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优质pH探头:使用优质缓冲液和拉曼光谱进行非侵入式在线pH监测。

The Good pH probe: non-invasive pH in-line monitoring using Good buffers and Raman spectroscopy.

作者信息

Müller David Heinrich, Börger Marieke, Thien Julia, Koß Hans-Jürgen

机构信息

Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062, Aachen, Germany.

出版信息

Anal Bioanal Chem. 2023 Dec;415(29-30):7247-7258. doi: 10.1007/s00216-023-04993-0. Epub 2023 Nov 20.

DOI:10.1007/s00216-023-04993-0
PMID:37982845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10684429/
Abstract

In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state of the art. However, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines for the first time the Good buffer 3-(N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels. Thus, the Good pH probe extends the list of critical process parameters monitorable using Raman spectroscopy and IHM by the pH value.

摘要

在生物过程中,pH值是一个关键的过程参数,需要进行监测和控制。对于pH监测,诸如pH电极之类的电位测定方法是目前的技术水平。然而,它们具有侵入性且会出现测量值漂移。光谱pH监测是一种电位测定方法的非侵入性替代方法,可避免这种测量值漂移。在本研究中,我们开发了Good pH探针,这是一种用于生物过程中光谱pH监测的方法,其有效工作范围在pH 6至pH 8之间,且不需要估算活度系数。Good pH探针首次将作为pH指示剂的Good缓冲液3-(N-吗啉代)丙磺酸(MOPS)与作为光谱技术的拉曼光谱以及用于光谱评估的间接硬建模(IHM)相结合。在详细表征过程中,我们证明了Good pH探针是可逆的,在15至40°C之间不表现出温度依赖性,对高达1100 mM的离子强度敏感性较低,并且适用于其他成分会显著叠加MOPS光谱特征的更复杂系统。最后,Good pH探针在工业相关的酶催化反应中成功用于非侵入性pH在线监测,预测均方根误差(RMSEP)为0.04 pH单位。因此,Good pH探针通过pH值扩展了使用拉曼光谱和IHM可监测的关键过程参数列表。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/ba918a264f97/216_2023_4993_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/fa1b916b7db9/216_2023_4993_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/de605f2d8256/216_2023_4993_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/73d7abf01d4b/216_2023_4993_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/b81138109e9b/216_2023_4993_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/ba918a264f97/216_2023_4993_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/de032ee7b0f2/216_2023_4993_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/17adc92d8bf6/216_2023_4993_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/6bf95ece29e2/216_2023_4993_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/27338f031975/216_2023_4993_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/fa1b916b7db9/216_2023_4993_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/de605f2d8256/216_2023_4993_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/73d7abf01d4b/216_2023_4993_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/b81138109e9b/216_2023_4993_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/339f202c610b/216_2023_4993_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/30ff363c1257/216_2023_4993_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29e6/10684429/ba918a264f97/216_2023_4993_Fig11_HTML.jpg

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