• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Engineering in Life Sciences >Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations
【2h】

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

机译：基于2D荧光和过程数据的软传感器可实时估算大肠杆菌培养物中的生物量

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.
机译：在生物过程中，特定的过程响应（例如生物量）通常无法直接在线测量，因为分析采样会不可避免地导致时间延迟。通过设计和过程分析技术概念，实时访问这些响应对于质量至关重要。软传感器通过使用先前导出的模型和实时实际过程数据间接测量目标变量来克服这些限制。在这项研究中，开发了基于二维荧光数据和过程数据的生物质软传感器，用于20升实验设计的综合研究，用于分批分批培养。将多元自适应回归样条算法应用于二维荧光光谱和过程数据，以估计过程中任何时间的生物量浓度。验证的预测误差为4.9％（0.99 g / L），新数据（外部验证）的预测误差为3.8％（0.69 g / L）。使用2D荧光数据的主成分和平行因子分析，鉴定了两种潜在的化学化合物，它们直接与细胞代谢有关。相同的波长对也是回归模型性能的重要预测指标。总体而言，建议的软传感器是用于在线监视过程性能，通过设计实现质量的宝贵工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号