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.     

Liberation of fermentable sugars from soybean hull biomass using ionic liquid 1‐butyl‐3‐methylimidazolium acetate and their bioconversion to ethanol

Optimized hydrolysis of lignocellulosic waste biomass is essential to achieve the liberation of sugars to be used in fermentation process. Ionic liquids (ILs), a new class of solvents, have been tested in the pretreatment of cellulosic materials to improve the subsequent enzymatic hydrolysis of the biomass. Optimized application of ILs on biomass is important to advance the use of this technology. In this research, we investigated the effects of using 1‐butyl‐3‐methylimidazolium acetate ([bmim][Ac]) on the decomposition of soybean hull, an abundant cellulosic industrial waste. Reaction aspects of temperature, incubation time, IL concentration, and solid load were optimized before carrying out the enzymatic hydrolysis of this residue to liberate fermentable glucose. Optimal conditions were found to be 75°C, 165 min incubation time, 57% (mass fraction) of [bmim][Ac], and 12.5% solid loading. Pretreated soybean hull lost its crystallinity, which eased enzymatic hydrolysis, confirmed by Fourier Transform Infrared analysis. The enzymatic hydrolysis of the biomass using an enzyme complex from Penicillium echinulatum liberated 92% of glucose from the cellulose matrix. The hydrolysate was free of any toxic compounds, such as hydroxymethylfurfural and furfural. The obtained hydrolysate was tested for fermentation using Candida shehatae HM 52.2, which was able to convert glucose to ethanol at yields of 0.31. These results suggest the possible use of ILs for the pretreatment of some lignocellulosic waste materials, avoiding the formation of toxic compounds, to be used in second‐generation ethanol production and other fermentation processes. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:312–320, 2016     

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.     

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     

On-line near infrared monitoring of glycerol-boosted anaerobic digestion processes: evaluation of process analytical technologies

A study of NIR as a tool for process monitoring of thermophilic anaerobic digestion boosted by glycerol has been carried out, aiming at developing simple and robust Process Analytical Technology modalities for on-line surveillance in full scale biogas plants. Three 5 L laboratory fermenters equipped with on-line NIR sensor and special sampling stations were used as a basis for chemometric multivariate calibration. NIR characterisation using Transflexive Embedded Near Infra-Red Sensor (TENIRS) equipment integrated into an external recurrent loop on the fermentation reactors, allows for representative sampling, of the highly heterogeneous fermentation bio slurries. Glycerol is an important by-product from the increasing European bio-diesel production. Glycerol addition can boost biogas yields, if not exceeding a limiting 5-7 g L(-1) concentration inside the fermenter-further increase can cause strong imbalance in the anaerobic digestion process. A secondary objective was to evaluate the effect of addition of glycerol, in a spiking experiment which introduced increasing organic overloading as monitored by volatile fatty acids (VFA) levels. High correlation between on-line NIR determinations of glycerol and VFA contents has been documented. Chemometric regression models (PLS) between glycerol and NIR spectra needed no outlier removals and only one PLS-component was required. Test set validation resulted in excellent measures of prediction performance, precision: r(2) = 0.96 and accuracy = 1.04, slope of predicted versus reference fitting. Similar prediction statistics for acetic acid, iso-butanoic acid and total VFA proves that process NIR spectroscopy is able to quantify all pertinent levels of both volatile fatty acids and glycerol.     

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

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.     

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.     

Applicability of near-infrared spectroscopy for process monitoring in bioethanol production

The applicability of near-infrared (NIR) spectroscopy to bioethanol production is investigated. The NIR technique can provide assistance for rapid process monitoring, because organic compounds absorb radiation in the wavelength range 1100–2300 nm. For quantification of a sample's chemical composition, a calibration model is required that relates the measured spectral NIR absorbances to concentrations. For calibration, the concentrations in g/l are determined by the analytical reference method high performance liquid chromatography (HPLC). The calibration models are built and validated for moisture, protein, and starch in the feedstock material, and for glucose, ethanol, glycerol, lactic acid, acetic acid, maltose, fructose, and arabinose in the processed broths. These broths are prepared in laboratory experiments: The ground cereal samples are fermented to alcoholic broths (‘mash’), which are divided into an ethanol fraction and the residual fraction ‘stillage’ by distillation. The NIR technology together with chemometrics proved itself beneficial for fast monitoring of the current state of the bioethanol process, primarily for higher concentrated substances (>1 g/l).     

Advanced near‐infrared monitor for stable real‐time measurement and control of Pichia pastoris bioprocesses

Near‐infrared spectroscopy is considered to be one of the most promising spectroscopic techniques for upstream bioprocess monitoring and control. Traditionally the nature of near‐infrared spectroscopy has demanded multivariate calibration models to relate spectral variance to analyte concentrations. The resulting analytical measurements have proven unreliable for the measurement of metabolic substrates for bioprocess batches performed outside the calibration process. This paper presents results of an innovative near‐infrared spectroscopic monitor designed to follow the concentrations of glycerol and methanol, as well as biomass, in real time and continuously during the production of a monoclonal antibody by a Pichia pastoris high cell density process. A solid state instrumental design overcomes the ruggedness limitations of conventional interferometer‐based spectrometers. Accurate monitoring of glycerol, methanol, and biomass is demonstrated over 274 days postcalibration. In addition, the first example of feedback control to maintain constant methanol concentrations, as low as 1 g/L, is presented. Postcalibration measurements over a 9‐month period illustrate a level of reliability and robustness that promises its adoption for online bioprocess monitoring throughout product development, from early laboratory research and development to pilot and manufacturing scale operation. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:749–759, 2014     

Assessment of in-line near-infrared spectroscopy for continuous monitoring of fermentation processes

The application of NIR in-line to monitor and control fermentation processes was investigated. Determination of biomass, glucose, and lactic and acetic acids during fermentations of Staphylococcus xylosus ES13 was performed by an interactance fiber optic probe immersed into the culture broth and connected to a NIR instrument. Partial least squares regression (PLSR) calibration models of second derivative NIR spectra in the 700-1800 nm region gave satisfactory predictive models for all parameters of interest: biomass, glucose, and lactic and acetic acids. Batch, repeated batch, and continuous fermentations were monitored and automatically controlled by interfacing the NIR to the bioreactor control unit. The high frequency of data collection permitted an accurate study of the kinetics, supplying lots of data that describe the cultural broth composition and strengthen statistical analysis. Comparison of spectra collected throughout fermentation runs of S. xylosus ES13, Lactobacillus fermentum ES15, and Streptococcus thermophylus ES17 demonstrated the successful extension of a unique calibration model, developed for S. xylosus ES13, to other strains that were differently shaped but growing in the same medium and fermentation conditions. NIR in-line was so versatile as to measure several biochemical parameters of different bacteria by means of slightly adapted models, avoiding a separate calibration for each strain.     

基于近红外光谱实时监测乙醇发酵过程多组分参数

生物量、葡萄糖浓度和乙醇浓度是乙醇发酵过程的重要参数,传统的方法通常对发酵液取样作离线测量,不仅需要采用多种仪器进行测试分析,而且耗时耗力,成为实时过程调控和优化的障碍。文中针对这些重要过程参数提出了一个基于近红外光谱技术的原位实时检测方法。通过采用浸入式近红外光谱仪对发酵溶液进行原位测量,基于多输出最小二乘支持向量机回归(MLS-SVR)方法建立了利用近红外光谱同时分析葡萄糖浓度、生物量和乙醇浓度的多输出预测模型。实验结果表明,该方法能实时准确地检测乙醇发酵过程中的葡萄糖浓度、生物量和乙醇浓度,而且相对于现有的偏最小二乘法(PLS)分别对各组分建模和预测,能明显提高测量准确性和可靠性。     

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.     

Investigation of the effect of clinically relevant interferents on glucose monitoring using near-infrared spectroscopy

Near infrared spectroscopy (NIR) is a promising technique for continuous blood glucose monitoring for diabetic patients. Four interferents, at physiological concentrations, were introduced to study how the glucose predictions varied with a standard multivariate calibration model. Lactate and ethanol were found to interfere strongly with the glucose predictions unless they were included in the calibration models. Lactate was mistaken for glucose and gave erroneously high glucose predictions, with a dose response of 0.46 mM/mM. The presence of ethanol resulted in too low glucose predictions, with a dose response of −0.43 mM/mM. Acetaminophen, a known interferent in the glucose monitoring devices used for diabetes management today, was not found to be an interferent in NIR spectroscopy, nor was caffeine. Thus, interferents that may appear in high concentrations, such as ethanol and lactate, must be included in the calibration or model building of future NIR-based glucose measurement devices for diabetes monitoring.     

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.     

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     

Ethanol and value‐added byproducts from rice straw by dimorphic fungus Mucor hiemalis

Different morphologies of Mucor hiemalis were induced and used for the production of ethanol and biomass from rice straw through a separate hydrolysis and fermentation process. The yield of enzymatic hydrolysis was improved from 40.4% for the untreated straw to 80–93% by employing sodium hydroxide and concentrated phosphoric acid pretreatments with or without ultrasonication. The best hydrolysis performance was achieved after pretreatment by sodium hydroxide assisted with ultrasonication. The ethanol yields from the hydrolysates were 0.39–0.44 g/g depending on the pretreatment method and the fungus morphology. The yeast‐like form of the fungus showed faster glucose assimilation and slightly higher ethanol yield compared to the other morphologies. The biomass yield of mostly yeast‐like cells was more than the other morphologies (0.202–0.282 g/g glucose). Moreover, the biomass of the yeast‐like cells had more protein content (46.7–52.4 %) compared to filamentous cells (37.7–46.3 %). The cell wall, alkali‐insoluble material (AIM) of the biomass, represented 16.3–20.1% of the biomass. On average, total chitin‐chitosan content of AIM of the biomass of purely filamentous, mostly filamentous, mostly yeast‐like, and purely yeast‐like forms of the fungus was 0.460, 0.373, 0.330, and 0.336 g/g AIM of the biomass, respectively.     

Bio‐based extraction and stabilization of anthocyanins

This work reports a novel method of recovering anthocyanin compounds from highly‐pigmented grapes via a fermentation based approach. It was hypothesized that batch growth of Zymomonas mobilis on simple medium would produce both ethanol and enzymes/biomass‐acting materials, the combination of which will provide a superior extraction when compared to simple alcohol extraction. To examine this hypothesis, Z. mobilis was fermented in a batch consisting of mashed Vitis vinifera and glucose, and the recovered anthocyanin pool was compared to that recovered via extraction with ethanol. Data indicated higher amounts of anthocyanins were recovered when compared to simple solvent addition. Additionally, the percent polymeric form of the anthocyanins could be manipulated by the level of aeration maintained in the fermentation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:601–605, 2016     

Non‐destructive allometric estimates of above‐ground and below‐ground biomass of high‐mountain vegetation in the Andes

Aim

Studies that monitor high‐mountain vegetation, such as paramo grasslands in the Andes, lack non‐destructive biomass estimation methods. We aimed to develop and apply allometric models for above‐ground, below‐ground and total biomass of paramo plants.

Location

The paramo of southern Colombia between 1°09′N and 077°50′W, at 3,400 and 3,700 m a.s.l.

Methods

We established 61 1‐m² plots at random locations, excluding disturbed, inaccessible and peat bog areas. We measured heights and basal diameters of all vascular plants in these plots and classified them into seven growth forms. Near each plot, we sampled the biomass from plants of abundant genera, after having measured their height and basal diameter. Hence, we measured the biomass of 476 plants (allometric set). For each growth form we applied power‐law functions to develop allometric models of biomass against basal diameter, height, height x basal diameter and height × basal area. The best models were selected using AIC_c weights. Using the observed and predicted plant biomass of the allometric set we calculated absolute percentage errors using cross‐validation. The biomass of a plot was estimated by summing the predicted biomass of all plants in a plot. Confidence limits around these sums were calculated by bootstrapping.

Results

For groups of <20 plants the biomass predictions yielded large (>15%) errors. Applying groups that resembled the 1‐m² plots in density and composition, the errors for above‐ground and total biomass estimates were <15%. Across all plots, we obtained an above‐ground, below‐ground and total plot biomass of 329 ± 190, 743 ± 486 and 1011 ± 627 g/m² (mean ± SD), respectively. These values were within the range of biomass estimates obtained destructively in the tropical Andes.

Conclusions

In new applications, if target vegetation samples are similar regarding growth forms and genera to our allometric set, their biomass might be predicted applying our equations, provided they contain at least 50–100 plants. In other situations, we would recommend gathering additional biomass measurements from local plants to evaluate new regression equations.