Fast detection and quantification of four dairy propionic acid bacteria in milk samples using real-time quantitative polymerase chain reaction

One-Year Monitoring of Prevalence and Diversity of Dairy Propionic Acid Bacteria in Raw Milk by Means of Culture-Dependent and Culture-Independent Methods

Even low levels of dairy propionic acid bacteria (dPAB) can cause cheese defects, resulting in severe economic losses for the producers of selected raw milk cheeses. Therefore, routine quality control of raw cheese milk for dPAB contamination is essential if propionic acid fermentation is undesired. Although knowledge of dPAB contamination of raw milk is important to understand cheese spoilage, long-term dPAB screening data are outdated, and studies taking into account different farm management parameters and their potential influence on dPAB levels are scarce. This study aims to provide insight into the dPAB levels of raw milk over time, to identify farm management factors that potentially influence dPAB levels, and to compare a cultural yeast extract lactate agar (YELA) and lithium glycerol agar (LGA) and a culture-independent method (qPCR) for dPAB quantification with respect to their applicability in routine quality control for the dairy industry. For this purpose, bulk tank milk from 25 dairy farms was screened for dPAB contamination over a one-year period. We were able to identify significant differences in the dPAB contamination levels in raw milk depending on selected farm-specific factors and observed relationships between the different types of milking systems and dPAB contamination levels in raw milk. When dPAB were quantified by cultivation on YELA, strong overgrowth of commensal microbiota impeded counting. Therefore, we conclude that quantification on LGA or by qPCR is preferable. Both methods, colony counting on LGA as well as quantification of dPAB using qPCR, have advantages for the application in (routine) quality control of raw milk, one being low-tech and inexpensive, the other being fast and highly specific, but the detection of (low level) dPAB contamination in raw milk remains a challenge.

Propionic acid bacteria in the food industry: An update on essential traits and detection methods

Propionic acid bacteria (PAB) is an umbrella term for a group of bacteria with the ability to produce propionic acid. In the past, due to this common feature and other phenotypic similarities, genetically heterogeneous bacteria were considered as a single genus, Propionibacterium. Members of this genus ranged from "dairy propionibacteria," which are widely known for their role in eye and flavor formation in cheese production, to "cutaneous propionibacteria," which are primarily associated with human skin. In 2016, the introduction of two new genera based on genotypic data facilitated a clear separation of cutaneous (Cutibacterium spp.) from dairy PAB (Propionibacterium spp., Acidipropionibacterium spp.). In light of these taxonomic changes, but with particular emphasis on dairy PAB, this review describes the current state of knowledge about metabolic pathways and other characteristics such as antibiotic resistance and virulence factors. In addition, the relevance of dairy PAB for the food industry and cheese production in particular is highlighted. Furthermore, methods for cultivation, detection, and enumeration are reviewed, incorporating the current taxonomy as well as the potential for routine applications.

Quantitatively determining the total bacterial count of raw goat milk using dielectric spectra

The objective of this study was to evaluate dielectric spectra as a means of quantitatively determining total bacterial count (TBC) of raw goat milk. The dielectric spectra, including dielectric constant (ε') spectra and dielectric loss factor (ε″) spectra, and TBC of 154 raw goat milk samples were measured using network analyzer and plate count methods, respectively. Owing to the poor linear relationship between TBC in logarithm and permittivities at a single frequency, chemometrics was used to reduce noise, identify outliers, select effective variables, and divide sample sets. Several linear models, such as multiple linear regression, ridge regression, and least absolute shrinkage and selection operator, were established to determine TBC based on the effective spectra of ε', ε″, and their combination (ε'+ε″). The results indicated that the models built using the spectra of ε'+ε″ and ε' had excellent TBC prediction performance. The best model was multiple linear regression based on ε'+ε″ spectra with the residual predictive deviation of 3.26. This study shows that the dielectric spectra had great potential to quantitatively and rapidly determine TBC of raw milk.

Protected Designation of Origin (PDO), Protected Geographical Indication (PGI) and Traditional Speciality Guaranteed (TSG): A bibiliometric analysis

Despite the importance of the literature on food quality labels in the European Union (PDO, PGI and TSG), our search did not find any review joining the various research topics on this subject. This study aims therefore to consolidate the state of academic research in this field, and so the methodological option was to elaborate a bibliometric analysis resorting to the term co-occurrence technique. Analysis was made of 501 articles on the ISI Web of Science database, covering publications up to 2016. The results of the bibliometric analysis allowed identification of four clusters: "Protected Geographical Indication", "Certification of Olive Oil and Cultivars", "Certification of Cheese and Milk" and "Certification and Chemical Composition". Unlike the other clusters, where the PDO label predominates, the "Protected Geographical Indication" cluster covers the study of PGI products, highlighting analysis of consumer behaviour in relation to this type of product. The focus of studies in the "Certification of Olive Oil and Cultivars" cluster and the "Certification of Cheese and Milk" cluster is the development of authentication methods for certified traditional products. In the "Certification and Chemical Composition" cluster, standing out is analysis of the profiles of fatty acids present in this type of product.