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相关概念视频

High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

556
High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
556
High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

309
In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
309
High-Performance Liquid Chromatography: Instrumentation00:57

High-Performance Liquid Chromatography: Instrumentation

487
High-performance liquid chromatography, or HPLC, is an analytical technique that separates liquid samples under high pressures. An HPLC instrument consists of glass bottles for storing solvents called mobile phase reservoirs. HPLC-grade solvents are used to maintain high purity, and the dissolved gases are removed using a degasser, such as a vacuum pumping system or sparging with helium. The solvents are then pumped into the analytical column using a screw-driven syringe or reciprocating pumps.
487

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相关实验视频

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Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
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通过考虑预测不确定性分析,为疏水性相互作用色谱开发基于模型的工艺.

Yu-Xiang Yang1, Shan-Jing Yao1, Dong-Qiang Lin1

  • 1Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.

Journal of chromatography. A
|April 29, 2025
PubMed
概括

贝叶斯推理量化了疏水相互作用色谱 (HIC) 模型预测中的不确定性,揭示了预测和实验产量之间的差异. 将这种不确定性分析集成到流程优化中,导致了更可靠的分离条件和更好的产品质量.

关键词:
贝叶斯的推理 贝叶斯的推理疏水性相互作用色谱学 疏水性相互作用色谱学机械模型是机械模型.不确定性量化不确定性的量化.

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科学领域:

  • 生物技术是生物技术.
  • 化学工程是化学工程的重要组成部分.
  • 计算建模 计算建模

背景情况:

  • 机械模型对于疏水性相互作用色谱 (HIC) 工艺开发至关重要.
  • 参数估计可以校准模型,但固有的偏差限制了预测准确度.
  • 预测 (97.3%) 和实验 (86.0%) 收益之间的差异突出了模型的局限性.

研究的目的:

  • 为了解决HIC过程优化中的模型预测偏差.
  • 量化模型参数和预测中的不确定性.
  • 开发一个风险回避,不确定性知情的流程开发框架.

主要方法:

  • 使用马尔科夫链蒙特卡洛 (MCMC) 的贝叶斯推理来计算参数不确定性.
  • 将参数不确定性转化为模型预测不确定性.
  • 将不确定性分析集成到HIC流程优化中.

主要成果:

  • 在一个精心校准的HIC模型中发现了显著的收益差异.
  • 量化模型预测的收益率不确定性 (76.9%96.5%),与实验观测一致.
  • 重新优化过程实现了收益率不确定性较小 (94.2%98.9%) 和高实验收益率 (95.8%).

结论:

  • 在HIC模型中,不确定性量化提高了流程优化的可靠性.
  • 这种方法有助于反映模型预测偏差并减少开发风险.
  • 提出的框架提高了模型预测的准确性,并最大限度地降低了基于模型的过程开发中的风险.