Assessment of near‐infrared spectral information for rapid monitoring of bioprocess quality

Abstract

Access to real-time process information is desirable for consistent and efficient operation of bioprocesses. Near-infrared spectroscopy (NIRS) is known to have potential for providing real-time information on the quantitative levels of important bioprocess variables. However, given the fact that a typical NIR spectrum encompasses information regarding almost all the constituents of the sample matrix, there are few case studies that have investigated the spectral details for applications in bioprocess quality assessment or qualitative bioprocess monitoring. Such information would be invaluable in providing operator-level assistance on the progress of a bioprocess in industrial-scale productions. We investigated this aspect and report the results of our investigation. Near-infrared spectral information derived from scanning unprocessed culture fluid (broth) samples from a complex antibiotic production process was assessed for a data set that incorporated bioprocess variations. Principal component analysis was applied to the spectral data and the loadings and scores of the principal components studied. Changes in the spectral information that corresponded to variations in the bioprocess could be deciphered. Despite the complexity of the matrix, near-infrared spectra of the culture broth are shown to have valuable information that can be deconvoluted with the help of factor analysis techniques such as principal component analysis (PCA). Although complex to interpret, the loadings and score plots are shown to offer potential in process diagnosis that could be of value in the rapid assessment of process quality, and in data assessment prior to quantitative model development.

Study of pellet formation of filamentous fungi Rhizopus oryzae using a multiple logistic regression model

“…For example, Vaidyanathan et al (2000) reported that using a polymertype supportive medium (PVP) to immobilize R. oryzae cells resulted in a LA yield of 71% and productivity 1.8 times higher than free filamentous mycelia. From a fermentation condition control perspective, there are many factors influencing the fungal pellet formation, such as nutrients, pH of medium, agitation, aeration, inoculum level, substrate concentration, polymer additives, viscosity of the medium, surface-active agents, etc.…”

Use of at‐line and in‐situ near‐infrared spectroscopy to monitor biomass in an industrial fed‐batch Escherichia coli process

“…The region was manually chosen based on the chemistry of the system rather than allowing the computer to simply build a model with no analytical basis. The purpose of this article is to discuss the route from at-line to in-situ application of NIRS and simply present a successful model rather than discuss the different models and critically evaluate their performance relative to their complexity, since this topic has been addressed in the recent publications by Vaidyanathan et al (2000Vaidyanathan et al ( , 2001.…”

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications

“…It has been shown that multivariate data analysis (MVDA) using projection methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and multiple regression can overcome challenges associated with biotech applications such as multidimensionality of the dataset, missing data and variation introduced by disturbing factors such as experimental error and noise (Johnson et al, 2007;Kourti, 2004;Martin and Morris, 2002). PCA has been applied to near-infrared spectral information derived from scanning unprocessed culture fluid samples from a complex antibiotic production process (Vaidyanathan et al, 2001). It was shown that changes in the spectral information that correspond to variations in the bioprocess can be identified and that the loadings and score plots can assist with process diagnosis and rapid assessment of process performance.…”