Rapid detection of food microorganisms from the perspective of cellular and molecular biomechanics leveraging biotechnology and computer vision

Food microbiological detection is an important part of food safety management. Understanding the behavior and characteristics of microorganisms at the cellular and molecular level can enhance the detection process. To quickly and effectively detect food microorganisms, a rapid microorganisms detection method based on biotechnology and computer vision is proposed. Firstly, food bacterial strains are cultivated based on biotechnology and sample data is prepared. At the cellular level, this involves understanding the growth kinetics and metabolic processes of the microorganisms. Secondly, a microorganisms classification detection model is proposed based on residual neural networks, and transfer learning and attention mechanisms are introduced to optimize the model. By mimicking the way cells and molecules interact and signal, these techniques can help the model better recognize and classify different microbial species. Considering the problem of insufficient detection of large-scale complex scenes, an improved object detection model is proposed, which introduces a lightweight model to replace the backbone feature network and uses deep separation convolution to replace ordinary convolution, thereby raising the training accuracy of the model. In the classification model experiment, the research model shows better model loss performance and microorganisms detection accuracy in both low-density and high-density microbial scenarios. In the analysis of object detection models, compared to other models, the research model has smaller losses. In large-scale scenes and multi-feature large-scale scenes, the research model has losses of 0.12 and 3.56, respectively, which are better than other models. In addition, in common microorganisms detection, the high accuracy of 99.75% for detecting Escherichia coli indicates the model's proficiency in recognizing the specific cellular and molecular characteristics of this microorganism, providing significant technical references for ensuring food safety and efficient microorganism detection from a cellular and molecular biomechanics perspective.