Food safety has always been one of the focus of social concern, and elemental analysis of food testing laboratory as an important means to ensure product safety, its quality control is crucial. This paper first introduces the key role of food laboratory element analysis in ensuring product safety and the main ways of food testing laboratory element analysis. Then, the paper focuses on the problems in quality control and puts forward a series of effective quality control strategies. It is hoped to provide useful guidance for the quality control of elemental analysis in food testing laboratories to ensure food safety and public health.

Objective Therefore, the purpose of this study is to verify and optimize the microbial contamination control strategies during the production of sterile injections to ensure product safety and improve production quality. Methods A randomized trial design was conducted between January 2022 and December 2023, a traditional control strategy between January 2022 and December 2022, and a post-optimized strategy between January 2023 and December 2023. By comparing the microbial pollution under different strategies, combining with the production environment monitoring, process process analysis and operator behavior code assessment, the effectiveness of the control strategy is comprehensively evaluated, and the strategy is optimized. Results The study found that the optimized strategy greatly reduced the risk of microbial contamination and improved production efficiency and product quality. Conclusion The validation and optimized microbial contamination control strategy has significant results in the production of sterile injection, which provides a strong guarantee to ensure the sterility and safety of the injection products. In the future, the production process will be continuously monitored, and the control strategy will be dynamically adjusted according to the actual situation to meet the changing production environment and regulatory requirements.

With the rapid development of digital technology, digital in the field of education online learning platform, manufacturing factory automation, financial industry mobile payment and other industries widely and far-reaching application. The digital transformation and upgrading of university laboratory asset management can realize real-time tracking and monitoring of assets, automated management processes, data analysis and optimization of decision-making, improve security and accuracy, and provide convenient query and traceability functions, improve management efficiency, reduce costs, and promote the compliance and efficient circulation and use of data assets. This paper analyzes the current situation of laboratory assets management in universities, discusses the significance and challenges of the digital transformation of laboratory assets management in the new era, and puts forward specific countermeasures for the digital transformation of laboratory assets management in universities.

Traditional textile testing methods have problems of subjectivity and inefficiency, and the testing method based on artificial intelligence technology provides a new way to solve these problems. This paper first summarizes the traditional textile inspection methods, including visual inspection, manual inspection and traditional machine vision inspection technology. Then, the textile testing and analysis methods based on artificial intelligence technology are introduced, including data acquisition and preprocessing, feature extraction and selection, testing model design and training, and testing result analysis and evaluation. Through the application of artificial intelligence technology, the automation, efficiency and accuracy of the textile testing process can be achieved, which brings new possibilities for the quality control and production optimization of the textile industry. Experimental results show that the detection accuracy of the proposed method is improved by 23.5% compared with the traditional method. Therefore, the textile testing method based on artificial intelligence technology can be widely promoted and applied in practice.

With the rapid development of medical science, laboratories in medical departments of higher education institutions have become the core areas for innovation and teaching. The precise research equipment in the laboratory is the cornerstone for conducting cutting-edge research and medical experimental teaching. However, the complexity, high value, and rapid iterative updates of laboratory equipment require a more refined management model to ensure optimal instrument performance and extend its lifespan. In the face of increasingly fierce international competition, efficient management of laboratory instruments is particularly important: it directly affects the progress and quality of laboratory research, as well as the academic status and educational quality of the entire department.

Objective To study error compensation techniques in high-precision pressure detection systems and improve measurement accuracy. Methods A comprehensive error compensation method based on neural networks was proposed, and a pressure sensor error model was established. An adaptive neural network compensator was designed. Results This method achieves effective compensation for nonlinear errors, and experimental results show that the systematic nonlinear error decreases from 0.25% FS to 0.015% FS and the maximum relative error from 0.38% to 0.022%. After temperature compensation, the temperature coefficient decreased to${0.002}\%\mathrm{{FS}}/{}^{\circ}\mathrm{C}$, which increased by${80}\%$compared to$\pm {0.01}\%$FS/${}^{\circ}\mathrm{C}$before compensation. The dynamic response time is in the${1.7}\sim {2.0}\mathrm{\;{ms}}$range, with a$-3\mathrm{\;{dB}}$bandwidth of${850}\sim {870}\mathrm{\;{Hz}}$. Conclusion The error compensation method proposed in this paper can effectively improve the measurement accuracy of pressure detection systems and provide new ideas for the development of high-precision pressure detection technology.

In the process of production and inspection of biological products, a large amount of relevant data will be generated, but there are problems such as low digital management and inconvenient work in the data processing process. This paper constructed a trend analysis system based on the characteristics of trend analysis in the quality control of biological products. Based on the research and development of laboratory information management system (LIMS) and FineReport (FR), the system integrated the relevant technologies of instrument data acquisition and inspection data cleaning, used the shewhart control chart to complete the collection, cleaning and summary of the detection data, and provided statistics, analysis and visual monitoring, alarm and other functions to improve the quality and efficiency of the trend analysis of biological products. The stability and variability in production and testing were tracked to provide reference for the scientific management of biological products.

With the increasingly serious environmental pollution, food may be affected by various new pollutants, such as microplastics and heavy metals. Therefore, it is necessary to continuously study the detection methods of new pollutants to ensure the overall safety of food. This article makes a deep thinking and innovative discussion on food testing work under the background of food safety.

In the era of rapid development of information technology, more and more research and development enterprises are seeking innovative research and development models, learning advanced research and development concepts, and achieving the transformation of traditional research and development to digital research and development. This article took Xin'an Chemical Group as an example to share practical experience and introduce the research and development model based on integrated product development (IPD) as the concept, combined with the construction of product lifecycle management (PLM) scientific research information management system, to explore and achieve the process of enterprise digital transformation, improved enterprise research and development level, and enhanced core competitiveness, so as to provide reference for the development of related enterprises.

Large-scale scientific instruments sharing-platform is an important public platform of scientific research, talent training, and social service of the universities. It is also important component of "double first-class" construction in the university. It has analyzed and summarized the equipment purchase and technical teams' construction of the large-scale scientific instruments sharing-platform of School of Physics science and Technology, Lanzhou University in this paper. And the safety management of scientific laboratory, the standardized operation and sharing of instruments have been introduced. Furthermore the construction scheme of outstanding large-scale scientific instruments sharing-platform was discussed, which has been to offer direction of improving the management level and enhancing the efficiency of large-scale instruments platform in the university.