Citric Acid Production by Yarrowia lipolytica NRRL Y-1094: Optimization of pH, Fermentation Time and Glucose Concentration Using Response Surface Methodology

Börekçi, Bilge Sayın; Kaya, Mükerrem; Kaban, Güzin

doi:10.3390/fermentation8120731

Cited by 3 publications

(2 citation statements)

References 35 publications

(35 reference statements)

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“…The issue of citric acid production by yeast has been extensively described in the work by Börekçi et al, “Citric Acid Production of Yeasts: An Overview” [ 69 ].…”

Section: Citric Acid Biosynthesismentioning

confidence: 99%

Citric Acid: Properties, Microbial Production, and Applications in Industries

Książek

2023

Molecules

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Citric acid finds broad applications in various industrial sectors, such as the pharmaceutical, food, chemical, and cosmetic industries. The bioproduction of citric acid uses various microorganisms, but the most commonly employed ones are filamentous fungi such as Aspergillus niger and yeast Yarrowia lipolytica. This article presents a literature review on the properties of citric acid, the microorganisms and substrates used, different fermentation techniques, its industrial utilization, and the global citric acid market. This review emphasizes that there is still much to explore, both in terms of production process techniques and emerging new applications of citric acid.

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“…The issue of citric acid production by yeast has been extensively described in the work by Börekçi et al, “Citric Acid Production of Yeasts: An Overview” [ 69 ].…”

Section: Citric Acid Biosynthesismentioning

confidence: 99%

Citric Acid: Properties, Microbial Production, and Applications in Industries

Książek

2023

Molecules

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“…Y. lipolytica can accumulate lipids and produce citric acid, isocitric acid, organic acids, lactones, erythritol, mannitol, and enzymes such as proteases and lipases [11][12][13]. It produces lipase in the presence of hydrophobic substrates, such as olive oil and oleic acid; nitrogen sources, such as peptone, casein, yeast extract, and tryptone; as well as several other substrates [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Biotechnological Valorization of Cupuaçu By-Products: Solid-State Fermentation for Lipase Production by Yarrowia lipolytica

Carvalho,

Rocha,

Sales

et al. 2023

Fermentation

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Lipases are enzymes that catalyze the hydrolysis of ester bonds of triacylglycerols at the oil–water interface, generating free fatty acids, glycerol, diacylglycerol, and monoacylglycerol, which can be produced from the fermentation of agro-industrial by-products rich in fatty acids, such as cupuaçu fat cake. In this study, Yarrowia lipolytica IMUFRJ50682 was used for lipase production from cupuaçu fat cake in solid-state fermentation (SSF) associated with soybean meal. The 2:1 ratio of cupuaçu fat cake/soybean meal increased the lipase activity of Y. lipolytica via SSF by approximately 30.3-fold compared to that in cupuaçu without supplementation. The optimal conditions for Y. lipolytica to produce lipase were obtained by supplementation with peptone, urea, and soybean oil (all at 1.5% w/v), reaching values of up to 70.6 U g−1. These results demonstrate that cupuaçu fat cake associated with soybean meal can be used for lipase production and adds value to cupuaçu by-products. Furthermore, the proper processing of by-products can contribute to improving the economic viability of the biotechnological processing industry and help prevent the accumulation of waste and environmental pollution.

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Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation

2023

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In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.

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Citric Acid Production by Yarrowia lipolytica NRRL Y-1094: Optimization of pH, Fermentation Time and Glucose Concentration Using Response Surface Methodology

Cited by 3 publications

References 35 publications

Citric Acid: Properties, Microbial Production, and Applications in Industries

Citric Acid: Properties, Microbial Production, and Applications in Industries

Biotechnological Valorization of Cupuaçu By-Products: Solid-State Fermentation for Lipase Production by Yarrowia lipolytica

Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation

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