Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy

The use of near infrared spectroscopy (NIRS) was investigated in the context of an efficient high cell density fed-batch industrial Pichia pastoris bioprocess for the production of a therapeutic mammalian protein. This process represented a considerable challenge from the viewpoint of using NIRS to model key analytes because it involved two carbon sources (glycerol and methanol) added at differing rates and times, used a chemically complex medium, and showed a change in liquid phase behaviour due to cell growth. Models for biomass, glycerol, methanol and product were constructed. Different methods of spectral collection and mathematical procedures were used relative to which analyte in the fermentation matrix was being modelled and the rationale behind the model building is clearly described. Regardless of the mode of spectral collection it was essential to consider the changes in modelled analyte concentration relative to changes in other spectral contributors (analytes). The study considerably extends the use of NIRS in fermentation processes to high cell density complex industrial production processes, and comments on how this further developments the technology towards routine in situ NIRS monitoring of bioprocesses.     

At-line monitoring of a submerged filamentous bacterial cultivation using near-infrared spectroscopy

The use of near infra-red spectroscopy (NIRS) to monitor a submerged filamentous bacterial bioprocess was investigated. An industrial strain of the filamentous bacterium Streptomyces fradiae was cultured in a 12 litre stirred tank reactor (STR) using a complex medium. This mycelial 4 phase (oil, water, gas and solid) system produced highly complex and variable matrices, therefore monitoring such a complex fluid with NIRS represented a considerable challenge. Nevertheless, successful models for four key analytes (methyl oleate, glucose, glutamate and ammonium) were built at-line (rapid off-line) using NIRS. In the present study, the methods used to formulate, select and validate the models for the key analytes are discussed, with particular emphasis on how the model performance can be critically evaluated. Since previous reports on NIRS in monitoring bioprocesses have either involved simpler matrices, or, in filamentous systems, have not discussed how NIRS models can be critically assessed, the emphasis in the present study on providing an insight into the modelling process in such a complex matrix, may be particularly important to the applicability of NIRS to such industrial bioprocesses.     

Temporally segmented modelling: a route to improved bioprocess monitoring using near infrared spectroscopy?

Near infrared spectroscopy (NIRS) was used to monitor an industrial bioprocess for the production of the antibiotic, tylosin, using a segmented modelling approach. Models were built over the entire time course of the fermentation from 0 to 150 h, and also in two distinct phases or segments of the bioprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationary phase). All models were validated externally and the performance of the full range and segmented models compared. The standard error of prediction (SEP) of the segmented models was less in both 50–100 h and 100–150 h and the correlation highest in the 50–100 h range. This would suggest that data segmentation is potentially a useful method of accommodating the impact of the pronounced matrix changes which occur in some bioprocesses in NIRS models for key analytes. While there are many reports on bioprocess monitoring using NIRS, there have been no previous studies on the use of segmented NIR models within a bioprocess as a means of accommodating matrix change.     

Fermentation process tracking through enhanced spectral calibration modeling

The FDA process analytical technology (PAT) initiative will materialize in a significant increase in the number of installations of spectroscopic instrumentation. However, to attain the greatest benefit from the data generated, there is a need for calibration procedures that extract the maximum information content. For example, in fermentation processes, the interpretation of the resulting spectra is challenging as a consequence of the large number of wavelengths recorded, the underlying correlation structure that is evident between the wavelengths and the impact of the measurement environment. Approaches to the development of calibration models have been based on the application of partial least squares (PLS) either to the full spectral signature or to a subset of wavelengths. This paper presents a new approach to calibration modeling that combines a wavelength selection procedure, spectral window selection (SWS), where windows of wavelengths are automatically selected which are subsequently used as the basis of the calibration model. However, due to the non-uniqueness of the windows selected when the algorithm is executed repeatedly, multiple models are constructed and these are then combined using stacking thereby increasing the robustness of the final calibration model. The methodology is applied to data generated during the monitoring of broth concentrations in an industrial fermentation process from on-line near-infrared (NIR) and mid-infrared (MIR) spectrometers. It is shown that the proposed calibration modeling procedure outperforms traditional calibration procedures, as well as enabling the identification of the critical regions of the spectra with regard to the fermentation process.     

At-line near-infrared spectroscopy for monitoring concentrations in temperature-triggered glutamate fermentation

Rapid development in the glutamate fermentation industry has dictated the need for effective fermentation monitoring by rapid and precise methods that provide real-time information for quality control of the end-product. In recent years, near-infrared (NIR) spectroscopy and multivariate calibration have been developed as fast, inexpensive, non-destructive and environmentally safe techniques for industrial applications. The purpose of this study was to develop models for monitoring glutamate, glucose, lactate and alanine concentrations in the temperature-triggered process of glutamate fermentation. NIR measurements of eight batches of samples were analyzed by partial least-squares regression with several spectral pre-processing methods. The coefficient of determination (R ²), model root-mean square error of calibration (RMSEC), root-mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the test calibration for the glutamate concentration were 0.997, 3.11 g/L, 2.56 g/L and 19.81, respectively. For the glucose concentration, R ², RMSEC, RMSEP and RPD were 0.989, 1.37 g/L, 1.29 g/L and 9.72, respectively. For the lactate concentration, R ², RMSEC, RMSEP and RPD were 0.975, 0.078 g/L, 0.062 g/L and 6.29, respectively. For the alanine concentration, R ², RMSEC, RMSEP and RPD were 0.964, 0.213 g/L, 0.243 g/L and 5.29, respectively. New batch fermentation as an external validation was used to check the models, and the results suggested that the predictive capacity of the models for the glutamate fermentation process was good.     

Quantifying species composition in root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers

Understanding of plant interactions is greatly limited by our ability to identify and quantify roots belonging to different species. We proposed and compared two methods for estimating the root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial root mixtures composed of two or three herbaceous species were prepared. The proportion of root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and actual root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.     

In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

The use of in-situ near infrared spectroscopy (NIRS) as a tool for monitoring four key analytes in a CHO-K1 animal cell culture was investigated. Previous work using on-line NIRS to monitor bioprocesses has involved its application ex-situ where the analyzer is physically outside the fermentor, or to microbial bioprocesses. This novel application of NIRS to monitor analytes within an animal cell culture using a steam sterilizable in-situ fiber optic probe is very important for furthering the use of NIRS within the bioprocessing industry. The method of calibration used to develop the models involved the use of large data sets so that all likely variation in stoichiometry was incorporated within the models. Successful models for glucose, lactate, glutamine, and ammonia were built with Standard Error of Predictions (SEP's) of 0.072 (g/L), 0.0144 (g/L), 0.308 (mM), and 0.036 (mM), respectively of the total concentration range.     

Critical evaluation of models developed for monitoring an industrial submerged bioprocess for antibiotic production using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is known to have potential for cost-effective monitoring of bioprocesses. Although this has been demonstrated in many instances and several models have been reported, information regarding the complexity of models required and their utility over extended periods of time is lacking. In the present study, the complexity of the models required for the NIRS prediction of substrate (oil) and product (tylosin) concentration in an industrial bioprocess that employs a physicochemically heterogeneous medium for antibiotic production was assessed. Measurements made by both the diffuse reflectance and transmittance modes were investigated. SEP values for the prediction of the analytes averaged 5% or less, for the successful models, when evaluated using an external validation set, 2 years after the initial model development exercise. Diffuse reflectance measurements showed poorer results, compared to transmittance measurements, especially for monitoring tylosin. In general, this investigation provides evidence to support the fact that models built for the prediction of analytes in a commercial bioprocess that employs a physicochemically complex production medium can be robust in performance over an extended period of time and that simple models based on fewer terms or latent variables can perform well, even in the context of matrices that are relatively complex. It also indicates that sample presentation is likely to be a critical factor in the successful application of NIRS in bioprocess monitoring, which merits further detailed investigation.     

Real‐time monitoring of high‐gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real‐time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm^?1 C–C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high‐ and low‐concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two‐set calibration models for starch (R² = 0.998, percent error = 2.5%) and ethanol (R² = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS‐based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman‐based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Biotechnol. Bioeng. 2013; 110: 1654–1662. © 2013 Wiley Periodicals, Inc.     

Applying near-infrared spectroscopy in downstream processing: one calibration for multiple clarification processes of fermentation media

The use of near-infrared spectroscopy (NIRS) is demonstrated in the first downstream processing (DSP) steps of an active pharmaceutical ingredient (API) manufacturing process. The first method developed was designed to assess the API content in the filtrate stream (aqueous) of a rotary drum vacuum filter. The PLS method, built after spectral preprocessing and variable selection, had an accuracy of 0.01% (w/w) for an API operational range between 0.20 and 0.45% (w/w). The robustness and extrapolation ability of the calibration was proved when samples from ultrafiltration and nanofiltration processes, ranging from 0 to 2% (w/w), were linearly predicted (R2=0.99). The development of a robust calibration model is generally a very time-consuming task, and once established it is imperative that it can be useful for a long period of time. This work demonstrates that NIR procedures, when carefully developed, can be used in different process conditions and even in different process steps of similar unit operations.     

Valorization of onion waste and by-products: MCR-ALS applied to reveal the compositional profiles of alcoholic fermentations of onion juice monitored by near-infrared spectroscopy

The overall purpose of the project, of which this study is a part, was to examine the feasibility of onion waste as a support-substrate for the profitable production of food-grade products. This study focused on the efficient production of ethanol from worthless onions by transforming the onion juice into onion liquor via alcoholic fermentation with the yeast Saccharomyces cerevisiae. The onion bioethanol produced could be later used as a favorable substrate for acetic fermentation to finally obtain onion vinegar. Near-infrared spectroscopy (NIRS), coupled with the multivariate curve resolution-alternating least squares (MCR-ALS) method, has been used to reveal the compositional and spectral profiles for both substrates and products of alcoholic fermentation runs, that is, total sugars, ethanol, and biomass concentration. The ambiguity associated with the ALS calculation was resolved by applying suitable inequality and equality constraints. The quality of the results provided by the NIR-based MCR-ALS methodology adopted was evaluated by several performance indicators, including the variance explained by the model, the lack of fit and the agreement between the MCR-ALS achieved solution and the results computed by applying previously validated PLS reference models. An additional fermentation run was employed to test the actual predictive ability of the ALS model developed. For all the components resolved in the fermentation system studied (i.e., total sugars, ethanol, and biomass), the final model obtained showed a high predictive ability and suitable accuracy and precision, both in calibration and external validation, confirmed by the very good agreement between the ALS responses and the reference values (the coefficient of determination was, in all cases, very close to 1, and the statistics confirmed that no significant difference was found between PLS reference models and the MCR-ALS methodology applied). Thus, the proven reliability of the MCR-ALS model presented in this study, based only on NIR measurements, makes it suitable for monitoring of the key species involved in the alcoholic fermentation of onion juice, allowing the process to be modeled and controlled in real time.     

Methodology for real-time,multianalyte monitoring of fermentations using an in-situ mid-infrared sensor

An in-situ, mid-infrared sensor was used to monitor the major analyte concentrations involved in the cultivation of Gluconacetobacter xylinus and the production of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concentrations, three different sets of standard spectra were used to develop calibration models, applying partial least-squares regression. It was possible to build a valid calibration model to predict the 700 spectra collected during the complete time course of the cultivation, using only 12 spectra collected every 10 h as standards. This model was used to reprocess the concentration profiles from 0 to 15 g/L of nine different analytes with a mean standard error of validation of 0.23 g/L. However, this calibration model was not suitable for real-time monitoring as it was probably based on non-specific spectral features, which were correlated only with the measured analyte concentrations. Valid calibration models capable of real-time monitoring could be established by supplementing the set of 12 fermentation spectra with 42 standards of measured analytes. A pulse of 5 g/L ethanol showed the robustness of the model to sudden disturbances. The prediction of the models drifted, however, toward the end of the fermentation. The most robust calibration model was finally obtained by the addition of 34 standard spectra of non-measured analytes. Although the spectra did not contain analyte-specific information, it was believed that this addition would increase the variability space of the calibration model. Therefore, an expanded calibration model containing 88 spectra was used to monitor, in real time, the concentration profiles of fructose, acetic acid, ethanol and gluconacetan and allowed standard errors of prediction of 1.11, 0.37, 0.22, and 0.79 g/L, respectively.     

拉曼光谱分析技术在资源微生物发酵性能评价中的应用

微生物发酵过程是细胞新陈代谢进行物质转化的过程,为了提高目标产物的转化率,需要对微生物发酵动态特性进行实时分析,以便实时优化发酵过程。拉曼光谱(Raman spectroscopy)量化测试作为一种有应用前景的在线过程分析技术,可以在避免微生物污染的条件下,实现精准监测,进而用于优化控制微生物发酵过程。【目的】以运动发酵单胞菌(Zymomonas mobilis)为例,建立微生物发酵过程中葡萄糖、木糖、乙醇和乳酸浓度拉曼光谱预测模型,并进行准确性验证。【方法】采用浸入式在线拉曼探头,收集运动发酵单胞菌发酵过程中多个组分的拉曼光谱,采用偏最小二乘法对光谱信号进行预处理和多元数据分析,结合离线色谱分析数据,对拉曼光谱进行建模分析和浓度预测。【结果】针对运动发酵单胞菌,首先实现拉曼分析仪对单一产品乙醇发酵过程的精准检测,其次基于多元变量分析,建立葡萄糖、乙醇和乳酸浓度变化的预测模型,实现对发酵过程中各成分浓度变化的准确有效分析。【结论】成功建立了一种评价资源微生物尤其是工业菌株发酵液多种组分的拉曼光谱分析方法。该方法为运动发酵单胞菌等工业菌株利用多组分底物工业化生产不同产物的实时检测,以及其他微生物尤其工业菌株的选育和过程优化提供了新方法。     

Effects of preculture variability on clavulanic acid fermentation.

The production profile of clavulanic acid by Streptomyces clavuligerus was shown to be strongly dependent on inoculum activity. Two sets of fermentations (A and B) were investigated at industrial pilot-plant scale using complex media. Type A fermentations were inoculated using late exponential growth phase mycelia. Type B fermentations were inoculated using mycelia harvested at stationary phase. Productivities throughout type A fermentations were consistently higher than type B, reaching a maximum at about 70 h and then decaying to the same final productivities at 140 h of type B runs. Several scheduling alternatives, based on combinations of the two inocula types and different fermentation lengths, were compared in terms of the overall process economics (fermentation and downstream). An increase of ca. 22% on the overall process profit is predicted using late exponential growth phase inocula and a fermentation duration of only 96 h. A new operating strategy was thus proposed for inoculum production based on the control of preculture activity using off-gas analysis. This method ensures higher productivity and better batch-to-batch reproducibility of clavulanic acid fermentations than traditional methods based on constant age inocula.     

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.     

In‐situ monitoring of Saccharomyces cerevisiae ITV01 bioethanol process using near‐infrared spectroscopy NIRS and chemometrics

The application feasibility of in‐situ or in‐line monitoring of S. cerevisiae ITV01 alcoholic fermentation process, employing Near‐Infrared Spectroscopy (NIRS) and Chemometrics, was investigated. During the process in a bioreactor, in the complex analytical matrix, biomass, glucose, ethanol and glycerol determinations were performed by a transflection fiber optic probe immersed in the culture broth and connected to a Near‐Infrared (NIR) process analyzer. The NIR spectra recorded between 800 and 2,200 nm were pretreated using Savitzky‐Golay smoothing and second derivative in order to perform a partial least squares regression (PLSR) and generate the calibration models. These calibration models were tested by external validation and then used to predict concentrations in batch alcoholic fermentations. The standard errors of calibration (SEC) for biomass, ethanol, glucose and glycerol were 0.212, 0.287, 0.532, and 0.296 g/L and standard errors of prediction (SEP) were 0.323, 0.369, 0.794, and 0.507 g/L, respectively. Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:510–517, 2016     

On-line process monitoring and chemometric modeling with 2D fluorescence spectra obtained in recombinant E. coli fermentations

2D spectrofluorometry produces a large volume of spectral data during fermentation processes with recombinant E. coli, which can be analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). An analysis of the spectral data by PCA results in scores and loadings that are not only visualized in the score-loading plots but are also used to monitor the fermentation processes on-line. The score plots provided useful qualitative information on four fermentation processes for the production of extracellular 5-aminolevulinic acid (ALA). Two chemometric models (PCR and PLS) were used to examine the correlation between the 2D fluorescence spectra and a few parameters of the fermentation processes. The results showed that PLS had slightly better calibration and prediction performance than PCR.     

THE USE OF NEAR INFRARED REFLECTANCE SPECTROMETRY FOR CHARACTERIZATION OF BROWN ALGAL TISSUE1

Measuring qualitative traits of plant tissue is important to understand how plants respond to environmental change and biotic interactions. Near infrared reflectance spectrometry (NIRS) is a cost‐, time‐, and sample‐effective method of measuring chemical components in organic samples commonly used in the agricultural and pharmaceutical industries. To assess the applicability of NIRS to measure the ecologically important tissue traits of carbon, nitrogen, and phlorotannins (secondary metabolites) in brown algae, we developed NIRS calibration models for these constituents in dried Sargassum flavicans (F. K. Mertens) C. Agardh tissue. We then tested if the developed NIRS models could detect changes in the tissue composition of S. flavicans induced by experimental manipulation of temperature and nutrient availability. To develop the NIRS models, we used partial least squares regression to determine the statistical relationship between trait values determined in laboratory assays and the NIRS spectral data of S. flavicans calibration samples. Models with high predictive power were developed for all three constituents that successfully detected changes in carbon, nitrogen, and phlorotannin content in the experimentally manipulated S. flavicans tissue. Phlorotannin content in S. flavicans was inversely related to nitrogen availability, and nitrogen, temperature, and tissue age interacted to significantly affect phlorotannin content, demonstrating the importance of studies that investigate these three variables simultaneously. Given the speed of analysis, accuracy, small tissue requirements, and ability to measure multiple traits simultaneously without consuming the sample tissue, NIRS is a valuable alternative to traditional methods for determining algal tissue traits, especially in studies where tissue is limited.     

Rational strain improvement for enhanced clavulanic acid production by genetic engineering of the glycolytic pathway in Streptomyces clavuligerus

Clavulanic acid is a potent beta-lactamase inhibitor used to combat resistance to penicillin and cephalosporin antibiotics. There is a demand for high-yielding fermentation strains for industrial production of this valuable product. Clavulanic acid biosynthesis is initiated by the condensation of L-arginine and D-glyceraldehyde-3-phosphate (G3P). To overcome the limited G3P pool and improve clavulanic acid production, we genetically engineered the glycolytic pathway in Streptomyces clavuligerus. Two genes (gap1 and gap2) whose protein products are distinct glyceraldehyde-3-phosphate dehydrogenases (GAPDHs) were inactivated in S. clavuligerus by targeted gene disruption. A doubled production of clavulanic acid was consistently obtained when gap1 was disrupted, and reversed by complementation. Addition of arginine to the cultured mutant further improved clavulanic acid production giving a greater than 2-fold increase over wild type, suggesting that arginine became limiting for biosynthesis. This is the first reported application of genetic engineering to channel precursor flux to improve clavulanic acid production.