Real-Time Monitoring of Bioethanol Fermentation with Industrial Musts Using Mid-Infrared Spectroscopy

Rodrigues, Kaio C. S.; Sonego, Jorge Luíz Silveira; Bernardo, André; Ribeiro, Marcelo Perencin de Arruda; Cruz, Antonio José Gonçalves; Badino, Alberto Colli

doi:10.1021/acs.iecr.8b01181

Cited by 18 publications

(33 citation statements)

References 41 publications

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“…The PLS method was applied for the multivariate analyses. The calibration set was used, following the leave‐one‐out cross‐validation (LOOCV) method, in order to obtain the number of latent variables (LV) for the models, according to Rodrigues et al [ 31 ] The minimum root‐mean‐square error of cross‐validation (RMSECV) was used as a criterion to select the number of LV, as follows:

italicRMSECV = \sqrt{\frac{1}{n_{italiccl}} \sum_{j = 1}^{n_{italiccl}} {(C_{j}^{((), i)} - {\overset{C}{true}}_{j}^{((), i)})}^{2}}

where n cl is the size of the calibration set; and

C_{j}^{(i)}

and

{\overset{false^}{C}}_{j}^{(i)}

are the reference and predicted concentrations of the j th sample for the i th analyte, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…It is important to mention that these experimental conditions (temperature, ethanol concentration, and volumetric CO 2 flow rate) represented the conditions of ethanol fermentations with in-situ ethanol removal by CO 2 stripping. [8,9,13,33]…”

Section: Absorption Experiments 241 | Gas Stream Generationmentioning

confidence: 99%

“…The PLS method was applied for the multivariate analyses. The calibration set was used, following the leaveone-out cross-validation (LOOCV) method, in order to obtain the number of latent variables (LV) for the models, according to Rodrigues et al [31] The minimum root-mean-square error of cross-validation (RMSECV) was used as a criterion to select the number of LV, as follows:…”

Section: Ft-mir Data Preprocessing and Multivariate Calibrationmentioning

confidence: 99%

“…Several literature reports have shown the benefits of using CO 2 stripping for ethanol removal during ethanol fermentation from corn starch, [6,7] sugarcane, [8,9] and sweet sorghum, [10] and for removal of butanol during acetone-butanol-ethanol fermentations. [11,12] However, efficient and cost-effective methods to recover ethanol from the gas phase are still needed, in order to make it feasible to use stripping in the ethanol fermentation process.…”

Section: Introductionmentioning

confidence: 99%

“…[29] The partial least squares (PLS) method has been widely used for calibration. [30] FT-MIR/PLS methodologies have been successfully applied for quantifying ethanol in different processes such as production of ethanol, [31] wine, [32] and other fermented alcoholic beverages. [22] To the best of our knowledge, there have been no previous reports in the literature concerning the use of infrared spectroscopy for the monitoring of gas absorption processes.…”

Section: Introductionmentioning

confidence: 99%

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Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

et al. 2020

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Fast, simple, accurate, and inexpensive methods for obtaining analyte concentration data are desirable in the industrial sector. In the present study, the use of Fourier transform mid-infrared (FT-MIR) spectroscopy, combined with partial least squares (PLS) regression, was investigated as a tool for real-time monitoring of processes of ethanol absorption in glycols. Calibration was performed using simple synthetic samples containing ethanol, water, and monoethylene glycol (MEG) or diethylene glycol (DEG). The PLS models presented excellent performance, with correlation coefficients (R 2) close to unity and root-mean-square errors of cross-validation (RMSECV) and prediction (RMSEP) lower than 2% of the calibration data ranges for both analytes (ethanol and water) in both absorbents (MEG and DEG). The monitoring technique developed has potential to be applied in absorption processes and could also be used in other large-scale unit operations, providing information in real time and enhancing process control.

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italicRMSECV = \sqrt{\frac{1}{n_{italiccl}} \sum_{j = 1}^{n_{italiccl}} {(C_{j}^{((), i)} - {\overset{C}{true}}_{j}^{((), i)})}^{2}}

where n cl is the size of the calibration set; and

C_{j}^{(i)}

and

{\overset{false^}{C}}_{j}^{(i)}

are the reference and predicted concentrations of the j th sample for the i th analyte, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Absorption Experiments 241 | Gas Stream Generationmentioning

confidence: 99%

Section: Ft-mir Data Preprocessing and Multivariate Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

et al. 2020

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Advance in Bioethanol Technology: Production and Characterization

Sasmal

Mohanty

2021

Liquid Biofuels

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On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process

Cortada-Garcia

Haggarty

Moses

et al. 2022

Biotech & Bioengineering

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The real-time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on-line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)-2,3-dihydroxy-isovalerate and (R)-2,3-dihydroxy-3-methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

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Real-Time Monitoring of Bioethanol Fermentation with Industrial Musts Using Mid-Infrared Spectroscopy

Cited by 18 publications

References 41 publications

Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

Advance in Bioethanol Technology: Production and Characterization

On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process

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