Relevant bibliographies by topics / Precision fermentation

Academic literature on the topic 'Precision fermentation'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Precision fermentation"

1

Laila, Umi, Rifa Nurhayati, Tyas Utami, and Endang Sutriswati Rahayu. "Prediction of Microbial Population in Sorghum Fermentation through Mathematical Models." Reaktor 19, no. 4 (December 31, 2019): 152–61. http://dx.doi.org/10.14710/reaktor.19.4.152-161.

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The mathematical models can be used as a tool in predicting microbial population in sorghum fermentation, either spontaneous fermentation or fermentation with the addition of lactic acid bacteria (LAB) inoculum. Gompertz model modified by Gibson, Gompertz model modified by Zwietering, Baranyi-Robert model, Fujikawa model, Richards model, Schnute model were used in predicting the growth of lactic acid bacteria (LAB) and coliform bacteria during spontaneous fermentation, and also the growth of LAB during fermentation with the addition of inoculum. Meanwhile, there was death (inactivation) of coliform bacteria during sorghum fermentation with the addition of LAB inoculum. The Geeraerd model and the Gompertz model modified by Gil et al. were used to predict the inactivation. The accuracy and precision of models were evaluated based on the Root Mean of Sum Square Error (RMSE), coefficient of determination (R2), and curve fitting. Gompertz model modified by Gibson had the highest accuracy and precision, which was followed by the accuracy of the Fujikawa model and Baranyi-Robert model in predicting the growth of LAB and the growth of coliform bacteria during spontaneous fermentation. Meanwhile, in predicting LAB growth during fermentation with the addition of inoculum, high accuracy and precision was obtained from Richards and Schnute models. In predicting the inactivation of coliform bacteria, Geeraerd model provided higher accuracy and precision compared to Gompertz model modified by Gil et al. Keywords: fermentation; inoculum; mathematical; model; sorghum; spontaneous
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Ginésy, Mireille, Josefine Enman, Daniela Rusanova-Naydenova, and Ulrika Rova. "Simultaneous Quantification of L-arginine and Monosaccharides during Fermentation: An Advanced Chromatography Approach." Molecules 24, no. 4 (February 22, 2019): 802. http://dx.doi.org/10.3390/molecules24040802.

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Increasing demand for L-arginine by the food and pharmaceutical industries has sparked the search for sustainable ways of producing it. Microbial fermentation offers a suitable alternative; however, monitoring of arginine production and carbon source uptake during fermentation, requires simple and reliable quantitative methods compatible with the fermentation medium. Two methods for the simultaneous quantification of arginine and glucose or xylose are described here: high-performance anion-exchange chromatography coupled to integrated pulsed amperometric detection (HPAEC-IPAD) and reversed-phase ultra-high-performance liquid chromatography combined with charged aerosol detection (RP-UHPLC-CAD). Both were thoroughly validated in a lysogeny broth, a minimal medium, and a complex medium containing corn steep liquor. HPAEC-IPAD displayed an excellent specificity, accuracy, and precision for arginine, glucose, and xylose in minimal medium and lysogeny broth, whereas specificity and accuracy for arginine were somewhat lower in medium containing corn steep liquor. RP-UHPLC-CAD exhibited high accuracy and precision, and enabled successful monitoring of arginine and glucose or xylose in all media. The present study describes the first successful application of the above chromatographic methods for the determination and monitoring of L-arginine amounts during its fermentative production by a genetically modified Escherichia coli strain cultivated in various growth media.
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Mančić, Stojan, Bojana Danilović, Marko Malićanin, Sandra Stamenković Stojanović, Nada Nikolić, Miodrag Lazić, and Ivana Karabegović. "Fermentative Potential of Native Yeast Candida famata for Prokupac Grape Must Fermentation." Agriculture 11, no. 4 (April 16, 2021): 358. http://dx.doi.org/10.3390/agriculture11040358.

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The fermentative potential of native Candida famata isolates from wild and cultivated blackberries was evaluated for potential application in Prokupac grape must fermentation. 5 isolates, out of a total 22 isolated yeasts, were identified as C. famata. After the initial screening of fermentative performances, microfermentation was performed in a sterile grape must. Produced samples were analyzed using the HPLC technique. All isolates showed an ability to grow at lower temperatures, good tolerance to 7% ethanol and 300 ppm of SO2. C. famata isolates WB-1, WB-2 and W-5 had similar fermentation performance, but WB-1 isolate was chosen for validation at a laboratory-scale level according to a pleasant, fruity aroma, highest fermentative vigor and power, good organic acid profile and the highest level of ethanol and glycerol produced in micro-vinification experiments. Good enological performance of selected C. famata WB-1 isolate is confirmed by higher level of glycerol, lower level of ethanol and acetic acid in wine samples produced in pure and sequential fermentation, when compared to the control sample. Throughout the selection of C. famata yeasts with good enological potential, this work gives a contribution in the area of precision enology, aiming to find a perfect match between non-exploited yeasts and “autochthonous” grape cultivar Prokupac.
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Aramrueang, Natthiporn, Passanun Lomwongsopon, Sasiprapa Boonsong, and Papassorn Kingklao. "Improved Spectrophotometric Method for Determination of High-Range Volatile Fatty Acids in Mixed Acid Fermentation of Organic Residues." Fermentation 8, no. 5 (April 29, 2022): 202. http://dx.doi.org/10.3390/fermentation8050202.

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Volatile fatty acids (VFAs) are the important intermediates indicating the stability and performance of fermentation process. This study developed the spectrophotometric method for determining high-range VFA concentration in mixed-acid fermentation samples. The performance was compared with the gas chromatography (GC) technique. The calibration curves of the modified method showed linearity over a wide and high concentration range of 250–5000 mg/L for individual C2–C6 VFAs in both linear and branched chains. In order to evaluate the modified method for VFA determination in complex fermentation matrices, fermentation samples produced from acidogenic fermentation of plant materials were spiked with acetic (500–1500 mg/L) and butyric acids (1000 mg/L). The accuracy and precision of the modified method for VFA determination were in the range of 94.68–106.50% and 2.35–9.26%, respectively, comparable to the GC method (94.42–99.13% and 0.17–1.93%). The developed method was applicable to measuring all C2–C6 compounds and VFA concentrations in the fermentation samples and had an acceptable accuracy and precision. The proposed method is analytically reliable and offers significant advantages in the rapid determination of VFAs in mixed acid fermentation of organic residues.
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Kimutai, Gibson, Alexander Ngenzi, Said Rutabayiro Ngoga, Rose C. Ramkat, and Anna Förster. "An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques." Journal of Sensors and Sensor Systems 10, no. 2 (July 2, 2021): 153–62. http://dx.doi.org/10.5194/jsss-10-153-2021.

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Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.
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Schlau, N., L. Duineveld, W. Z. Yang, T. A. McAllister, and M. Oba. "Precision processing barley grain did not affect productivity of lactating dairy cows." Canadian Journal of Animal Science 93, no. 2 (June 2013): 261–68. http://dx.doi.org/10.4141/cjas2012-133.

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Schlau, N., Duineveld, L., Yang, W. Z., McAllister, T. A. and Oba, M. 2013. Precision processing barley grain did not affect productivity of lactating dairy cows. Can. J. Anim. Sci. 93: 261–268. This study evaluated the effects of precision processing (PP; processing based on kernel size) barley grain on ruminal fermentation and productivity of lactating dairy cows. Twenty multiparous Holstein cows, including eight ruminally cannulated cows, were used in a replicated 4×4 Latin square design with 21-d periods. Diets contained light barley grain processed precisely using a narrow roller setting (LB), heavy barley processed precisely using a wide roller setting (HB), processed HB and LB mixed at equal proportions (PP), or equal parts of light and heavy barley grain processed at a single narrow roller setting (CON). All diets consisted of 40% barley grain, 40% barley silage, and 20% of a supplement premix. Comparisons were made between LB and HB to evaluate the effect of barley quality, and between PP and CON to evaluate the effect of precision processing. Dry matter intake, sorting index, ruminal fermentation characteristics, and nutrient digestibility were not affected by diet. In addition, milk yield and concentrations of milk fat, protein, and lactose were not different, although milk urea nitrogen concentration was greater for PP vs. CON and for LB vs. HB. These results suggest that precision processing barley grain based on kernel size may not drastically affect ruminal fermentation and milk production in lactating dairy cows.
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Choi, Sun-Il, Hee-Yeon Kwon, Im-Joung La, Yeon-Hui Jo, Xionggao Han, Xiao Men, Se-Jeong Lee, Yong-Deok Kim, Geum-Su Seong, and Ok-Hwan Lee. "Development and Validation of an Analytical Method for Deacetylasperulosidic Acid, Asperulosidic Acid, Scopolin, Asperuloside and Scopoletin in Fermented Morinda citrifolia L. (Noni)." Separations 8, no. 6 (June 5, 2021): 80. http://dx.doi.org/10.3390/separations8060080.

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Fermentation is a technology that enhances biologically active ingredients, improves the absorption rate and induces the generation of new functional ingredients by the catalytic action of enzyme systems possessed by microorganisms. In this study, changes in the content of five kinds of bioactive compounds (deacetylasperulosidic acid, asperulosidic acid, scopolin, asperuloside and scopoletin) of Morinda citrifolia L. were confirmed by fermentation, and a high-performance liquid chromatography-photodiode array (HPLC-PDA) analysis method for measuring analytes was developed and validated. HPLC method for the determination of five bioactive compounds in Morinda citrifolia L. extracts (MCE) was validated in terms of sensitivity, linearity, selectivity, limit of detection (LOD) and quantification (LOQ), precision and accuracy. The coefficient of determination of the calibration curve for bioactive compounds (1.56–100 μg/mL) showed linearity (R2 ≥ 0.9999). LOD and LOQ were in the range 0.04–0.97 and 0.13–2.95 μg/mL, respectively. The range of intra- and intraday accuracies values (recovery) were 97.5–121.9% and 98.8–118.1%, respectively, and precision value (RSDs) of the bioactive compounds were <4%. In addition, changes in the content of five bioactive compounds in MCE by fermentation were confirmed. These results indicate that the developed fermentation and analysis method could be applied in the development of potential functional food ingredients.
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Hickey, M. C., A. P. Moloney, M. O'Connell, and J. Connolly. "Modification of an in vitro batch culture technique to improve precision in long-term studies." BSAP Occasional Publication 22 (1998): 323–25. http://dx.doi.org/10.1017/s0263967x00033000.

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In vitro techniques have been developed to facilitate the measurement of nutritional variability amongst food. Many kinetic studies have utilized the modified Tilley and Terry technique, with long-term incubations carried out in Erlenmeyer flasks. These are inefficient in utilizing incubator space for large scale studies. However substitution of Erlenmeyer flasks with tubes as fermentation units leaves the system prone to ‘bridging’, the formation of dense mats of forage particles by entrapped gas, above the level of the media in a fermentation unit. The objective of experiment 1 was to establish an effective incubation technique to eliminate the random variation caused by bridging.
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He, Tao, Xiang Hu, Jiu Yin Chen, and Xian Wang. "A Fuzzy Predictive Controlling Method of Fermentation Tank Temperature." Advanced Materials Research 1037 (October 2014): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.248.

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Liquor fermentation is a complex biochemical process, to control the temperature of fermentation tank fastly and accurately in the process can improve the efficiency and quality of fermentation .Because of the nonlinear and time lag of the process, meanwhile the conventional PID control and is difficult to solve practical problems in precise control,So this paper puts forward a fuzzy predictive controlling method, by combining the advantages of fuzzy control and predictive control for big lag, nonlinear fermentation systems.After using MATLAB to make comparative data simulation, the result shows that the proposed design method can better dynamic and static characteristics of both system, with static characteristic small overshoot, fast response, high steady precision, etc, can be useful in industrial control systems.
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Leong, Yi Ying, Chuii Khim Chong, Lian En Chai, Safaai Deris, Rosli Illias, Sigeru Omatu, and Mohd Saberi Mohamad. "Simulation of Fermentation Pathway Using Bees Algorithm." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 1, no. 2 (July 1, 2013): 13–19. http://dx.doi.org/10.14201/adcaij2012121319.

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In this paper, we propose Bees Algorithm (BA) to enhance the performance in estimating the parameters for metabolic pathway data to simulate fermentation pathway for Saccharomyces cerevisiae. However, the parameter estimation of biological processes has always been a challenging task due to the complexity and nonlinear equations. Therefore, we present this algorithm as a new approach for parameter estimation for biological interactions to obtain more accurate parameter values. The result shows that BA outperforms other estimation algorithms as it produces the most accurate kinetic parameters, which contributes to the precision of simulated kinetic model.
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Conference papers on the topic "Precision fermentation"

1

Luo, Fei, Ondrej Halgas, Pratish Gawand, and Sagar Lahiri. "Animal-free protein production using precision fermentation." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/ntka8679.

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The $1.4 trillion animal industry could not sustainably scale further to feed the next billion population, as it is resource intensive, and heavy in greenhouse gas emission. The recent plant-based food movement has provided solution for more sustainable protein sources. However, the plant-based food sector faces challenges in reaching parity in texture, sensory experience (mouthfeel) and nutritional value as animal products, limiting their potential of reaching beyond the vegan and flexitarian consumers. The technical challenge behind this problem is that proteins from plants have intrinsically different amino acid compositions and structures from animal proteins, making it challenging to emulate the properties of animal products using plant-proteins alone. There is a clear and underserved need for novel protein ingredients that can complement plant-based protein ingredients to achieve parity of animal products. Fermentation is considered the third pillar of alternative protein revolution. At Liven, we focus our efforts on developing precision fermentation technology to produce functional protein ingredients that are natural replica of animal proteins. Using engineering biology, we transforms microorganisms with genes that are responsible for producing animal proteins such as collagen and gelatin. The transformed microorganisms are cultivated in fermenters to produce proteins from plant-based raw-materials. Since the protein produced are have identical amino acid sequences and structure as proteins that would be derived from animals, they provide the desired texture and sensory characteristics currently missing in plant-based formulations. For instance, our animal-free gelatin provides the functionality of thermally reversible gel. As our protein ingredients provides functionality and nutrition value of animal proteins, these ingredients could complement plant-based protein ingredients to deliver alt-protein food formulations more accurately emulate animal products, expand the market acceptance of alt-protein foods to mass consumers.
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Jiménez-Márquez, F., J. Vázquez, J. Úbeda, and J. L. Sánchez-Rojas. "High-precision optoelectronic sensor device for monitoring fermentation kinetics and maceration of wine." In SPIE Microtechnologies, edited by Ulrich Schmid, José Luis Sánchez de Rojas Aldavero, and Monika Leester-Schaedel. SPIE, 2013. http://dx.doi.org/10.1117/12.2016935.

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Armenta, Roberto. "Science and commercial evolution of plant-based microbial oils rich in omega-3 fatty acids: An overview." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/nzrm2789.

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Plant-based microbial omega-3 rich oils are successful products in the marketplace, particularly in the field of nutrition, including nutritional supplements, functional ingredients, and concentrates as prescription drugs for treating acute cardiovascular illnesses. Notably, during the last 2 years of the COVID pandemic, interest and demand for plant-based microbial omega-3 products have further increased. The latter augmented also by sustainability challenges facing the traditional source of these fatty acids: fish oil. Research using microalgae as single-cell factories for making oils with omega-3's started decades ago and it has matured as an established industrial microbiology industry via mostly precision fermentation systems. Science and industry are evolving on the type of microorganisms used, including both heterotrophic and phototropic strains, and their respective biological improvements. Also, newer innovation is yielding new oil compositions, containing more than one type of omega-3's and other fatty acids with growing nutritional interests (e.g., omega-7's). This work will present an overview of the science and commercial evolution of plant-based microbial oil products and potential new areas for future innovation.
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