Enhancing Raw Bovine Milk Quality using Ultraviolet-C (UV-C) Irradiation: A Microbial and Lipid Peroxidation Study.

This study investigated the efficacy of ultraviolet-C (UV-C) irradiation in enhancing the quality of raw bovine milk by targeting microbial populations and lipid peroxidation, both of which are key factors in milk spoilage. We categorized the raw milk samples into three groups based on initial bacterial load: low (<3 Log 10 CFU/mL), medium (3-4 Log 10 CFU/mL), and high (>4 Log 10 CFU/mL). Using a 144 W thin-film UV-C reactor, we treated the milk with a flow rate of 3 L/min. We measured the bacterial count including standard plate count, coliform count, coagulase-negative staphylococci count, and lactic acid bacteria count and lipid peroxidation (via thiobarbituric acid reactive substances assay) pre- and post-treatment. Our results show that UV-C treatment significantly reduced bacterial counts, with the most notable reductions observed in high and medium initial load samples (>4 and 3-4 Log 10 CFU/mL, respectively). The treatment was particularly effective against coliforms, showing higher reduction efficiency compared to coagulase-negative staphylococci and lactic acid bacteria. Notably, lipid peroxidation in UV-C treated milk was significantly lower than in pasteurized or untreated milk, even after 72 hours. These findings demonstrate the potential of UV-C irradiation as a pre-treatment method for raw milk, offering substantial reduction in microbial content and prevention of lipid peroxidation, thereby enhancing milk quality.

Research on Shelf-Life Extension Technologies for Food Sustainability: An Assessment of Scientific Activities and Networks.

A clearer understanding of research streams and players involved in efforts to address the sustainability of global food and agricultural systems is needed to clarify the current state of scientific knowledge and form collaborations to pursue future research directions. This study presents new insights into this issue through a scientometric process involving a case study of technologies for extending fruit shelf-life. The text mining software was utilized to analyze 3,131 Web of Science-indexed articles published between 2000 and 2020 as a means to glean the conceptual structure of current knowledge and conduct a social network analysis to explore scientific and publication activity. The findings were mapped onto a strategic diagram of research productivity and collaboration between players at the national, organizational, and individual levels. This research's main findings highlight that research on shelf-life technology is in continuous development, and academic institutions from China, Spain, and the U.S. are the core national players in this field. The results provide insights for further investigation to strengthen co-research and technological development programs in other fields. Researchers who are exploring networking opportunities can use the model and process presented as a guideline for identifying emerging and future research trends and formulating strategies.

Collaboration Network and Trends of Global Coronavirus Disease Research: A Scientometric Analysis.

As a global pandemic threatens health and livelihoods, finding effective treatments has become a vital issue that requires worldwide collaboration. This study examines research collaboration and network profiles through a case study of coronavirus diseases, including both the extinct severe acute respiratory syndrome coronavirus (SARS-CoV) and the emerging species (SARS-CoV-2). A scientometric process was designed to apply quantitative tools and a qualitative approach employing technological expertise to accomplish a three-level collaboration analysis. The text mining software, VantagePoint, was used to analyze research articles from the Web of Science database to identify the key national, organizational, and individual players in the coronavirus research field combined with indicators, namely, the breadth and depth of collaboration. The results show that China and the United States are at the center of coronavirus research networks at all three levels, including many endeavors involving single or joint entities. This study demonstrates how governments, public sectors, and private sectors, such as the pharmaceutical industry, can use scientometric analysis to gain insight into the holistic research trends and networks of players in this field, leading to the formulation of strategies to strengthen research and development programs. Furthermore, this approach can be utilized as a visualization and decision support tool for further policy planning, identification and execution of collaboration, and research exchange opportunities. This scientometric process should be directly applicable to other fields.

Intention to adopt and diffuse innovative ultraviolet light C system to control the growth of microorganisms in raw milk among Thais Dairy Farmers.

This research aims to investigate the factors affecting Thai dairy farmers' intention to use ultraviolet C technology (UV-C) to reduce the number of microorganisms in raw milk. The researcher applied the innovation and diffusion theory (IDT) and the technology acceptance model 2 (TAM2) to evaluate dairy farmer's intention. The 477 dairy farmers in the central part of Thailand were interviewed using questionnaires. The collected data were analyzed using multinomial logistic regression (MLR). It was found that the intention to adopt UV-C can accurately predict the factors affecting the intention to use the technology based on the intention levels to create diffusion and promote acceptance. To increase the diffusion and acceptance of UV-C, the perceived usefulness to increase the milk price was the major influenced factor to promote technology acceptance. Providing facilities for maintenance, the use of this innovation and reporting news about the benefits of new technology to create positive attitudes and confidence lead to the spread of technology driven by early adopters. The technology developers should consider and design technology compatible with existing systems. However, the collaboration of all related parties is also needed to encourage Thai dairy farmers to try the new technology and observe the actual results.