基于傅里叶变换近红外光谱实时分析1,3-丙二醇发酵过程生物量的在线监测方法

生物量是反映生物发酵过程进展的重要参数,对生物量进行实时监测可用于对发酵过程的调控优化。为克服目前主要采用的离线方法检测生物量时间滞后和人工测量误差较大等缺点,本研究针对1,3-丙二醇发酵过程设计了一个基于傅里叶变换近红外光谱实时分析技术的生物量在线监测实验平台,通过对实时采集光谱预处理以及敏感光谱段分析,应用偏最小二乘算法,建立了1,3-丙二醇发酵过程生物量变化的动态预测模型。以底物甘油浓度为60 g/L和40 g/L的发酵过程作为外部验证实验,分析得到模型的预测均方根误差分别为0.341 6和0.274 3,结果表明所建立的模型具有较好的实时预测能力,能够实现对1,3-丙二醇发酵过程中生物量的有效在线监测。     

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。     

基于近红外检测技术建立发酵过程中L-色氨酸浓度预测模型

目的:我国发酵法生产L-色氨酸存在着产酸率、糖酸转化率和提取率较低以及检测速度较慢等不足之处,为了提升发酵控制水平及产物浓度的快速检测,采用近红外检测技术建立发酵过程中L-色氨酸浓度预测模型。方法:利用近红外光谱技术,结合偏最小二乘法,建立发酵液中L-色氨酸浓度预测模型。结果:在光谱预处理为二阶导数、波数范围为6101.8~5450 cm-1条件下获得最优L-色氨酸浓度预测模型。经验证,该模型具有一定的准确性和可靠性,其预测值与测量值仅有5.16%的偏差。结论:该模型具有较好的预测能力,可为大肠杆菌L-色氨酸发酵过程中浓度快速检测提供可靠数据,并为L-色氨酸发酵过程控制提供理论和实践依据。     

生物量浓度实时在线检测方法的研究

微生物的存在会改变发酵液的电特性,发酵液在无线电频率范围内的电容率增量是测量频率和生物量浓度的函数.基于对发酵液电容率分布的研究,提出了测量生物量浓度的新方法.用此方法不用取样就能对发酵液中的生物量进行实时在线测量,而且测得的是活的生物量浓度.制作的电极直接插入发酵器中并满足高温蒸气灭菌条件.此方法在生化制药、食品发酵、啤酒酿造、污水检测等工业领域里有很好的推广应用前景.     

谷氨酸棒杆菌发酵过程中亮氨酸浓度近红外模型的建立

目的:建立谷氨酸棒杆菌发酵过程中亮氨酸浓度近红外模型,为实现谷氨酸棒杆菌发酵生产亮氨酸的发酵过程自动化控制提供理论基础和实践依据。方法:首先在5 L发酵罐中进行亮氨酸发酵,每隔一段时间采集发酵液样品,用高效液相色谱精确分析各样品中的亮氨酸实际浓度,再利用近红外分析仪和相关软件,对各样品进行近红外光谱扫描分析,并通过近红外光谱分析软件进行数据处理,建立谷氨酸棒杆菌发酵过程中亮氨酸浓度的近红外预测模型,最后通过外部检验方法检验模型的准确性。结果:结合偏最小二乘法,在波长为9043.3~7489.1 cm-1、减去一条直线作为光谱预处理的条件下,获得谷氨酸棒杆菌发酵过程中亮氨酸浓度最优近红外预测模型。该模型交叉验证误差均方根(RMSECV)、决定系数(R2)以及剩余预测偏差(RPD)分别为1.29 g/L、0.977和4.55。结论:经过验证,该模型的准确性和可靠性较强,实际值与预测值之间的误差较小,能够较好地检测发酵过程中的亮氨酸浓度。     

固体发酵物料可溶性糖和乙醇检测方法研究进展

发酵料中可溶性糖与乙醇含量是判断发酵进程的重要指标,开发糖和乙醇快速检测方法对发酵过程优化与控制非常重要。对于固体物料的检测,通常需要在检测前将固体物料中的糖和乙醇转移到液相。综述了固体物料中可溶性糖和乙醇的提取和定量检测方法,以及用于酒精固体发酵过程监控的近红外光谱定性与定量模型建立情况。最后,对固体物料可溶性糖和乙醇含量的检测技术与固体发酵过程监控技术提出建议。     

谷胱甘肽发酵过程中的乙醇控制

在酿酒酵母谷胱甘肽发酵中,比较了乙醇控制在一定浓度和逐步下降两种乙醇控制方式对谷胱甘肽合成的影响,后者较好。考察了后一控制方法与葡萄糖浓度和流加速率、呼吸商、谷胱甘肽总量和含量的关系,结果表明,在不添加前体氨基酸的情况下能达到1620mg／L。     

奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用

相对于传统生化测定方法,基于近红外光谱(Near infrared spectroscopy,NIRS)玉米籽粒蛋白质含量检测是一种快速、非破坏、且适用于多组分同时检测的新方法。但在建模过程中,由于奇异数据(异常值)的存在会影响近红外光谱模型的预测精度和稳定性,我们采用奇异数据筛选法剔除了玉米籽粒近红外光谱中的奇异数据并建立了玉米籽粒蛋白质含量的偏最小二乘支持向量机(Least squares support vector machine,LS-SVM)模型。本文分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样法(Monte-Carlo Sampling,MCS)剔除了玉米籽粒蛋白质光谱数据中的奇异数据并对模型结果进行比较。在剔除奇异数据的基础上,采用偏最小二乘回归法(Partial least squares regression,PLSR)提取主成分,并基于小生境蚁群算法(Niche ant colony algorithm,NACA)优化偏最小二乘支持向量机(LS-SVM)模型参数(γ和σ2),建立基于LS-SVM的玉米籽粒蛋白质定量分析模型。结果表明,采用3种奇异数据筛选法剔除奇异数据后所建LS-SVM模型的预测结果都优于采用原光谱数据所建模型,相比较而言,蒙特卡洛采样法为基于近红外光谱检测玉米籽粒蛋白质的最佳奇异数据筛选法。     

休哈塔假丝酵母乙醇发酵性能的研究

木糖的高效发酵是制约纤维素燃料乙醇生产的技术瓶颈之一,高性能发酵菌种的开发是本领域研究的重点。以木糖发酵的典型菌株休哈塔假丝酵母为材料,研究氮源配比、葡萄糖和木糖初始浓度、葡萄糖添加及典型抑制物等因素对其木糖利用和乙醇发酵性能的影响规律。结果表明,硫酸铵更适宜于木糖和葡萄糖发酵产乙醇。在摇瓶振荡发酵条件下,该酵母可发酵164.0 g/L葡萄糖生成61.9 g/L乙醇,糖利用率和乙醇得率分别为99.8%和74.0%;受酵母细胞膜上转运体系的限制,对木糖的最高发酵浓度为120.0 g/L,可生成45.7 g/L乙醇,糖利用率和乙醇得率分别达到94.8%和87.0%。休哈塔假丝酵母发酵木糖的主要产物为乙醇,仅生成微量的木糖醇;添加葡萄糖可促进木糖的利用;休哈塔假丝酵母在葡萄糖发酵时的乙酸和甲酸的耐受浓度分别为8.32和2.55 g/L,木糖发酵时的乙酸和甲酸的耐受浓度分别为6.28和1.15 g/L。     

戊糖和己糖共发酵生产燃料乙醇条件研究

利用嗜鞣管囊酵母P-01对木糖和葡萄糖共发酵生产燃料乙醇的条件进行了试验研究,结果表明,木糖和葡萄糖混合液生产燃料乙醇的最佳条件为发酵液的pH值5.5、30℃、摇床转速120 r/min、接种量10%、发酵液初始糖浓度6%、葡萄糖与木糖之比为2、发酵周期为84h。在最佳发酵条件下,发酵醪液中的燃料乙醇浓度为2.101%,糖醇转化率为35%。     

A near-infrarod spectroscopy technique for the control of fermentation processes: An application to lactic acid fermentation

A near-infrared (NIR) spectroscopy technique for the control of lactic acid fermentation process has been proposed. Lactic acid, glucose, and biomass concentrations were determined by the NIR spectroscopy method. The three parameters examined were closely correlated to the results obtained with classical laboratory procedures. Moreover, the conditions for the on-line utilization of the NIR spectroscopy measurement system were pointed out. The great versatility of the NIR spectroscopy should permit its use for other fermentation processes. (c) 1994 John Wiley & Sons, Inc.     

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.     

Inline noninvasive Raman monitoring and feedback control of glucose concentration during ethanol fermentation

Raman spectroscopy as a process analytical technology tool was implemented for the monitoring and control of ethanol fermentation carried out with Saccharomyces cerevisiae. The need for the optimization of bioprocesses such as ethanol production, to increase product yield, enhanced the development of control strategies. The control system developed by the authors utilized noninvasive Raman measurements to avoid possible sterilization problems. Real-time data analysis was applied using partial least squares regression (PLS) method. With the aid of spectral pretreatment and multivariate data analysis, the monitoring of glucose and ethanol concentration was successful during yeast fermentation with the prediction error of 4.42 g/L for glucose and 2.40 g/L for ethanol. By Raman spectroscopy-based feedback control, the glucose concentration was maintained at 100 g/L by the automatic feeding of concentrated glucose solution. The control of glucose concentration during fed-batch fermentation resulted in increased ethanol production. Ethanol yield of 86% was achieved compared to the batch fermentation when 75 % yield was obtained. The results show that the use of Raman spectroscopy for the monitoring and control of yeast fermentation is a promising way to enhance process understanding and achieve consistently high production yield.     

An on-line approach to monitor ethanol fermentation using FTIR spectroscopy

Fermentation process control is currently limited by its inability to measure parameters such as substrate, product, and biomass concentrations rapidly for consistent on-line feedback. Physical and chemical parameters, such as temperature and pH, currently can be obtained on-line using appropriate sensors. However, to obtain information on the concentration of the substrate, product, and biomass, samples must be taken off-line for measurement. With the use of spectroscopic techniques, real-time monitoring of process constituents such as product and substrate is possible. Spectroscopic techniques are rapid and nondestructive, require minimal or no sample preparation, and can be used to simultaneously assess several constituents in complex matrices. The production of ethanol is the largest fermentation process in terms of production volume and economic value as a result of its prominence in the food, agricultural, and fuel industries. This study attempts to develop an on-line ethanol fermentation monitoring technique using Fourier transform infrared (FTIR) spectroscopy with a flow-through ATR capability. Models developed using multivariate statistics, employed to obtain on-line FTIR measurements, were successfully validated by off-line HPLC analysis and spectrophotometry data. Standard errors of prediction (SEP) values of 0.985 g/L (R2 = 0.996), 1.386 g/L (R2 = 0.998), and 0.546 (R2 = 0.972) were obtained for ethanol, glucose, and OD, respectively. This work demonstrates that FTIR spectroscopy could be used for rapid on-line monitoring of fermentation.     

青贮对柳枝稷制取燃料乙醇转化过程的影响

青贮是一种传统的生物质原料保存方法,广泛应用于纤维素乙醇炼制领域尚需要考察其对原料品质和下游乙醇转化过程的影响。文中以秋季(初、中和末)收割的柳枝稷为原料,通过青贮、高温水热(LHW)预处理、纤维素酶水解和同步糖化与发酵(SSF)实验对上述问题予以回答。结果显示,秋季初收割的柳枝稷以不同湿度青贮后pH均小于4.0,干重损失小于2%,各主要成分与青贮前相比无明显变化;LHW预处理中青贮样品半纤维素水解率普遍高于未贮存样品,但青贮同样使原料获得了更高的发酵抑制物产生水平;青贮柳枝稷葡萄糖、木糖和半乳糖产量(预处理+酶水解)高于未贮存柳枝稷;经过168 h的SSF,青贮样品乙醇浓度为12.1 g/L,未贮存的秋季初、秋季中和秋季末柳枝稷为底物的浓度分别为10.3 g/L、9.7 g/L和10.6 g/L。综上,青贮有助于提高柳枝稷LHW预处理效率、酶水解率和乙醇产量。     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

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     

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     

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.     

Enhanced production of L-(+)-lactic acid in chemostat by Lactobacillus casei DSM 20011 using ion-exchange resins and cross-flow filtration in a fully automated pilot plant controlled via NIR

Due to the lack of suitable in-process sensors, on-line monitoring of fermentation processes is restricted almost exclusively to the measurement of physical parameters only indirectly related to key process variables, i.e., substrate, product, and biomass concentration. This obstacle can be overcome by near infrared (NIR) spectroscopy, which allows not only real-time process monitoring, but also automated process control, provided that NIR-generated information is fed to a suitable computerized bioreactor control system. Once the relevant calibrations have been obtained, substrate, biomass and product concentration can be evaluated on-line and used by the bioreactor control system to manage the fermentation. In this work, an NIR-based control system allowed the full automation of a small-scale pilot plant for lactic acid production and provided an excellent tool for process optimization. The growth-inhibiting effect of lactic acid present in the culture broth is enhanced when the growth-limiting substrate, glucose, is also present at relatively high concentrations. Both combined factors can result in a severe reduction of the performance of the lactate production process. A dedicated software enabling on-line NIR data acquisition and reduction, and automated process management through feed addition, culture removal and/or product recovery by microfiltration was developed in order to allow the implementation of continuous fermentation processes with recycling of culture medium and cell recycling. Both operation modes were tested at different dilution rates and the respective cultivation parameters observed were compared with those obtained in a conventional continuous fermentation. Steady states were obtained in both modes with high performance on lactate production. The highest lactate volumetric productivity, 138 g L(-1) h(-1), was obtained in continuous fermentation with cell recycling.