This study was carried out for the characterization and discrimination of the indigenous Gram positive, catalase-positive cocci (GCC) population in sucuk, a traditional Turkish dry-fermented sausage. Sucuk samples, produced by the traditional method without starter culture were collected from 8 local producers in Kayseri/Turkey and a total of 116 GCC isolates were identified by using different molecular techniques. Two different molecular fingerprinting methods; namely, randomly amplified polymorphic DNA-PCR (RAPD-PCR) and repetitive extragenic palindrome-PCR (rep-PCR), were used for the clustering of isolates and identification at species level was carried out by full length sequencing of 16S rDNA. Combining the results obtained from molecular fingerprinting and 16S rDNA sequencing showed that the dominant GCC species isolated from the sucuk samples was Staphylococcus saprophyticus followed by Staphylococcus succinus and Staphylococcus equorum belonging to the Staphylococcus genus. Real-time PCR DNA melting curve analysis and high-resolution melting (HRM) analysis targeting the V1 + V3 regions of 16S rDNA were also applied for the discrimination of isolates belonging to different species. It was observed statistically different Tm values and species-specific HRM profiles for all except 2 species (S. saprophyticus and Staphylococcus xylosus) that have high 16S rDNA sequence similarity. The combination of rep-PCR and/or PCR-RAPD with 16S rRNA gene sequencing was an efficient approach for the characterization and identification of the GCC population in spontaneously fermented sucuk. On the other hand, intercalating dye assays were found to be a simple and very promising technique for the differentiation of the GCC population at species level.

A new method based on high resolution melting (HRM) analysis was developed for the differentiation and classification of the yeast species that cause food spoilage. A total 134 strains belonging to 21 different yeast species were examined to evaluate the discriminative power of HRM analysis. Two different highly variable DNA regions on the 26 rRNA gene were targeted to produce the HRM profiles of each strain. HRM-based grouping was compared and confirmed by (GTG)5 rep-PCR fingerprinting analysis. All of the yeast species belonging to the genera Pichia, Candida, Kazachstania, Kluyveromyces, Debaryomyces, Dekkera, Saccharomyces, Torulaspora, Ustilago, and Yarrowia, which were produced as species-specific HRM profiles, allowed discrimination at species and/or strain level. The HRM analysis of both target regions provided successful discrimination that correlated with rep-PCR fingerprinting analysis. Consequently, the HRM analysis has the potential for use in the rapid and accurate classification and typing of yeast species isolated from different foods to determine their sources and routes as well as to prevent contamination.

Multi Fragment Melting Analysis System (MFMAS) is a novel approach that was developed for the species-level identification of microorganisms. It is a software-assisted system that performs concurrent melting analysis of 8 different DNA fragments to obtain a fingerprint of each strain analyzed. The identification is performed according to the comparison of these fingerprints with the fingerprints of known yeast species recorded in a database to obtain the best possible match. In this study, applicability of the yeast version of the MFMAS (MFMAS-yeast) was evaluated for the identification of food-associated yeast species. For this purpose, in this study, a total of 145 yeast strains originated from foods and beverages and 19 standard yeast strains were tested. The DNAs isolated from these yeast strains were analyzed by the MFMAS, and their species were successfully identified with a similarity rate of 95% or higher. It was shown that the strains belonged to 43 different yeast species that are widely found in the foods. A clear discrimination was also observed in the phylogenetically related species. In conclusion, it might be suggested that the MFMAS-yeast seems to be a highly promising approach for a rapid, accurate, and one-step identification of the yeasts isolated from food products and/or their processing environments.

The MFMA has great potential for fast and accurate investigation of yeast communities associated with food spoilage to determine their sources and routes and to prevent contamination.