分光光度法技术:生物过程监测的通用工具

Chandni Chandarana, Jyoti Suthar, Aman Goyel
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引用次数: 0

摘要

通过分析光谱方法对生物过程进行在线分析,以产生快速的样品分析。生物转化是由连续过程直接控制的,它改善了质量管理。已经报道了各种在线分析方法。本文主要综述了红外[NIR和MIR]的应用;荧光;紫外光谱和拉曼光谱用于生物过程的在线监测。紫外-可见光谱法在生物过程监测中的应用,用于测量样品中存在的不同化学化合物。对蛋白质和其他大分子的测量,其中光被功能基团的分子吸收,产生非特异性紫外-可见光谱。拉曼光谱支持MIR,产生不同的强度和选择性。拉曼测量来自单色辐射源的非弹性散射。结合化学计量模型用于大肠杆菌培养,可提高荧光光谱监测和荧光自动化程度。光谱学方法在生物过程分析中的应用产生了复杂的光谱。所讨论的方法产生的数据集重叠光谱的所有这些成分,这需要多元数据分析方法,如偏最小二乘(PLS),回归或主成分回归的数据分析,也使用校准数据集和化学计量算法,这超出了审查的范围。
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Spectrophotometric Techniques: A Versatile Tool for Bioprocess Monitoring
Online analysis of bioprocesses by analytical spectroscopic methods is used to produce fast sample analysis. Bio-transformations are directly controlled by continuous process It improves management of Quality. Various methods for online analysis have been reported. This review article majorly covers applications for infrared [NIR and MIR]; Fluorescence; Ultraviolet [UV] Spectroscopy and Raman Spectroscopy for online monitoring of bioprocesses. The use of Uv- Vis spectroscopy in bioprocess monitoring to measure different chemicals compound present in sample. The measurement of proteins and other large molecule, where light is absorbed by functional group of molecules, resulting in non-specific uv-vis spectra. Raman spectroscopy is supportive to MIR, yielding different intensities and selectivity. Raman measures inelastic scattering from a monochromatic radiation source. Fluorescence spectroscopy monitoring and automation of fluorescence can be improved by using in combination with chemometric model for cultivation of e-coli. The application of spectroscopic methods for the analysis of bioprocess result in complex spectra. The methods under discussion produce datasets which overlapping spectra for all of these components which requires multivariate data analysis method, such as Partial least square (PLS), regression or principal component regression for data analysis also the use of calibration dataset and chemometric algorithms which is beyond the scope of review.
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