MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A NEURAL SOFT-SENSOR IN AN ELECTRONIC SPREADSHEET

Santos, Brunno F.; Simiqueli, Ana Paula Resende; Ponezi, Alexandre Nunes; Pastore, Gláucia Maria; Fileti, Ana Maria Frattini

doi:10.1590/0104-6632.20180354s20160664

Cited by 7 publications

(3 citation statements)

References 24 publications

(19 reference statements)

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“…This suggests that Bacillus subtilis may have utilized surfactin as a carbon source to help its maintenance and survival in the fermentation. The same behavior was reported by Santos et al (2018). In order to avoid the biosurfactant consumption, Shaligram and Singhal (2010) suggested that batch fermentation for surfactin production should be ended at an appropriate time.…”

Section: Time-course Profile Of Surfactin Productionsupporting

confidence: 63%

Effect of oxygen mass transfer coefficient (kLa) on surfactin production by Bacillus subtilis ATCC 21332

Martins,

Debon,

Schmidell

et al. 2024

Preprint

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Surfactin is one of the most effective biosurfactants available and has several interesting properties. However, the high cost and low yield associated with surfactin production have been major drawbacks preventing its wider industrial application. The use of intensive stirring and aeration, in order to increase surfactin synthesis, can cause excessive foaming, resulting in low levels of biosurfactant production inside the bioreactor. Therefore, the aim of this study was to evaluate the influence of the oxygen mass transfer coefficient (kLa) on surfactin productivity in a bioreactor integrated with a foam collector using low aeration rates. The experiments were carried out at 32°C, using a mineral salts medium containing 10 g/L of glucose, at constant pH 7.0. The results showed that the best surfactin production performance was achieved at kLa = 21.88 h− 1 (with 0.3 vvm aeration and 250 rpm stirring), which resulted in a maximum productivity of 14.1 mg/L.h and a maximum surfactin concentration of 443.1 mg/L in the foam. Despite loss of the culture broth through the exhaust air line, a very high surfactin concentration was reached in the foam phase, which can facilitate downstream processing and purification of the product. Besides, the best results regarding surfactin enrichment and recovery were obtained by using low aeration rate combined with high agitation rate.

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Section: Time-course Profile Of Surfactin Productionsupporting

confidence: 63%

Effect of oxygen mass transfer coefficient (kLa) on surfactin production by Bacillus subtilis ATCC 21332

Martins,

Debon,

Schmidell

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Different renewable feedstocks as substrates in fermentation bioprocesses have been studied. These include rice straw (Panjiar et al, 2020), sugarcane bagasse (Marcelino et al, 2019) corncobs (Sari et al, 2020), corn husk (Saimmai et al, 2018), banana stalks (Asgher et al, 2020), pineapple peels (Vieira et al, 2021), orange peels (Sari et al, 2020), beetroot peels (Dos Santos et al, 2018) and others. Using Achromobacter sp.…”

Section: Discussionmentioning

confidence: 99%

Biosurfactants production by some Aspergilli species under solid state fermentation

El-Halmouch,

Nasr,

Al-Sodany

et al. 2024

Egypt. J. Bot.

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B IOSURFACTANTS are active secondary metabolites producing` by certain microorganisms that have the ability to reduce surface and interfacial tension. They have numerous advantages, such as high biodegradability, increased foaming, low toxicity and performance under challenging conditions. Current study was compared between the potential production of biosurfactant by A. carneus OQ152507, A. niger OQ195934 and A. fumigatus OQ195264 using solid state fermentation (SSF) as a low-cost alternative technology. These isolates were cultivated under SSF using different eight agricultural wastes to produce biosurfactants. The optimized bioprocess conditions for the maximum biosurfactant productivity were potato peels waste (20g/l) as a carbon source at 35ºC and pH 7 in glucose peptone medium for A. carneus OQ152507, sun flower seed shells (28g/l) at 35ºC and pH 7 in starch nitrate medium for A. niger OQ195934 and tea wastes (20g/l) at 30ºC and pH 6 in glucose peptone medium for A. fumigatus OQ195264. Their stability was observed at 120°C, 30g/l of NaCl and alkaline pH 10 for A. carneus and pH9 for A. niger and A. fumigatus biosurfactants. The biosurfactants were identified as glycolipid and phospholipids molecules by Phenol-H 2 SO 4 and phosphate test. GC-MS and FT-IR analysis confirmed that the tested fungi participate in producing three hydrophobic fatty acid chains: Cis-Vaccenic acid, n-Hexadecanoic acid and Hexadecanoic acid 2-hydroxy-1 (hydroxymethyl) ethyl ester in distinct peaks with the highest percentage area. While, each one has at least one specific biosurfactant moiety.

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“…In recent years, the rapid development of artificial intelligence (AI) technology has made the intelligentization of microbial fermentation processes a hot topic (Coşgun et al, 2021; Gao et al, 2021; Santos et al, 2017; Wang et al, 2023). Due to their multidimensionality and complex implicit relationships, microbial fermentation data can be processed using AI technology based on artificial neural networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Enhancing and monitoring spore production in Clostridium butyricum using pH‐based regulation strategy and a robust soft sensor based on back‐propagation neural networks

Xu,

Zhang,

Wang

et al. 2023

Biotech & Bioengineering

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Clostridium butyricum (C. butyricum) is a probiotic that forms anaerobic spores and plays a crucial role in regulating gut microbiota. However, the total viable cell count and spore yield of C. butyricum in industrial production are comparatively low. To this end, we investigated the metabolic characteristics of the strain and proposed three distinct pH regulation strategies for enhancing spore production. In addition, precise measurement of fermentation parameters such as substrate concentration, total viable cell count, and spore concentration is crucial for successful industrial probiotics production. Nevertheless, online measurement of these intricate parameters in the fermentation of C. butyricum poses a considerable challenge owing to the complex, nonlinear, multivariate, and strongly coupled characteristics of the production process. Therefore, we analyzed the capacitance and conductivity acquired from a viable cell sensor as the core parameters for the fermentation process. Subsequently, a robust soft sensor was developed using a seven‐input back‐propagation neural network model with input variables of fermentation time, capacitance, conductivity, pH, initial total sugar concentration, ammonium ion concentration, and calcium ion concentration. The model enables the online monitoring of total viable biomass count, substrate concentrations, and spore yield, and can be extended to similar fermentation processes with pH changes as a characteristic feature.This article is protected by copyright. All rights reserved.

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MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A NEURAL SOFT-SENSOR IN AN ELECTRONIC SPREADSHEET

Cited by 7 publications

References 24 publications

Effect of oxygen mass transfer coefficient (kLa) on surfactin production by Bacillus subtilis ATCC 21332

Effect of oxygen mass transfer coefficient (kLa) on surfactin production by Bacillus subtilis ATCC 21332

Biosurfactants production by some Aspergilli species under solid state fermentation

Enhancing and monitoring spore production in Clostridium butyricum using pH‐based regulation strategy and a robust soft sensor based on back‐propagation neural networks

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