accurately detect serovar Infantis, and to successfully differentiate Infantis from the other 59 serovars in real-time PCR with a R² of 0.999 and an efficiency of 95.76%. The developed method was applied to 54 Salmonella strains belonging to eight dominant serovars, and distinguished Infantis from the other seven serovars with an accuracy of 100%. The diagnostic primer set also did not show false positive amplification with 32 strains from eight non-Salmonella bacterial species. This cost-effective and rapid method can be considered an alternative to the classic serotyping using antisera.

Droplet digital polymerase chain reaction (ddPCR) is one of the newest and most promising tools providing absolute quantification of target DNA molecules. Despite its emerging applications in microorganisms, few studies reported its use for detecting lactic acid bacteria. This study evaluated the applicability of a ddPCR assay targeting molecular genes obtained from in silico analysis for detecting Lactiplantibacillus plantarum subsp. plantarum, a bacterium mainly used as a starter or responsible for fermentation in food. The performance characteristics of a ddPCR were compared to those of a quantitative real-time PCR (qPCR). To compare the linearity and sensitivity of a qPCR and ddPCR, the calibration curve for a qPCR and the regression curve for a ddPCR were obtained using genomic DNA [10²–10⁸ colony-forming units (CFU)/mL] extracted from a pure culture and spiked food sample. Both the qPCR and ddPCR assays exhibited good linearity with a high coefficient of determination in the pure culture and spiked food sample (R² ≥ 0.996). The ddPCR showed a 10-fold lower limit of detection, suggesting that a ddPCR is more sensitive than a qPCR. However, a ddPCR has limitations in the absolute quantitation of high bacterial concentrations (>10⁶ CFU/mL). In conclusion, a ddPCR can be a reliable method for detecting and quantifying lactic acid bacteria in food.

Although Weissella cibaria and W. confusa are essential food-fermenting bacteria, they are also opportunistic pathogens. Despite these species being commercially crucial, their taxonomy is still based on inaccurate identification methods. In this study, we present a novel approach for identifying two important Weissella species, W. cibaria and W. confusa, by combining matrix-assisted laser desorption/ionization and time-of-flight mass spectrometer (MALDI-TOF MS) data using machine-learning techniques. After on- and off-plate protein extraction, we observed that the BioTyper database misidentified or could not differentiate Weissella species. Although Weissella species exhibited very similar protein profiles, these species can be differentiated on the basis of the results of a statistical analysis. To classify W. cibaria, W. confusa, and non-target Weissella species, machine learning was used for 167 spectra, which led to the listing of potential species-specific mass-to-charge (m/z) loci. Machine-learning techniques including artificial neural networks, principal component analysis combined with the K-nearest neighbor, support vector machine (SVM), and random forest were used. The model that applied the Radial Basis Function kernel algorithm in SVM achieved classification accuracy of 1.0 for training and test sets. The combination of MALDI-TOF MS and machine learning can efficiently classify closely-related species, enabling accurate microbial identification.

Kimchi, a traditional Korean fermented vegetable, has received considerable attention for its health-promoting effects. This study analyzes the cultivable microbial community in kimchi fermented at different temperatures using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to comprehensively understand the factors affecting the quality of kimchi. Of the 5204 strains isolated from kimchi, aligned with the in-house database, 4467 (85.8%) were correctly identified at the species level. The fermentation temperature affected the microbial community by varying the pH and acidity, which was mainly caused by temperature-dependent competition between the different lactic acid bacteria (LAB) species in kimchi. LAB, such as Levilactobacillus (Lb.) brevis and Lactiplantibacillus (Lpb.) plantarum associated with rancidity and tissue softening, proliferated faster at higher temperatures than at low temperature. In addition, LAB, such as Latilactobacillus (Lat.) sakei and Leuconostoc (Leu.) mesenteroides, which produce beneficial substances and flavor, were mainly distributed in kimchi fermented at 4 °C. This study shows as a novelty that MALDI-TOF MS is a robust and economically affordable method for investigating viable microbial communities in kimchi.