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

Corro-Herrera, Víctor Abel; Gómez-Rodríguez, J.; Hayward‐Jones, Patricia M.; Barradas-Dermitz, Dulce María; Gschaedler‐Mathis, Anne; Aguilar‐Uscanga, María Guadalupe

doi:10.1002/elsc.201700189

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…Several in-line and on-line methods to monitor alcoholic fermentation have been investigated, including in-situ transflectance near-infrared spectroscopy [7,8], and Raman spectroscopy probes [9]; automated flow-through mid-infrared spectroscopy [10], Fourier transform infrared spectroscopy [11], and piezoelectric MEMS resonators [12]; non-invasive Raman spectroscopy through transparent vessel walls [13]; and CO 2 emission monitoring [14]. Ultrasonic (US) sensors are an attractive monitoring technique owing to their low Fermentation 2021, 7, 34 2 of 13 cost and have previously been used to study fermentation, including as in-line methods on circulation lines [15], in-situ in tanks [16], and using non-invasive, through-transmission of the fermenting media [17,18].…”

Section: Introductionmentioning

confidence: 99%

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

et al. 2021

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Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2 = 0.952, mean absolute error (MAE) = 0.265, mean squared error (MSE) = 0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2 = 0.948, MAE = 0.283, MSE = 0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

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Section: Introductionmentioning

confidence: 99%

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

et al. 2021

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Section: Introductionmentioning

confidence: 99%

“…Further, the absorption intensities are directly affected by the concentration of the absorbing compound in the sample, making NIR a sensitive instrument for qualitative and quantitative analytical information (Pasquini, 2018). Recently, several studies have demonstrated the usefulness of NIR for the characterization of several key analytes in cell cultures (Arnold et al, 2003; Corro‐Herrera et al, 2018; Kambayashi et al, 2020; Kozma et al, 2019; Li et al, 2018). However, in contrast to MIR spectroscopy, the NIR spectrum is more enigmatic with overlapping combinations bands and overtones, thus necessitating extensive use of multivariate statistical models for quantitative analysis (Torniainen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

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As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

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“…In-line and on-line sensors underpin this transformation by collecting the real-time data to provide automatic decision-making and minimise human involvement [6]. Several in-line and on-line methods to monitor alcoholic fermentation have been investigated, such as near-infrared spectroscopy [3,7], Raman spectroscopy [8,9], mid-infrared spectroscopy [10], Fourier transform infrared spectroscopy [11], MEMS resonators [12], CO 2 emission monitoring [13], and ultrasonic (US) sensors [14][15][16][17][18]. Typically, these techniques use calibration techniques to correlate sensor data to material composition across the full range of process conditions (e.g., temperature) [3].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

2021

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Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling identification of lagging fermentations or prediction of ethanol production end points. Ultrasonic sensors have previously been used for in-line and on-line fermentation monitoring and are increasingly being combined with machine learning models to interpret the sensor measurements. However, fermentation processes typically last many days and so impose a significant time investment to collect data from a sufficient number of batches for machine learning model training. This expenditure of effort must be multiplied if different fermentation processes must be monitored, such as varying formulations in craft breweries. In this work, three methodologies are evaluated to use previously collected ultrasonic sensor data from laboratory scale fermentations to improve machine learning model accuracy on an industrial scale fermentation process. These methodologies include training models on both domains simultaneously, training models in a federated learning strategy to preserve data privacy, and fine-tuning the best performing models on the industrial scale data. All methodologies provided increased prediction accuracy compared with training based solely on the industrial fermentation data. The federated learning methodology performed best, achieving higher accuracy for 14 out of 16 machine learning tasks compared with the base case model.

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

Cited by 4 publications

References 34 publications

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

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