Real-time update of calibration model for better monitoring of batch processes using spectroscopy

In order to reduce the large calibration matrix usually required for calibrating multiwavelength optical sensors, a simple algorithm based on the addition in process of new standards is proposed. A small calibration model, based on 14 standards, is periodically updated by spectra collected on-line during fermentation operation. Concentrations related to these spectra are reconciled into best-estimated values, by considering carbon and oxygen balances. Using this method, fructose, acetate, and gluconacetan were monitored during batch fermentations of Gluconacetobacter xylinus 12281 using mid-infrared spectroscopy. It is shown that this algorithm compensates for noncalibrated events such as production or consumption of by-products. The standard error of prediction (SEP) values were 0.99, 0.10, and 0.90 g/L for fructose, acetate, and gluconacetan, respectively. By contrast, without an updating of the calibration model, the SEP values were 2.46, 0.92, and 1.04 g/L for fructose, acetate, and gluconacetan, respectively. Using only 14 standards, it was therefore possible to approach the performance of an 88-standard-based calibration model having SEP values of 1.11, 0.37, and 0.79 g/L for fructose, acetate, and gluconacetan, respectively. Therefore, the proposed algorithm is a valuable approach to reduce the calibration time of multiwavelength optical sensors.     

Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy

A partial least-squares calibration model, relating mid-infrared spectral features with fructose, ethanol, acetate, gluconacetan, phosphate and ammonium concentrations has been designed to monitor and control cultivations of Gluconacetobacter xylinus and production of gluconacetan, a food grade exopolysaccharide (EPS). Only synthetic solutions containing a mixture of the major components of culture media have been used to calibrate the spectrometer. A factorial design has been applied to determine the composition and concentration in the calibration matrix. This approach guarantees a complete and intelligent scan of the calibration space using only 55 standards. This calibration model allowed standard errors of validation (SEV) for fructose, ethanol, acetate, gluconacetan, ammonium and phosphate concentrations of 1.16 g/l, 0.36 g/l, 0.22 g/l, 1.54 g/l, 0.24 g/l and 0.18 g/l, respectively. With G. xylinus, ethanol is directly oxidized to acetate, which is subsequently metabolized to form biomass. However, residual ethanol in the culture medium prevents bacterial growth. On-line spectroscopic data were implemented in a closed-loop control strategy for fed-batch fermentation. Acetate concentration was controlled at a constant value by feeding ethanol into the bioreactor. The designed fed-batch process allowed biomass production on ethanol. This was not possible in a batch process due to ethanol inhibition of bacterial growth. In this way, the productivity of gluconacetan was increased from 1.8 x 10(-3) [C-mol/C-mol substrate/h] in the batch process to 2.9 x 10(-3) [C-mol/C-mol substrate/h] in the fed-batch process described in this study.     

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media.

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.     

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     

In situ near infrared spectroscopy for analyte‐specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations

There are many challenges associated with in situ collection of near infrared (NIR) spectra in a fermentation broth, particularly for highly aerated and agitated fermentations with filamentous organisms. In this study, antibiotic fermentation by the filamentous bacterium Streptomyces coelicolor was used as a model process. Partial least squares (PLS) regression models were calibrated for glucose and ammonium based on NIR spectra collected in situ. To ensure that the models were calibrated based on analyte‐specific information, semisynthetic samples were used for model calibration in addition to data from standard batches. Thereby, part of the inherent correlation between the analytes could be eliminated. The set of semisynthetic samples were generated from fermentation broth from five separate fermentations to which different amounts of glucose, ammonium, and biomass were added. This method has previously been used off line but never before in situ. The use of semisynthetic samples along with validation on an independent batch provided a critical and realistic evaluation of analyte‐specific models based on in situ NIR spectroscopy. The prediction of glucose was highly satisfactory resulting in a RMSEP of 1.1 g/L. The prediction of ammonium based on NIR spectra collected in situ was not satisfactory. A comparison with models calibrated based on NIR spectra collected off line suggested that this is caused by signal attenuation in the optical fibers in the region above 2,000 nm; a region which contains important absorption bands for ammonium. For improved predictions of ammonium in situ, it is suggested to focus efforts on enhancing the signal in that particular region. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

A New Evaluation Method for 2‐D Fluorescence Spectra Based on Theoretical Modeling

This article presents a new evaluation procedure of 2‐D fluorescence spectra obtained during a yeast cultivation without performing a calibration measurement. The 2‐D fluorescence spectra are used to predict the process variables biomass, glucose and ethanol. The new calibration procedure uses a theoretical model of these process variables, i.e., differential equations, to replace any calibration measurement. The theoretical model parameters are identified simultaneously during the calculation of the chemometric models. The root mean square error of prediction of the chemometric models with respect to off‐line measurements are 1.5 g/L, 0.40 g/L and 0.56 g/L for glucose, biomass and ethanol, respectively.     

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.     

On-line measurement of the substrate concentrations in <Emphasis Type="Italic">Pichia pastoris</Emphasis> fermentations using FT-IR/ATR

The glycerol and methanol concentrations in Pichia pastoris fermentations were measured on-line using Fourier transform infrared spectroscopy and an attenuated total reflection probe. Partial least squares regression was used to obtain calibration models. The models were regressed on synthetic multi-component spectra and semi-synthetic fermentation broth spectra. These were obtained by spectral addition. The accuracy for the on-line measurement of glycerol, given as standard error of prediction (SEP), was determined to 0.68 g/l, and the SEP of methanol was 0.13 g/l. We show how reliable calibration models are obtained relatively effortlessly by replacing extensive sampling from the reactor with simple mathematical manipulations of the model regression spectra.     

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.     

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。     

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.     

Automatic on-line fermentation headspace gas analysis using a computer-controlled gas chromatograph

A computer-controlled headspace gas chromatograph was used to monitor the progress of ethanol production from both aerobic batch and anaerobic continuous fermentations. Using an automatic, electropneumatic sampling system, aliquots of fermentation headspace gas were injected directly onto the column for quantitative ethanol determinations every six minutes. A sample volume of 1 mL permitted liquid ethanol concentrations from 2 to 100 g/L to be measured with better than 3% standard deviation on five repeated injections. Provided fermenter liquid temperature and ionic strength were maintained constant, the signal-tohyphen;concentration ratio remained linear to 80 g/L ethanol. This quantitative gas chromatographic (GC) method is suitable for accurate, precise analysis of multiple solvent fermentations, and is limited only by the elution rate and separating capacity of the GC column.     

分批补料对木糖发酵生产乙醇的影响

以树干毕赤酵母为发酵菌种,纯木糖为发酵底物,通过分批补料来提高糖利用率以及乙醇得率。结果表明,在24h内,最佳初始木糖浓度为80g/L,在28h的发酵周期中,可以将木糖浓度提高至90g/L,在32h发酵周期内可以将木糖浓度提高至100g/L。通过分批补料,乙醇浓度得到明显提高。当总糖浓度分别为80g/L、90g/L时,24h发酵周期内,分批补料次数以1次为宜,乙醇浓度分别达30.95g/L、32.60g/L,相比于不补料即一次性投料,乙醇浓度分别提高了9.36%、9.18%。总糖浓度100g/L,28h发酵周期内,补料2次效果最佳,乙醇浓度达37.49g/L,比一次性投料下提高了10.36%,较一次性投料达到相同发酵效果缩短了4h。     

Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations

A laboratory strain and an industrial strain of Saccharomyces cerevisiae were grown at high substrate concentration, so-called very high gravity (VHG) fermentation. Simultaneous saccharification and fermentation (SSF) was applied in a batch process using 280 g/L maltodextrin as carbon source. It was shown that known ethanol and osmotic stress responses such as decreased growth rate, lower viability, higher energy consumption, and intracellular trehalose accumulation occur in VHG SSF for both strains when compared with standard laboratory medium (20 g/L glucose). The laboratory strain was the most affected. GC-MS metabolite profiling was applied for assessing the yeast stress response influence on cellular metabolism. It was found that metabolite profiles originating from different strains and/or fermentation conditions were unique and could be distinguished with the help of multivariate data analysis. Several differences in the metabolic responses to stressing conditions were revealed, particularly the increased energy consumption of stressed cells was also reflected in increased intracellular concentrations of pyruvate and related metabolites.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

Real-time analyte monitoring of a fungal fermentation, at pilot scale, using in situ mid-infrared spectroscopy

Robust in situ biochemical monitoring is essential for the development of substrate feed control to optimize fermentation processes. The scale up of the fermentation for the fungus Glarea lozoyensis can benefit from such technology to improve the yield of the pharmaceutically important pneumocandin of interest and control the levels of unwanted analogues. A new in situ probe, using a diamond attenuated total reflection element, was evaluated at pilot scale for the quantitative measurement of fermentation analytes using Fourier transform mid-IR spectrometry. The new technology was shown to be stable, unaffected by reactor operation conditions of agitation, airflow, and backpressure, but sensitive to temperature control. Both glucose and phosphate were simultaneously monitored during a seed fermentation at 280 L pilot scale using complex medium with detection to 0.1 g/L for both analytes. Fructose, glutamate, and proline were monitored at 75 L scale using production media with detection limits of 0.1, 0.5, and 0.5 g/L respectively. Partial least squares calibration/prediction models were created for analytes of interest using off-line reference measurements and specific spectral regions. Good fits were obtained between off-line measurements and those predicted by in situ mid-IR. Standard errors of prediction (SEP) for glucose (range 18-0.1 g/L) and phosphate (range 11-7.5 g/L) were 0.16 and 1.8 g/L respectively with mean percentage errors (MPEs) around 2.5%. SEP values for the production process: fructose (range 20-0.1 g/L), glutamate (8-0.5 g/L), and proline (12-0.5 g/L) were 0.44, 0.6, and 0.5 g/L respectively with MPEs of 2.2, 5.3, and 10.1%. The technology effectively demonstrates quantitative multicomponent analysis of fermentation processes using in situ monitoring.     

In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed‐batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off‐line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Kinetic modeling of Candida shehatae ATCC 22984 on xylose and glucose for ethanol production

Candida shehatae ATCC 22984, a xylose-fermenting yeast, showed an ability to produce ethanol in both glucose and xylose medium. Maximum ethanol produced by the yeast was 48.8?g/L in xylose and 52.6?g/L in glucose medium with ethanol yields that varied between 0.3 and 0.4?g/g depended on initial sugar concentrations. Xylitol was a coproduct of ethanol production using xylose as substrate, and glycerol was detected in both glucose and xylose media. Kinetic model equations indicated that growth, substrate consumption, and product formation of C. shehatae were governed by substrate limitation and inhibition by ethanol. The model suggested that cell growth was totally inhibited at 40?g/L of ethanol and ethanol production capacity of the yeast was 52?g/L, which were in good agreement with experimental results. The developed model could be used to explain C. shehatae fermentation in glucose and xylose media from 20 to 170?g/L sugar concentrations.     

自絮凝酵母SPSC01在组合反应器系统中酒精连续发酵的研究

建立了一套由四级磁力搅拌发酵罐串联组成、总有效容积4000mL的小型组合生物反应器系统 ,其中一级罐作为种子培养罐。以脱胚脱皮玉米粉双酶法制备的糖化液为种子培养基和发酵底物 ,进行了自絮凝颗粒酵母酒精连续发酵的研究。种子罐培养基还原糖浓度为100g L ,添加 (NH₄)₂HPO₄ 和KH₂PO₄ 各 20g L ,以0.017h^-1 的恒定稀释速率流加 ,并溢流至后续酒精发酵系统。发酵底物初始还原糖浓度 220g/L ,添加 (NH₄)₂HPO₄ 15g/L和KH₂PO₄2 5g/L ,流加至第一级发酵罐 ,稀释速率分别为 0.017、0.025、0.033、0.040和0.05 0h^-1。实验数据表明 ,自絮凝颗粒酵母在各发酵罐中呈部分固定化状态 ,在稀释速率0.040h^-1 条件下 ,发酵系统呈一定的振荡行为 ,其他四个稀释速率实验组均能够达拟稳态。当稀释速率不超过 0 0 33h^-1 ,流出末级发酵罐的发酵液中酒精浓度可以达到 12 % (V/V)以上 ,残还原糖和残总糖分别在 0 11%和 0 35 % h^-1,流出末级发酵罐的发酵液中酒精浓度可以达到12%（V/V）以上,残还原糖和残总糖分别在0.11%和0.35%（W/V）以下。在稀释速率为0.033h^-1时,计算发酵系统酒精的设备生产强度指标为3.32（g·L^-1·h^-1）,与游离酵母细胞传统酒精发酵工艺相比,增加约1倍。