摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-目前关于食品安全的研究结果已经发表。根据News Rx记者在尼日利亚奥博莫绍的新闻报道，研究表明：“预测微生物学是一个快速发展的领域，多年来由于其在食品安全中的广泛应用而引起了人们的极大兴趣。预测模型被广泛应用于食品微生物学中，以估计食品中微生物的生长。”新闻编辑们引用了Helix Biogen Institute的一篇研究文章：“这些模型将食物内在因素和外在因素之间的动态相互作用表示为数学方程，然后应用这些数据来预测食物的寿命、腐败和微生物风险评估。由于它们能够预测微生物风险，这些工具也被整合到危险分析临界控制点(HACCP)协议中。然而，与大多数新技术一样，它们的使用也受到了严重的限制。人们发现预测模型无法模拟动态环境下不同菌群在食物中复杂的微生物相互作用。为了解决这个问题，研究人员正在将几种新技术整合到预测模型中，以提高效率和准确性。越来越多的新技术如全基因组测序(WGS)、宏基因组学、人工智能、机器学习正迅速被采用到新一代模型中。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on food safety h ave been published. According to news reporting from Ogbomosho, Nigeria, by News Rx journalists, research stated, “Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse app lication in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products.” The news editors obtained a quote from the research from Helix Biogen Institute: “These models represent the dynamic interactions between intrinsic and extrinsi c food factors as mathematical equations and then apply these data to predict sh elf life, spoilage, and microbial risk assessment. Due to their ability to predi ct the microbial risk, these tools are also integrated into hazard analysis crit ical control point (HACCP) protocols. However, like most new technologies, sever al limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To addres s this issue, researchers are integrating several new technologies into predicti ve models to improve efficiency and accuracy. Increasingly, newer technologies s uch as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models.”
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