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     

Chemometric modelling based on 2D-fluorescence spectra without a calibration measurement

MOTIVATION: 2D fluorescence spectra provide information from intracellular compounds. Fluorophores like trytophan, tyrosine and phenylalanin as well as NADH and flavins make the corresponding measurement systems very important for bioprocess supervision and control. The evaluation is usually based on chemometric modelling using for their calibration procedure off-line measurements of the desired process variables. Due to the data driven approach lots of off-line measurements are required. Here a methodology is presented, which enables to perform a calibration procedure of chemometric models without any further measurement. RESULTS: The necessary information for the calibration procedure is provided by means of the a priori knowledge about the process, i.e. a mathematical model, whose model parameters are estimated during the calibration procedure, as well as the fact that the substrate should be consumed at the end of the process run. The new methodology for chemometric calibration is applied for a batch cultivation of aerobically grown S. cerevisiae on the glucose Schatzmann medium. As will be presented the chemometric models, which are determined by this method, can be used for prediction during new process runs. AVAILABILITY: The MATHLAB routine is free available on request from the authors.     

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.     

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.     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

Real‐time monitoring of ethanol production during Pichia stipitis NRRL Y‐7124 alcoholic fermentation using transflection near infrared spectroscopy

The application of in situ near‐infrared spectroscopy monitoring of xylose metabolizing yeast such as Pichia stipitis for ethanol production with semisynthetic media, applying chemometrics, was investigated. During the process in a bioreactor, biomass, glucose, xylose, ethanol, acetic acid, and glycerol determinations were performed by a transflection probe immersed in the culture broth and connected to a near‐infrared process analyzer. Wavelength windows in near‐infrared spectra recorded between 800 and 2200 nm were pretreated using Savitzky–Golay smoothing, second derivative and multiplicative scattering correction in order to perform a partial least squares regression and generate the calibration models. These calibration models were tested by external validation (78 samples). Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. Moreover, regressions coefficients (β) and variable influence in the projection plots were used to assess the results. A novelty is the use of β versus VIP dispersion plots to determine which vectors have more influence on the response in order to improve process comprehension and operability. Validated models were used in a real‐time monitoring during P. stipitis NRRL Y7124 semisynthetic media fermentations.     

超高浓度培养基条件下酵母细胞生长及酒精生成的准稳态动力学研究

在1．5L搅拌式发酵罐中,使用葡萄糖质量浓度分别为120、200、280g／L的培养基进行酿酒酵母Saccharomyces cerevisiae连续发酵生成酒精的动力学研究。研究发现,当培养基中葡萄糖浓度为200和280g／L时,发酵液中残糖浓度、酒精浓度以及菌体生物量从小幅度波动的准稳态发展到大幅度波动的振荡状态。提出了伴有周期性振荡现象准稳态过程的概念,并针对该过程,建立了兼有底物和产物抑制的酵母细胞生长和产物酒精生成动力学模型。     

Chemometric modeling and two-dimensional fluorescence analysis of bioprocess with a new strain of <Emphasis Type="Italic">Klebsiella pneumoniae</Emphasis> to convert residual glycerol into 1,3-propanediol

The goal of this study was to show that the metabolism of Klebsiella pneumoniae under different aeration strategies could be monitored and predicted by the application of chemometric models and fluorescence spectroscopy. Multi-wavelength fluorescence was applied to the on-line monitoring of process parameters for K. pneumoniae cultivations. Differences observed in spectra collected under aerobiosis and anaerobiosis can be explained by the different metabolic states of the cells. To predict process variables such as biomass, glycerol, and 1,3-propanediol (1,3-PD), chemometric models were developed on the basis of the acquired fluorescence spectra, which were measured continuously. Although glycerol and 1,3-PD are not fluorescent compounds, the results showed that this technique could be successfully applied to the on-line monitoring of variables in order to understand the process and thus improve 1,3-PD production. The root mean square errors of predictions were 0.78 units, 10 g/L, and 2.6 g/L for optical density, glycerol, and 1,3-PD, respectively.     

Analytical monitoring of alcoholic fermentation using NIR spectroscopy

Alcoholic fermentation under Saccharomyces cerevisiae yeasts is governed largely by glucose uptake, biomass formation, ethanol and glycerin production, and acidification. In this work, PLS calibration models were developed with a view to determining these analytical parameters from near infrared spectra and analytical data provided by the corresponding reference methods. The models were applied to a set of samples obtained from various fermentation processes. The glucose, ethanol, and biomass values predicted by the models exhibited a high correlation with those provided by the reference method.     

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.     

Monitoring a bioprocess for ethanol production using FT-MIR and FT-Raman spectroscopy

The application of Fourier transform mid-infrared (FT-MIR) spectroscopy and Fourier transform Raman (FT-Raman) spectroscopy for process and quality control of fermentative production of ethanol was investigated. FT-MIR and FT-Raman spectroscopy along with multivariate techniques were used to determine simultaneously glucose, ethanol, and optical cell density of Saccharomyces cerevisiae during ethanol fermentation. Spectroscopic measurement of glucose and ethanol were compared and validated with the high-performance liquid chromatography (HPLC) method. Spectral wave number regions were selected for partial least-squares (PLS) regression and principal component regression (PCR) and calibration models for glucose, ethanol, and optical cell density were developed for culture samples. Correlation coefficient (R ²) value for the prediction for glucose and ethanol was more than 0.9 using various calibration methods. The standard error of prediction for the PLS first-derivative calibration models for glucose, ethanol, and optical cell density were 1.938 g/l, 1.150 g/l, and 0.507, respectively. Prediction errors were high with FT-Raman because the Raman scattering of the cultures was weak. Results indicated that FT-MIR spectroscopy could be used for rapid detection of glucose, ethanol, and optical cell density in S. cerevisiae culture during ethanol fermentation. Journal of Industrial Microbiology & Biotechnology (2001) 26, 185–190. Received 16 November 2000/ Accepted in revised form 12 January 2001     

Scale-up and integration of alkaline hydrogen peroxide pretreatment, enzymatic hydrolysis, and ethanolic fermentation

Alkaline hydrogen peroxide (AHP) has several attractive features as a pretreatment in the lignocellulosic biomass‐to‐ethanol pipeline. Here, the feasibility of scaling‐up the AHP process and integrating it with enzymatic hydrolysis and fermentation was studied. Corn stover (1 kg) was subjected to AHP pretreatment, hydrolyzed enzymatically, and the resulting sugars fermented to ethanol. The AHP pretreatment was performed at 0.125 g H₂O₂/g biomass, 22°C, and atmospheric pressure for 48 h with periodic pH readjustment. The enzymatic hydrolysis was performed in the same reactor following pH neutralization of the biomass slurry and without washing. After 48 h, glucose and xylose yields were 75% and 71% of the theoretical maximum. Sterility was maintained during pretreatment and enzymatic hydrolysis without the use of antibiotics. During fermentation using a glucose‐ and xylose‐utilizing strain of Saccharomyces cerevisiae, all of the Glc and 67% of the Xyl were consumed in 120 h. The final ethanol titer was 13.7 g/L. Treatment of the enzymatic hydrolysate with activated carbon prior to fermentation had little effect on Glc fermentation but markedly improved utilization of Xyl, presumably due to the removal of soluble aromatic inhibitors. The results indicate that AHP is readily scalable and can be integrated with enzyme hydrolysis and fermentation. Compared to other leading pretreatments for lignocellulosic biomass, AHP has potential advantages with regard to capital costs, process simplicity, feedstock handling, and compatibility with enzymatic deconstruction and fermentation. Biotechnol. Bioeng. 2012; 109:922–931. © 2011 Wiley Periodicals, Inc.     

On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy

Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.     

UV/VIS diffuse reflectance and fluorescence spectroscopy of glucose fermentation with saccharomyces cerevisiae

UV/VIS diffuse reflectance spectroscopy and fluorescence spectroscopy have been used to investigate the cytochrome and pyridine nucleotide spectra during aerobic biomass growth of Saccharomyces cerevisiae followed by an anaerobic ethanol formation process. The cytochrome and NAD(P)H spectra are closely related to fermentation parameters such as biomass growth rate and ethanol concentration.     

Evaluation of multiwavelength culture fluorescence for monitoring the aroma compound 4-hydroxy-2(or 5)-ethyl-5(or 2)-methyl-3(2H)-furanone (HEMF) production

Fluorescence spectra of a 4-hydroxy-2(or 5)-ethyl-5(or 2)-methyl-3(2H)-furanone (HEMF) fermentation culture broth were combined with measurable process variables for off-line and on-line process monitoring. Culture broth fluorescence in UV and visible ranges was acquired by a fiber optic LCD array spectrometer. Process dynamics was followed on-line using a fiber optic probe attached to an external recirculation loop of the bioreactor. Partial least squares and stepwise regression methods were used to correlate measurable process parameters with the components of the fluorescence spectra. Both methods provided adequate approximation of yeast density, HEMF, glucose, and ethanol concentrations from fluorescence spectra. HEMF production was observed during the oxido-reductive growth phase when there was a lack of measurable oxygen in the culture broth and an excess of glucose. The addition of glucose resulted in the rapid production of HEMF and other metabolite intermediates such as ethanol, acetate, and glycerol.     

Production of ethanol and biomass from thin stillage by Neurospora intermedia: A pilot study for process diversification

Dry mill ethanol processes produce ethanol and animal feed from whole grains, where the wastewater after the distillation and separation of solid materials is called “thin stillage.” In this work, similar production of ethanol (3.5 g/L) and biomass (5 g/L) from thin stillage was obtained during batch cultivation of the edible fungus Neurospora intermedia in a 2‐m high airlift reactor and bubble column. The fungal biomass, containing 50% w/w protein and 12% w/w lipids, was rich in essential amino acids and omega‐3 and ‐6 fatty acids. In a continuous mode of fermentation, dilution rates of up to 0.2 h^?1 could be applied without cell washout in the bubble column at 0.5 vvm. At 0.1 h^?1, around 5 g/L of ethanol and 4 g/L of biomass containing ca. 50% w/w protein were produced. The fungus was able to assimilate saccharides in the liquid fraction as well as sugar backbones such as xylan and arabinan in the solid fraction. The inclusion of the current process could potentially lead to the production of 11 000 m³ of ethanol (5.5% improvement vs. normal industrial process) and around 6300 tons of high‐quality biomass for animal feed at a typical facility producing 200 000 m³ ethanol per year.     

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.     

Production of ethanol from corn stover hemicellulose hydrolyzate using <Emphasis Type="Italic">Pichia stipitis</Emphasis>

Hemicellulose liquid hydrolyzate from dilute acid pretreated corn stover was fermented to ethanol using Pichia stipitis CBS 6054. The fermentation rate increased with aeration but the pH also increased due to consumption of acetic acid by Pichia stipitis. Hemicellulose hydrolyzate containing 34 g/L xylose, 8 g/L glucose, 8 g/L Acetic acid, 0.73 g/L furfural, and 1 g/L hydroxymethyl furfural was fermented to 15 g/L ethanol in 72 h. The yield in all the hemicellulose hydrolyzates was 0.37–0.44 g ethanol/g (glucose + xylose). Nondetoxified hemicellulose hydrolyzate from dilute acid pretreated corn stover was fermented to ethanol with high yields, and this has the potential to improve the economics of the biomass to ethanol process.     

Repeated-batch fermentations of xylose and glucose-xylose mixtures using a respiration-deficient Saccharomyces cerevisiae engineered for xylose metabolism

Xylose-fermenting Saccharomyces strains are needed for commercialization of ethanol production from lignocellulosic biomass. Engineered Saccharomyces cerevisiae strains expressing XYL1, XYL2 and XYL3 from Pichia stipitis, however, utilize xylose in an oxidative manner, which results in significantly lower ethanol yields from xylose as compared to glucose. As such, we hypothesized that reconfiguration of xylose metabolism from oxidative into fermentative manner might lead to efficient ethanol production from xylose. To this end, we generated a respiration-deficient (RD) mutant in order to enforce engineered S. cerevisiae to utilize xylose only through fermentative metabolic routes. Three different repeated-batch fermentations were performed to characterize characteristics of the respiration-deficient mutant. When fermenting glucose as a sole carbon source, the RD mutant exhibited near theoretical ethanol yields (0.46 g g(-1)) during repeated-batch fermentations by recycling the cells. As the repeated-batch fermentation progressed, the volumetric ethanol productivity increased (from 7.5 to 8.3 g L(-1)h(-1)) because of the increased biomass from previous cultures. On the contrary, the mutant showed decreasing volumetric ethanol productivities during the repeated-batch fermentations using xylose as sole carbon source (from 0.4 to 0.3 g L(-1)h(-1)). The mutant did not grow on xylose and lost fermenting ability gradually, indicating that the RD mutant cannot maintain a good fermenting ability on xylose as a sole carbon source. However, the RD mutant was capable of fermenting a mixture of glucose and xylose with stable yields (0.35 g g(-1)) and productivities (0.52 g L(-1)h(-1)) during the repeated-batch fermentation. In addition, ethanol yields from xylose during the mixed sugar fermentation (0.30 g g(-1)) were higher than ethanol yields from xylose as a sole carbon source (0.21 g g(-1)). These results suggest that a strategy for increasing ethanol yield through respiration-deficiency can be applied for the fermentation of lignocellulosic hydrolyzates containing glucose and xylose.     

利用木糖-葡萄糖为原料产乙醇酵母的筛选、鉴定及发酵试验

建立筛选利用木糖为碳源产乙醇酵母模型,获得一株适合利用木质纤维素为原料产乙醇的酵母菌株。样品经麦芽汁培养基培养后,以木糖为唯一碳源的筛选培养基初筛,再以重铬酸钾显色法复筛。通过生理生化和26D1/D2区对筛选得到的菌株进行分析和鉴定,该菌初步鉴定为Pichia caribbica。经过筛选得到的菌株Y2-3以木糖（40g/L）为唯一碳源发酵时：生物量为23.5g/L,木糖利用率为94.7 %,乙醇终产量为4.57 g/L;以混合糖（葡萄糖40 g/L,木糖20 g/L）发酵时：生物量为28.6 g/L,木糖利用率为94.2 %,葡萄糖利用率为95.6%,乙醇终产量为20.6 g/L。Pichia caribbica是可以转化木糖及木糖-葡萄糖混合糖为乙醇的酵母菌株,为利用木质纤维素发酵乙醇的进一步研究奠定了基础。