Biofilms formed by pathogenic or spoilage microorganisms have become serious issues in the dairy industry, as this mode of life renders such microorganisms highly resistant to cleaning-in-place (CIP) procedures, disinfectants, desiccation, and other control strategies. The advent of omics techniques, especially the integration of different omics tools, has greatly improved our understanding of the features of microbial biofilms, and provided in-depth knowledge on developing effective methods that are directly against deleterious biofilms. This review provides novel insights into the single use of each omics tool and the application of multiomics tools to unravel the mechanisms of biofilm formation, specific molecular phenotypes exhibited by biofilms, and biofilm control strategies. Challenges and future perspective on the integration of omics tools for biofilm studies are also addressed.

Lipids in dairy products, as the crucial components in the most essential biological processes, were among the many nutrients delivered to the infants and adults. The structures of lipids are not only intricate but also varied, which makes the holistic and systematic analyses challenging. In recent years, lipidomics, as the new promoter of lipomics in the omics era, has become one of the fastest growing scientific disciplines in food science research. In addition, lipidomics is also one of the major methods to explore the dynamic changes and chemical compositions of lipids in dairy products in recent years. It is a relatively new frontier of intermolecular interactions research, using advanced liquid chromatography mass spectrometry technology to explore lipids. In this paper, the latest progress of liquid chromatography mass spectrometry based lipidomics was reviewed, with emphasis on the application of lipidomics and metabolic disease risk in dairy research. This includes a detailed workflow for routine lipidomics analysis, as well as examples of applications devoted dairy foods components, quality, safety, and revealing the health benefits of dairy products lipids. Advanced and effective methods of MS promote the in-depth study of gut microbiota and human metabolic disease risk and provide tangible solutions for further research in this field.

The presence of pathogenic Shiga toxin-producing Escherichia coli (STEC) in dairy products represents a public health concern because of its ability to produce the toxins Stx1 and Stx2, which cause intestinal diseases. Monitoring the stages of milk production and checking dairy products for contamination are crucial steps to ensure dairy safety. This study aimed to report the occurrence of thermotolerant coliforms, E. coli, and STEC strains in pasteurized dairy products and to evaluate the antibiotic resistance profiles, serotypes, and characterizations of the STEC isolates by pulsed-field gel electrophoresis. We obtained a total of 138 pasteurized dairy products from 15 processing plants in Bahia, Brazil, to examine coliforms, E. coli, and STEC strains. We found that 43% of samples (59/138) contained thermotolerant coliforms, and 30% (42/138) did not comply with Brazilian regulations. Overall, 6% (9/138) were positive for E. coli and 4% (5/138) were positive for STEC. We recovered 9 STEC isolates from pasteurized cream (2/9), Minas Padrão cheese (2/9), Minas Frescal cheese (4/9), and ricotta (1/9). All isolates were stx2-positive, and 2 were eae-positive. All isolates were negative for the \"big 6\" STEC serogroups, belonging instead to serotypes ONT:HNT, ONT:H12, O148:H-, OR:H40, OR:HNT, and O148:HNT. Pulsed-field gel electrophoresis revealed 100% genetic similarity among 3 isolates from 2 different samples produced in the same production facility, which may suggest cross-contamination. As well, we found isolates that were 98% similar but in samples produced in different production facilities, suggesting a mutual source of contamination or a circulating strain. Two STEC strains exhibited resistance to streptomycin. Although the isolates presented a low resistance profile and no strain belonged to the \"big 6\" pathogenic group, the circulation of stx2-positive STEC strains in ready-to-eat products highlights the importance of epidemiological surveillance inside the Brazilian dairy chain.

Milk and milk products play a vital role in diets around the globe. Due to their nutritional benefits there has been an increase in production and consumption over the past thirty years. For this growth to continue the safety and authenticity of dairy products needs to be maintained which is a huge area of concern. Throughout the process, from farm to processor, different sources of contamination (biological, chemical or physical) may occur either accidently or intentionally. Through online resources (the EU Rapid Alert System for Food and Feed (RASFF) and HorizonScan) safety and fraud data were collected from the past five years relating to milk and milk products. Cheese notifications were most frequently reported for both safety alerts (pathogenic micro-organisms) and fraud incidences (fraudulent documentation). Alongside the significant number of biological contaminations identified, chemical, physical and inadequate controls (in particular; foreign bodies, allergens, industrial contaminants and mycotoxins) were also found. Although the number of incidents were significantly smaller, these contaminants can still pose a significant risk to human health depending on their toxicity and exposure. Grey literature provided a summary of contamination and fraud issues from around the globe and shows its potential to be used alongside database resources for a holistic overview. In ensuring the integrity of milk during ever changing global factors (climate change, competition between food and feed and global pandemics) it is vital that safety and authenticity issues are continually monitored by industry, researchers and governing bodies.

BACKGROUND: Dairy safety has caused widespread concern in society. Unsafe dairy products have threatened people\'s health and lives. In order to improve the safety of dairy products and effectively prevent the occurrence of dairy insecurity, countries have established different prevention and control measures and safety warnings.
OBJECTIVE: The purpose of this study is to establish a dairy safety prediction model based on machine learning to determine whether the dairy products are qualified.
METHODS: The 34 common items in the dairy sampling inspection were used as features in this study. Feature selection was performed on the data to obtain a better subset of features, and different algorithms were applied to construct the classification model.
RESULTS: The results show that the prediction model constructed by using a subset of features including \"total plate\", \"water\" and \"nitrate\" is superior. The SN, SP and ACC of the model were 62.50%, 91.67% and 72.22%, respectively. It was found that the accuracy of the model established by the integrated algorithm is higher than that by the non-integrated algorithm.
CONCLUSIONS: This study provides a new method for assessing dairy safety. It helps to improve the quality of dairy products, ensure the safety of dairy products, and reduce the risk of dairy safety.