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/lp/springer-journals/optimization-of-bioethanol-production-from-sugarcane-molasses-by-the-ttom27sy6w
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- Springer Journals
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- Copyright © The Author(s) 2022
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- 1590-4261
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- 1869-2044
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- 10.1186/s13213-022-01706-3
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Abstract
Purpose: Yeast strains tolerant to a wide range of stress conditions are needed for the production of bioethanol from substrates rich in sugar. In our earlier research findings, Meyerozyma caribbica isolate MJTm3 (OM329077) demon- strated remarkable stress tolerance and fermentative activity. The present study aimed to optimize six fermentation parameters to generate conducive fermentation conditions for ethanol production by M. caribbica isolate MJTm3. Method: The response surface method (RSM) based on central composite design (CCD) was employed to optimize process conditions for higher bioethanol yield. The optimization process was carried out based on six independ- ent parameters, namely temperature (25–35 °C), pH (5.5–6.5), inoculum size (10–20% (v/v)), molasses concentration (25–35 (w/v)), mixing rate (110–150 rpm), and incubation period (48–72-h). Analysis of ethanol concentration was done by HPLC equipped with a UV detector. Result: The optimal conditions of the parameters resulting in a maximum predicted ethanol yield were as follows: pH 5.5, an inoculum size of 20%, a molasses concentration of 25 °Bx, a temperature of 30 °C, an incubation period of 72-h, and a mixing rate of 160 revolutions per minute (rpm). Using the above optimum conditions, the model predicted −1 a bioethanol yield of 79%, 92% of the theoretical yield, a bioethanol concentration of 49 g L , and a productivity of −1 −1 0.68 g L h . A batch fermentation experiment was carried out to validate the predicted values and resulted in a −1 bioethanol yield of 86%, 95% of theoretical yield, a bioethanol concentration of 56 g L , and productivity of 0.78 g −1 −1 L h . On the other hand, the surface plot analysis revealed that the synergistic effect of the molasses concentra- tion and the mixing rate were vital to achieving the highest bioethanol yield. These values suggested that the RSM with CCD was an effective method in producing the highest possible output of bioethanol from molasses in actual operation. Conclusion: The study confirmed the potential of using M. caribbica isolate MJTm3 for bioethanol production from sugarcane molasses under the abovementioned optimal fermentation conditions. Keywords: Yeast, Optimization, Response surface method, Fermentation, Molasses, Bioethanol *Correspondence: estifhawaz19@gmail.com Institute of Biotechnology, Addis Ababa University, Addis Ababa, Ethiopia Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Hawaz et al. Annals of Microbiology (2023) 73:2 Page 2 of 15 examples are laser texturing, coatings, mass reduction of Background engine parts, lubricant composition, and use of synthetic The world is under serious pressure due to the limita - oil (Khuong et al. 2017). tion of energy sources, the fluctuation of oil prices, the The following are the main sugar-rich biomasses used nonrenewable nature of fossil fuels, and the decreasing in bioethanol production: (i) sugar-containing raw mate- amount of reserve oil available. On the other hand, the rials, such as sugar beet, sugarcane molasses, whey, increment in greenhouse gas emissions from fossil fuels and sweet sorghum; (ii) starch-containing feedstocks, is another growing concern due to its direct negative such as grains like corn, wheat, and root crops like cas- impact on the environment (Sarris et al. 2014). In addi- sava; and (iii) lignocellulosic biomass: straw, agricultural tion, nations are looking into alternative energy due to waste, crop, and wood residues (Mussatto et al. 2010). their dependence on petroleum-producing countries The global bioethanol production indicates that 60% (Turhan et al. 2015). In this context, biomass-based of bioethanol is produced from biomass that contains energy sources such as bioethanol have emerged as a starch, and the remaining 40% is produced from sug- renewable alternative energy source, because of its eco- arcane and sugar beet (Miguel et al. 2022). Sugarcane friendly nature (Rattanapan et al. 2011), and can be eas- molasses is considered an excellent substrate for bioeth- ily blended with gasoline (Hansen et al. 2005). Liquid anol production (Bouallagui et al. 2013; Shafaghat et al. biofuels, like bioethanol, are widely used in the trans- 2010) due to the presence of high amounts of ferment- portation sector as a mixture of gasoline. Bioethanol able sugars (i.e., glucose, fructose, and sucrose). Molas- has several advantages such as a higher-octane number, ses is also a cheap substrate, noncompetitive with a food higher flame speeds, broader flammability limits, and stock (Campbell and Block 2010), and abundantly avail- increased heat of vaporization compared to gasoline able. It needs less pretreatment during preparation com- (Table 1). Moreover, it is less toxic, readily biodegrad- pared to starchy, cellulosic, and hemicellulose materials able, and produces fewer air pollutants than petroleum (Razmovski and Vučurović 2011). Generally, molasses is ( John et al. 2011). a byproduct of the sugarcane industry and contains most Bioethanol is blended with gasoline at volume percent- of the microbial growth factors, including minerals and ages of 5, 10, and 85% denoted by the fuel names E5-E85. organic nutrients, and it is largely used as a cost-effective The use of mixtures of E-5 and E-10 bioethanol does not feedstock by the ethanol-producing industry (Ghosh require any engine modifications, whereas E-85 bioetha - and Ghose 2003). In Ethiopia, a huge amount of sugar- nol can only be used in flexible fuel vehicles (Bušić et al. cane molasses (542,316 tons/year) is produced annually, 2018). However, using bioethanol as a fuel has several and both public and private bioethanol factories use this drawbacks, including corrosive effects on electric fuel material to produce bioethanol (Gebreegziabher et al. pumps and fuel injectors, issues starting engines in cold 2017). According to Hawaz et al. (2022), the Ethiopian weather, and tribological effects on lubricant charac - bioethanol sector faces serious problems with improper teristics and engine performance. Although there are resource usage and waste disposal concerns as a result of numerous ways to enhance an engine’s performance and low substrate conversion efficiency into the desired etha - lengthen its lifespan by reducing friction and wear, some nol product. Therefore, this problem demands the devel - opment of an urgent bioconversion approach for effective resource usage. The current study was conducted in col - Table 1 The physicochemical specification of gasoline and ethanol laboration with Ethiopian distilleries to develop the best wild-type yeast strain with optimal fermentation condi- Specification Gasoline Ethanol tions and industrially robust ethanologenic properties. Yeasts have been employed for many years to ferment Chemical formula C H (n = 4–12) C H OH n 2n+2 2 5 sugar-rich biomass into bioethanol. Saccharomyces cer- M/(g/mol) 100–105 46.07 evisiae is the most commonly used yeast species for Octane number 88–100 108 bioethanol production since it tolerates a wide range of ρ/(kg/dm ) 0.69–0.79 0.79 stress conditions (Lin et al. 2012). However, the fermen- Boiling point/°C 27–225 78 tative capability of the S. cerevisiae strain is impaired Freezing point/°C −22.2 −96.1 when exposed to a high ethanol concentration, high Flash point/°C −43 13 temperatures, and high osmotic pressure during the Autoignition temperature/°C 275 440 . 3 3 fermentation process (Basso et al. 2008). Therefore, the Lower heating value 10 /(kJ/dm ) 30–33 21.1 ethanol fermentation parameters that affect the fer - Latent vaporization heat/(kJ/kg) 289 854 mentative characteristics of the yeast need to be opti- Solubility in water Insoluble Soluble mized. On the other hand, nonconventional yeasts, such Source: (Yüksel and Yüksel 2004) Ha waz et al. Annals of Microbiology (2023) 73:2 Page 3 of 15 as Wickerhamomyces anomalus, Pichia stipites strain This is due to the rapid consumption by yeast of buffering NRRL-Y-7124, and Kluyveromyces marxianus isolate Kf1 capacity (i.e., amino acids) and the related production of (cited as Kluyveromyces fragilis), were reported as being an acidic material such as organic acids. Furthermore, the stress-tolerant and good ethanol producers (Atitallah pH value of the propagated broth decreased to 3.2 as prop- et al. 2020; Mussatto et al. 2012). We previously investi- agation time exceeded to 24-h, making it more acidic. As a gated a variety of stress-tolerant and fermentative wild- result, the number of viable yeast cells started to decrease. type yeast strains that were isolated from biowaste and Although the molasses propagated media (MPM) has an other byproducts of the Metehara and Fincha sugar fac- acidic pH, the yeast’s cellular morphology is maintained tories in Ethiopia (Hawaz et al. 2022). Among the multi- (Fig. 2a and b). This is consistent with our earlier findings, stress-resistant yeast strains, the nonconventional yeast which stated that M. caribbica MJTm3 was found to be isolate Meyerozyma caribbica MJTm3 (accession number tolerant to acidic pH in YPD broth (Hawaz et al. 2022). OM329077, National Center for Biotechnology Informa- Regarding the reduction of molasses concentration in tion (nih. gov)) was found to be ethanol tolerant (20%), function to cell viability and incubation period, the Brix osmotolerant (50% (w/v)), and temperature tolerant (45 was decreased to 4.25 °Bx, 4.5 °Bx, 5.5 °Bx, and 6.25 °Bx −1 °C) and able to produce bioethanol (42 g L ) from highly from 8 °Bx, 10 °Bx, 12 °Bx, and 14 °Bx, respectively, during concentrated sugarcane molasses (Hawaz et al. 2022). a 24-h propagation period. Consequently, viable yeast cell 8 8 The process of bioethanol fermentation is influenced values of a density of 4.26 × 10 cells/mL, 8.75 × 10 cells/ 8 7 by several factors that affect a series of biochemical reac - mL, 9.52 × 10 cells/mL, and 2.1 × 10 cells/mL were pro- tions involved. Determining the optimum conditions duced (Fig. 1a, b, c, and d, respectively). In this experiment, (pH, temperature, substrate concentration, inoculum the molasses concentration was reduced by half after 24 h volume, mixing rate, duration of the fermentation, etc.) of propagation, yet lesser viable yeast cells were counted at is a crucial step during bioethanol production. The use of 14 Bx, and this is due to the cause of longer propagation Design-Expert is widely applied to identify and optimize duration. The propagated yeast density under different the key process variables in order to improve system- molasses concentration can be seen in Fig. 3. Even though atically the concentration of the aimed product (Jargal- changes in pH and cell density were observed until the end saikhan and Saraçoğlu 2008; Uncu and Cekmecelioglu of the propagation time, the molasses concentration (Brix 2011). Consequently, the response surface methodology content) stabilized after 20-h propagation time. (RSM) is chosen as a statistical method that provides a rapid assessment of the key operating factors in which a Model validation and optimization of fermentation response parameter is influenced by several independ - parameters ent factors (El-Gendy et al. 2013). Thus, the present The aim of this study was to maximize the yield of bioetha - study aimed to optimize the fermentation parameters to nol produced from sugarcane molasses by optimizing the enhance bioethanol production from sugarcane molas- conditions of the fermentation parameters essential for ses by using the locally isolated Meyerozyma caribbica efficiently converting all of the available fermentable sugar MJTm3 in a shaker flask using an RSM-based central to ethanol. The complete design matrix of the independ - composite design (CCD). ent variables in actual values corresponding with predicted responses of the bioethanol yield is presented in the sup- plementary data. A second-order quadratic model equation Results and discussion was generated based on the experimental data and CCD, Propagation characteristics of M. caribbica isolate MJTm3 and this indicated linear, interaction, and quadratic effect of To achieve a maximal yeast concentration for effective con - variables on bioethanol yield as (+ve) or (−ve) under Eq. 1. version of the substrate to ethanol, the yeast cells have been The quadratic equations had high regression coefficients, propagated through four phases that each contain vary- and the lack of fit values was insignificant (p > 0.05), dem- ing concentrations of molasses (viz., 8, 10, 12, and 14 °Bx). onstrating that the quadratic models fit the data well. Overall, the findings showed that the yeast cell number increased exponentially up to 12 °Bx but started to decline Y =+ 24.30 + 0.4401A + 0.6653B + 0.4703C − 3.41D + 4.00E + 2.47F sharply after 14 °Bx. The results revealed that the yeast cell 2 2 2 2 2 2 − 3.54A + 0.3438B + 0.8547C + 1.04D + 0.9509E + 1.21F population increased by 41, 51, and 8% at 8 °Bx, 10 °Bx, and + 1.90AB − 3.68AC + 0.7165AD + 0.8818AE − 1.13AF 12 °Bx, respectively, after 24-h propagation time. + 1.67BC + 0.3925BD − 0.9626BE + 1.12BF (1) The pH value showed a decreasing trend when the prop - + 0.1981CD + 0.4089CE + 2.27CF agation period was extended to 24-h. Specifically, the pH − 1.53DE − 0.6768DF demonstrated decreasing dynamics from 5.5 to 3.5, 3.6, and 3.5, at 8 °Bx, 10 °Bx, and 12 °Bx, respectively after a − 0.4527EF 24-h propagation time (Fig. 1a, b, c, and d, respectively). Hawaz et al. Annals of Microbiology (2023) 73:2 Page 4 of 15 Fig. 1 The dynamics of yeast cell propagation as a function of pH and incubation period at 8 °Bx (a), 10 °Bx (b), 12 °Bx (c), and 14 °Bx (d). A maximum yeast cell density of 9.52 × 10 cells/mL was achieved at 12 °Bx molasses concentration after 24h Fig. 2 The cellular morphology of M. caribbica isolate MJTm3 photographed at 100× magnification with a bright field microscopy while being exposed to pH 5.5 (a) and 3.2 (b) after 24-h where Y is the bioethanol yield (%) and a positive sign The statistical significance of the quadratic model was indicates a synergetic effect, whereas a negative sign indi - determined using analysis of variance (ANOVA). The sta - cates an antagonistic effect. tistical significance was controlled by F-test and p-values, Ha waz et al. Annals of Microbiology (2023) 73:2 Page 5 of 15 Fig. 3 M. caribbica isolate MJTm3 propagation at different concentrations of molasses propagation medium (MPM) containing 8 °Bx (phase 1), 10 °Bx (phase 2), and 12 °Bx (phase 3) at 24-h propagation time and the model was found to be highly significant at a greater than four (R > 4) and an adequate accuracy value 95% confidence level (Eq. 5), with an F-value of 65.48 of 38.42 were found using ANOVA measurement. The and a very low probability (p < 0.0001) (Table 2). This current model can therefore be reliable and employed indicated that there is less than 0.01% chance that this to navigate the design space. This is in agreement with error is caused by noise. The significance of the current Hamouda et al. (2015), who obtained an experimental model agrees with results of Hamouda et al. (2015) who ethanol yield of 8.20–41.4% with the corresponding pre- developed a model for optimization ethanol production dicted values ranging from 9.26–39.1%, respectively, with parameters with statistically significance at a 95% confi - an adequate signal of 17.1. On the other hand, the cur- dence level, an F-value of 29.1, and a very low probabil- rent developed model indicated that the lack of fit (F = ity of p < 0.0001 with < 0.01% chance that of which error 0.0525) was found significant relative to the pure error. caused by noise. The actual and predicted values, as well as the normal The model fitting reliability was evaluated using R and plot of experimental design residuals, which are shown adjusted R and found to be 0.9682 and 0.9534, respec- in Fig. 4a and b, further supported the aforementioned tively (Table 2). These values indicated that approxi - ANOVA analysis. A plot of the predicted and experimen- mately 96.82% of the variability in the response obtained tal values of the bioethanol yield is shown in Fig. 4a. The is explained by the model ensuring perfect fit of the plot showed a strong correlation (R = 0.9682) between model to the observed data. This is in line with results the experimental and predicted data, demonstrating that from Hamouda et al. (2015), the R value 0.953, which the model accurately predicted the bioethanol produc- demonstrated the high significance of the model with tion within the experimental range under consideration. 95.3% of confidence for bioethanol production from sug - This demonstrates that the experimental outcomes were arcane molasses. Cavalaglio et al. (2016) have developed a largely consistent. The residuals for the bioethanol pro - model fitted with R equal to 0.970 with 97% significance duction are normally distributed on a normal plot, as for optimization of ethanol production from a cellulosic shown in Fig. 4b, with results extremely closely spaced to substrate. Flayeh (2017) has designed a model that fitted a straight line with no substantial departure. with a slightly lower confidence of 90.25% compared to In the current study, the temperature (A), pH (B), the present reported values. inoculum size (C), molasses concentration (D), mixing The regression model’s suitability between the experi - rate (E), and incubation period (F) were selected as key mental and predicted data of the response parameter factors to maximize the bioethanol yield (%) using CCD. suggests a reasonable correlation over the tested experi- Results revealed that all linear and interaction factors, mental ranges (Table 2). According to the current analy- except for temperature (A), inoculum size (C), pH and sis, a ratio of 38.42 adequate signals (Eq. 4) was achieved molasses concentration (BD), inoculum size and molas- for the CCD consisting of 86 trials, for which the overall ses concentration (CD), inoculum size and mixing rate experimental bioethanol yield ranged between 8.41 and (CE), and mixing rate and incubation period (EF), were 46.59% and corresponding predicted value of 8.28 to significant at the 95% confidence level. The independent 39.98%, respectively. An adequate signal-to-noise ratio of factors, such as linear (B, D, E, F), interactive (AB, AC, Hawaz et al. Annals of Microbiology (2023) 73:2 Page 6 of 15 Table 2 Analysis of variance for response surface of quadratic model for the CCD experiments Source SS Df MS F-value P-value Model 4731.99 27 175.26 65.48 < 0.0001 Significant A 13.39 1 13.39 5.00 0.0292 B 30.96 1 30.96 11.57 0.0012 C 15.29 1 15.29 5.71 0.0201 D 802.05 1 802.05 299.65 < 0.0001 E 1101.06 1 1101.06 411.36 < 0.0001 F 422.51 1 422.51 157.85 < 0.0001 AB 230.10 1 230.10 85.97 < 0.0001 AC 866.95 1 866.95 323.90 < 0.0001 AD 32.85 1 32.85 12.27 0.0009 AE 49.76 1 49.76 18.59 < 0.0001 AF 81.03 1 81.03 30.27 < 0.0001 BC 178.25 1 178.25 66.60 < 0.0001 BD 9.86 1 9.86 3.68 0.0599 BE 59.31 1 59.31 22.16 < 0.0001 BF 79.90 1 79.90 29.85 < 0.0001 CD 2.51 1 2.51 0.9383 0.3367 CE 10.70 1 10.70 4.00 0.0503 CF 328.57 1 328.57 122.76 < 0.0001 DE 150.18 1 150.18 56.11 < 0.0001 DF 29.31 1 29.31 10.95 0.0016 EF 13.12 1 13.12 4.90 0.0308 A 190.47 1 190.47 71.16 < 0.0001 B 2.79 1 2.79 1.04 0.3117 C 11.11 1 11.11 4.15 0.0462 D 16.45 1 16.45 6.15 0.0161 E 12.50 1 12.50 4.67 0.0348 F 21.44 1 21.44 8.01 0.0064 Residual 155.24 58 2.68 Lack of fit 145.56 49 2.97 2.76 0.0525 Not significant Pure error 9.68 9 1.08 Corrected total 4887.23 85 2 2 R-squared (R ), 0.9682; adjusted R , 0.9534; adeq. precision, 38.42 SS Sum of squares, DF Degree of freedom, MS Mean square AD, AE, AF, BC, BE, BF, CF, DE, and DF), and quad- and incubation period (h) were considered significant ratic (A ) were found significant model terms (p < 0.05), variables (p < 0.0001). The mixing rate revealed the high - whereas all quadratic variables were showed insignifi - est positive impact on the bioethanol yield (%), followed cant (p > 0.05), except for temperature (A ) (p < 0.0001) by the incubation period. This suggests that increasing (Table 2). This is in agreement with Kamal et al. (2021) the mixing rate and incubation time beyond their preset who reported that linear pH, interactive inoculum size, values will increase the bioethanol yield. Other fermenta- and pH were significant model terms for efficient bioeth - tion factors, i.e., inoculum size, initial pH, and tempera- anol production from sugarcane molasses. ture, showed a slightly positive impact on the bioethanol The Pareto chart was plotted to highlight the most sig - production (p = 0.0201, 0.0012, and 0.0292, respectively). nificant independent variables and their main and inter - In the present study, molasses concentration exerted an action effects on bioethanol production (Fig. 5). The adverse negative impact (p < 0.0001) on the ethanol pro- effect of each independent parameter on bioethanol pro - duction process, while its quadratic effect demonstrated duction was confirmed by the coefficient of the quadratic the highest positive impact on ethanol fermentation (p = equation (Eq. 1). As a result, linearly, mixing rate (rpm) 0.0161). The quadratic effects of the incubation period, Ha waz et al. Annals of Microbiology (2023) 73:2 Page 7 of 15 Fig. 4 Predicted versus actual values (a). Normal probability plot versus standardized residuals (b) Fig. 5 Pareto chart showing the linear, interactive, and quadratic effects of different factors on the response bioethanol yield (Y ) E/S initial molasses concentration, mixing rate, and inoculum incubation period (p < 0.0001) > temperature and mix- size showed a positive impact on the fermentation pro- ing rate (p < 0.0001) > temperature and initial molasses cess in declining order (p = 0.0064, 0.0161, 0.0348, and concentration (p = 0.0009) > inoculum size and mix- 0.0462, respectively). On the other hand, the incubation ing rate (p = 0.0503) > initial pH and initial molasses temperature showed the highest negative quadratic effect concentration (p = 0.599) > inoculum size and initial (p < 0.0001) on the bioethanol yield. molasses concentration (p = 0.3367). The negative The positive interactive effect of all the parameters interactive effect of the investigated parameters ranked within the studied range of the experiment ranked in in the following decreasing order: temperature and declining order: inoculum size and mixing rate (p < inoculum size (p < 0.0001) > initial molasses concen- 0.0001) > temperature and initial pH (p < 0.0001) > ini- tration and mixing rate (p < 0.0001) > temperature and tial pH and inoculum size (p < 0.0001) > initial pH and incubation period (p < 0.0001) > initial pH and mixing Hawaz et al. Annals of Microbiology (2023) 73:2 Page 8 of 15 rate (p < 0.0001) > initial molasses concentration and by M. caribbica isolate MJTm3. To elucidate the opti- incubation period (p = 0.0016) > mixing rate and incu- mum condition of each factor for a maximum bioethanol bation period (p = 0.0308). yield (%) production, thee-dimensional response surfaces (3D) were plotted based on the predicted second-degree Surface plot analysis polynomial equation (Fig. 6). An extensive experimental trial was conducted over the According to the response surface plots for bioetha- considered range of 86 experimental runs to determine nol yield as a function of temperature and molasses con- the optimum operating value for the factors that will centration in Fig. 6a, lowering the temperature from 35 maximize the bioethanol yield from sugarcane molasses to 30.25 °C and the molasses concentration from 35 to Fig. 6 The 3D response surface plots of ethanol yield of molasses concentration and temperature (a), mixing rate and temperature (b), mixing rate and molasses concentration (c), incubation period and temperature (d), incubation period and molasses concentration (e), and incubation period and mixing rate (f). Green, yellow, and red colors represent lower, medium, and higher levels of ethanol yield, respectively Ha waz et al. Annals of Microbiology (2023) 73:2 Page 9 of 15 25.13 °Bx resulted in an increase of the bioethanol yield. at 25.19 °Bx and 149.67 rpm, which is because the yeast At 34.90 °Bx and 25.05 °C, a lower bioethanol production cells are in contact with vital nutrients, like sugars, which of 17.40% was observed, but a higher bioethanol yield resulted in an effective bioethanol yield (Kopsahelis of 28.70% was shown at 25.06 °Bx of the molasses and et al. 2007). Moreover, at 25.25 °Bx and 147.32 rpm, the 29.78 °C. There is a negative correlation between molas - bioethanol output started to decrease. ses concentration and bioethanol yield, meaning that a Figure 6d shows the negative interactive effect of decrease in molasses concentration will result in higher temperature and incubation period on bioethanol pro- bioethanol yield. The yield began to decrease at 25.09 °Bx duction. Results showed that the yield increased with a and 29.96 °C, which is because high temperatures and decrease of the incubation temperature from 35 to 29.86 high substrate concentration have a negative effect on °C, and a slight decrease in the incubation period from 72 the fermentation capacity of the yeast cells (Cazetta et al. to 71.78 h. At 29.86 °C and a 71.78 h incubation period, a 2007). Low bioethanol yield signified that the yeast was maximum yield of 27.85% ethanol was produced. How- sensitive to the inhibitory compound present in the fer- ever, at 25.05 °C and 48.19 h, the lowest bioethanol yield mentation medium. Moreover, the enzymes that regulate of 18.04% was measured. An earlier study using Saccha- the fermentation process are sensitive to high tempera- romyces species indicated that fermentation at 31 °C for tures which can result in the denaturation of their ter- 72-h produced 26.4% of bioethanol yield (Flayeh 2017). tiary structure (Phisalaphong et al. 2006). According to According to Zabed et al. (2014), a longer fermentation Liu and Shen (2008), the optimal operating temperature period is required to recover a high ethanol yield with the for free fermenting yeast cells was near 30 °C. It has been highest productivity using a batch fermentation system. shown that increasing the initial molasses concentra- This suggests that the use of a short fermentation time tion to 25 °Bx resulted in a maximum bioethanol yield of and a short incubation temperature cause inefficient eth - 25% (Hamouda et al. 2015). In agreement with our find - anol fermentation due to inadequate growth of microor- ing, Morimura et al. (1997) reported the highest ethanol ganisms (Zabed et al. 2014). yield and ethanol concentration at an optimum of 25 °Bx Figure 6e illustrates the interaction effect of molasses molasses concentration by a yeast strain K211 of Saccha- concentration and incubation time on the bioethanol romyces species. yield. The plot showed that ethanol production increased A positive interacting effect between temperature and as the incubation period was extended from 48.16 to mixing rate was seen for the bioethanol yield Fig. 6b. The 71.99-h, while the concentration of molasses decreased bioethanol yield increased with an increase of the mixing from 35 to 25.10 °Bx. At 48.16-h of incubation time and rate from 111 to 149.77 rpm and temperatures ranging 25.11 °Bx, a lower ethanol yield of 26.71% was obtained. from 25 to 30 °C. Results showed that at 110 rpm and 25 A maximum bioethanol yield of 32.98% was recorded °C, a bioethanol production of 18.20% was obtained. At at 25.10 °Bx and 71.99-h of molasses concentration and a mixing rate of 149.77 rpm and a temperature of 30 °C, incubation period, respectively. When the fermentation a maximum yield of 29.26% of bioethanol was observed. flask was overloaded with the substrate, a continuous When the mixing speed and temperature reached 30.2 °C fermentation rate caused the yeast cells to experience and 149.83 rpm, respectively, the bioethanol production osmotic shock that has an inhibitory effect on the yeast started to decline. It is obvious that as the agitation rate (Azhar et al. 2017; Cavalaglio et al. 2016). The results increases, the diffusion of the necessary nutrient from revealed that the production of bioethanol started to the fermentation broth to the yeast cells is increased. decline immediately after 72-h and 26 °Bx of incubation However, at the same time, this also increased the release time and molasses concentration, respectively. of ethanol from the cells to the fermentation broth. The A positive interactive effect of mixing rate and incuba - ideal agitation rate for ethanol fermentation by yeast cells tion time on the production of bioethanol from sugar- was reported as 150–200 rpm (Zabed et al. 2014). In the cane molasses is demonstrated in Fig. 6f. The production current situation, the fermentation broth was efficiently of bioethanol improved by increasing the mixing rate mixed and distributed when the agitation rate increased from 110.43 to 149.94 rpm and the incubation time from ≈150 rpm. 48.15 to 71.96-h. A lower bioethanol yield of 19.60% was Figure 6c demonstrates the negative interactive effect shown at 48.15-h and 110.43 rpm. However, a maximum of molasses concentration and mixing rate for bioetha- bioethanol production of 32.45% was produced at 149.94 nol production from molasses. The bioethanol yield rpm and 71.96-h of mixing rate and incubation time, increased with an increase in mixing rate from 110.19 respectively. The plot also demonstrated that the bioetha - to 149.67 rpm and a decrease of the molasses concentra- nol yield started to decrease at 147.92 rpm and 70.89-h tion from 35 to 25.18 °Bx. This finding revealed that the of mixing rate and incubation time, respectively. Fur- maximum bioethanol yield of 34.84% was demonstrated thermore, when the incubation period reached 72-h, the Hawaz et al. Annals of Microbiology (2023) 73:2 Page 10 of 15 production of bioethanol started to decline. This might specified above. Results showed that, during the fermen - be due to ethanol oxidation, organic acid formation, and tation process, the pH value slightly declined from 5.5 to substrate depletion in the fermentation broth that could 5.27 at 24-h. However, after 48-h of the incubation period, potentially accelerate the deactivation of enzymes and the pH returned back to the optimal condition (i.e., 5.5) thereby lowering both ethanol yield and yeast cell viabil- without adjustment and was kept constant until comple- ity (Kopsahelis et al. 2007). Low ethanol yield may also tion of the ethanol fermentation process, which might occur due to the formation of secondary byproducts that be due to the production of enzymes and other chemi- limit ethanol productivity (Ergun and Mutlu 2000; Ham- cals required for adaptation to the new environment to ouda et al. 2015). facilitate the overall ethanol fermentation process. On the other hand, molasses might exhibit a buffering effect Fermentation under optimum conditions attributed to its acid composition (weak acids and amino Ethanol fermentation parameters, such as pH, inocu- acids) and phosphates that would regulate the pH values lum size, molasses concentration, temperature, mixing to 3-5 and 6-7, respectively (Cazetta et al. 2007). rate, and incubation period, were optimized in the above In the present study, the key process variables were described experiments and applied to evaluate the relia- experimentally supported to produce a maximum −1 bility of the model equation using batch fermentation. In bioethanol concentration of 56 g.L with a bioethanol the maximum predicted bioethanol concentration of 49 yield of 86% and a percent theoretical yield of 95% from −1 g L, bioethanol yield of 78.6% was obtained under the the 25 °Bx molasses concentration within 72-h in Fig. 7d. predicted optimal conditions of pH 5.5, 20% inoculum Figure 7b and c shows the chromatogram of the internal size, 25 °Bx initial molasses concentration, 30 °C tem- ethanol standard at 30% dilution and the highest ethanol perature, 72-h incubation period, and 159 rpm with the concentration throughout all experimental runs, respec- desirability of 1.0. tively. The overview of the fermentation steps under Fermentation was conducted using a bioreactor with a optimum fermentation conditions is demonstrated in working volume of 5 L to validate the predicted optimal Fig. 8. Hamouda et al. (2015) obtained a bioethanol con- −1 conditions in the actual experiment using the parameters centration and bioethanol yield of 32.32 g.L and 44%, Fig. 7 Standard ethanol calibration curve (a), standard ethanol concentration chromatogram at 30% dilution (b), maximum ethanol concentration chromatogram among experimental runs (c), and validation experiment under optimum operating conditions (d) Ha waz et al. Annals of Microbiology (2023) 73:2 Page 11 of 15 Fig. 8 Overveiw of complete set experiments under optimum fermentation conditions obtained from response surface methodology (RSM) based −1 on the central composite design (CCD). Under these optimum condition, a bioethanol concentration 56 g.L and a bioethanol yield of 86% were produced Pretreatment of molasses respectively, from sugarcane molasses using Pichia vero- Raw molasses was pretreated with 99.8% sulfuric acid nae strain HC-22 during 60-h fermentation time. The (H SO ) and heated at 90 °C for 2-h (Zohri et al. 2022) to bioethanol yield in our study was found higher due to 2 4 remove unwanted particles, dirt, and retarding microbial the differences in fermentation period, inoculum size, contaminants (De Vasconcelos et al. 2004; Malik 2016; fermenting microorganisms, and pretreatment meth- Rahman et al. 2013). To achieve the desired Brix level, ods. Over all, the current study suggests that the process various amounts of raw molasses were diluted with dis- optimization using RSM was applicable to optimize the tilled water. In agreement with Hawaz et al. (2022), for bioethanol fermentation reliably from molasses by M. yeast cell propagation, the concentration of molasses caribbica isolate MJTm3. was adjusted to 8, 10, and 12 °Bx, while it was diluted in accordance with the design matrix for ethanol fermenta- tion. Diammonium phosphate (DAP) was added to the Materials and methods medium and homogenized with a magnetic stirrer at Feedstock collection −1 the optimum concentration of 4 g L . Finally, the pH For testing the bioethanol fermentation of the yeasts, 20 was adjusted using 99.8% H SO and autoclaved at 121 L of raw and fresh concentrated sugarcane molasses (63 2 4 °C for 15 min in line with the design matrix. The treated °Bx) was obtained from the molasses storage tanker at medium was standing overnight under laminar air flow the Fincha Sugar Factory using sterile plastic containers for decantation (Arshad et al. 2008). The clear upper sus - on March 2020. Prior to collection, the automated agita- pension of the broth was then carefully poured into a tor was used to homogenize the biomass. Fincha Sugar newly sterile fermentation flask under the safety cabinet. Factory is located in Wollega province in West-Central Ethiopia (8° 31′ N 39° 12′ E) that has a humid subtropical climate with average annual temperatures of 31 °C. The biomass was immediately transported in an icebox to the Medium preparation Fermentation Laboratory, Department of Biotechnology, For inoculum preparation, yeast extract peptone dex- −1 Addis Ababa Science and Technology University. After trose (YPD) broth supplemented with 100 mg L chlo- −1 arrival at the laboratory, the biomass was kept at room ramphenicol was utilized with a composition of (10 g L −1 −1 temperature for 24-h in tightly closed containers for fur- yeast extract, 20 g L peptone, 20 g L dextrose, and 20 −1 ther propagation and fermentation use.g L agar) (Ramos et al. 2013). The pH of the YPD broth Hawaz et al. Annals of Microbiology (2023) 73:2 Page 12 of 15 was adjusted to 5.5 using 1N HCl and/or 1N NaOH and and an alcohol concentration between 3 and 4% (v/v) sterilized at 121 °C for 15 min. The strain was maintained were selected (Mukhtar et al. 2010). at 4 °C on YPD agar slants. To prepare the propagation and fermentation medium, concentrated molasses were diluted with various Batch fermentation amounts of distilled water to reach the desired Brix level A batch fermentation was carried out in 1000 mL Erlen- (degree of dissolved soluble solids in water). The pH of meyer flasks containing different concentrations (degree the propagation medium was then adjusted to 5.5 using Brix) of molasses and pH that were adjusted in accord- 99.8% of concentrated sulfuric acid (H SO ) (De Vas- 2 4 ance with the experimental design (supplementary concelos et al. 2004), while the pH of the fermentation data). The molasses fermentation medium (MFM) was medium was adjusted in accordance with the set experi- inoculated with propagated yeast cells as an inoculum mental conditions (supplementary data), and sterilization as per the experimental design specification. Bioethanol at 121 °C for 15 min was then performed. fermentation was performed under a shaker incubator regulated at different shaking speeds and temperatures according to the experimental conditions established in Culture source, inoculum preparation, and propagation the complete design matrix. Samples for analysis were In this study, Meyerozyma caribbica isolate MJTm3, a wild taken according to the prescribed incubation period (h), indigenous yeast with the accession number OM329077, −1 and the ethanol concentration (g L ) of each experimen- was used from the culture collection at Department of tal run was quantified using high-performance liquid Biotechnology, Addis Ababa Science and Technology Uni- chromatography (HPLC) equipped with a UV detector versity, Ethiopia. This yeast was previously isolated from (Duarte et al. 2009). the Metehara sugar factory’s sugarcane mill juice tanker three denoted as MJTm3 and was used in this experimen- tal study because it demonstrated a remarkable tolerance Quantitative estimations to ethanol (20% of ethanol), sugar (50% (w/v) of glucose), The total reducing sugar (TRS%) and residual sugar con - and temperature (45 °C) (Hawaz et al. 2022). tent (RS%) of treated molasses and the fermented broth M. caribbica isolate MJTm3 was refreshed from the samples were determined using the 3,5-dinitrosalicylic stock culture in 10 mL of yeast extract peptone dextrose acid method (DNS) (Miller 1959) at Wonji Research (YPD) broth at pH 5.5 and incubated at 30 °C for 24 h in and Development Center, Ethiopian Sugar Corporation, a rotary shaker incubator operating at 150 rpm (Flayeh Ethiopia. The ethanol concentration analysis was carried 2017). The yeast cells were harvested by centrifuging at out at the Department of Food Engineering, Addis Ababa 5000 g and resuspended in a sterile 10 mL of 1% peptone Science and Technology University, Ethiopia. In order water followed by a second centrifugation. The harvested to accomplish a standard calibration curve, different cells were resuspended in 10 mL of 1% peptone water volumes of the internal standard were diluted with the and used as an active culture for inoculation (Boboye and standard solution (acetone) in a 2 mL vial. Six different Dayo-Owoyemi 2009). concentrations (5, 10, 15, 20, 25, and 30%) were prepared Inoculum propagation was carried out in 250 mL of (Fig. 7a). The same percentage of the internal standard sterilized and cotton plugged Erlenmeyer flasks. An solution for sample preparation was added (Mohammed amount of 1 mL of active viable yeast cells (2.51 × 10 et al. 2018). cells/mL) was inoculated into the sterilized molasses −1 The ethanol concentration (g L ) was measured by propagation medium (MPM) adjusted at four concentra- −1 high-performance liquid chromatography (HPLC) (1200 tion (8, 10, 12, and 14 °Bx). Finally, 4 g L of diammo- Series Agilent HPLC, Germany) equipped with an UV nium phosphate was added to the fermentation medium detector at 280 nm (model Agilent 1260 infinity, Ger - (Hawaz et al. 2022). Propagation of M. caribbica isolate many) and Spherisorb Amino (N H ) Cartridge column MJTm3 was carried out under vigorous shaking at 150 2 (pore size 80 A, inner diameter 4.6 mm, length 250 mm, rpm and 30 °C for 24-h. and particle size 5 𝜇m, Waters, Germany). The mobile Fermented samples were collected every 6-h for 24-h phase was acetonitrile and water (70:30 (v/v)), the flow to measure the Brix, alcohol content (v/v), residual sugar rate was 0.25 mL/min, and the sample injection volume (RS%), pH, and cell viability of the propagated medium. was 10 𝜇l with a column temperature of 25 °C. Estima - These measurements were made using a refractometer tion of bioethanol yield (Y ), volumetric ethanol pro- (ATAGO densimeter model 2312; ATAGO Co., Ltd., E/S ductivity (P ), and percent of theoretical ethanol yield Tokyo, Japan), a pH meter, ebulliometer, and a hemocy- V was determined using Eqs. 2–4 as described by Hamouda tometer, respectively. For molasses fermentation, propa- et al. (2015) and Laopaiboon et al. (2009). gated cultures containing 3.0 × 10 cells/mL, RS% 3.0%, Ha waz et al. Annals of Microbiology (2023) 73:2 Page 13 of 15 independent factors and optimize the fermentation vari- bioethanol concentration g L − 1 Y (%) = × 100 (2) E∕S ables with a maximum bioethanol yield. The experimen - total sugar utilized g L − 1 tal data were fitted to the respective response variable’s second-order polynomial equation as follows: actual ethanol content (g) % of theoretical yield = × 100 5 5 6 i=1 theoretical ethanol content (g) (3) Y = βo + β x + β x x + β x i i ij i j ii i=1 i=1 j=i+1 6 maximum ethanol concentration gL − 1 PV g∕L∕h = (4) (5) fermentation time (hr) where Y is the bioethanol yield (%) and βo the value of the center point. βi, βij, and βii are the linear, interactive, Experimental design and quadratic coefficients, respectively, and xi and xj are The RSM based on CCD was used to optimize the the independent factors. bioethanol fermentation parameters from molasses by the stress-tolerant M. caribbica isolate MJTm3. Fer- Fermentation under optimum conditions mentation factors which exerted a significant effect on A validation experiment was conducted under the opti- the ethanol percentage (v/v) were selected based on the mal fermentation conditions obtained from the response data obtained through the single factor experiment, vary- surface plot. After adjusting the pH to 5.5, the molasses ing one variable at a time optimization. Design-Expert fermentation medium (MFM) was sterilized at 121 °C 12.0 (State-Ease, Inc., Minneapolis, USA) was employed for 15 min and allowed to stand overnight to cool down to generate experimental runs using CCD with two lev- to room temperature and sedimented unnecessary con- els (+1 and −1) for six independent factors (Table 3). In stituents suspended in the molasses fermentation broth addition, this model was used to develop regression and (Arshad et al. 2008). The clear suspension from the upper graphical analysis of the experimental data. Analysis of part was transferred aseptically and inoculated with the variance (ANOVA) was applied to evaluate the key con- previously optimized inoculum size of 20% (v/v) into a tribution and significance of each variable to the bioetha - bioreactor (ECMA-C20604RS, Taiwan) with a working nol yield (Cavalaglio et al. 2016). volume of 5 L containing 25 °Bx of molasses concentra- The model fit statistics described by the equation was tion. Initially, the yeast cells were propagated at aeration confirmed by regression model analysis. RSM was used rate of 3.0 vvm for 24-h (Chang et al. 2018). The pH and to identify the optimal operating conditions of each inde- dissolved oxygen were measured by a pH meter and oxy- pendent variable for ethanol production (Bezerra et al. gen electrode, respectively. The fermentation process was 2008). According to the CCD, two levels corresponding carried out anaerobically at 29 °C and 160 rpm for 72-h. to low (−1) and high (+1) were used for each experimen- −1 Finally, the ethanol concentration (g L ) was deter- tal variable, namely temperature (A), initial pH (B), inoc- mined using HPLC according to the previously described ulum size (C), initial molasses concentration (D), mixing method under Quantitative estimations section. rate (E), and incubation period (F) (Table 3). The central point was replicated 10 times for a total of 86 experi- Data analysis mental runs (supplementary data). In the present study, The significance of each fermentation parameters was bioethanol yield was selected as a response parameter analyzed using ANOVA analysis under central composite during optimization of ethanol fermentation parameters. design (CCD). The significant difference in the variables A response surface experiment was done to produce a was considered at p < 0.05. All of the experiments were prediction model to detect the interaction effects of the done in triplicate. Table 3 Parameters and level of the experiment design Conclusion Parameters Levels The RSM were found reliable to identify the key pro - −1 0 +1 cess variables and to optimize bioethanol fermentation parameters using the wild indigenous yeast strain M. Temperature, °C 25 30 35 caribbica isolate MJTm3 isolated from sugarcane mill pH 5.5 6.0 6.5 juice tanker. In addition, the quadratic model and 3-D Inoculum size, % (v/v) 10 15 20 response surfaces plots were found suitable to predict Molasses concentration (w/v) 25 30 35 and investigate the variation of the bioethanol yield as Mixing rate, rpm 110 130 150 per the experimental design. Depending on the maxi- Incubation period, h 48 60 72 mum bioethanol yield obtained from the surface plot, the Hawaz et al. Annals of Microbiology (2023) 73:2 Page 14 of 15 Amsterdam, the Netherlands. Ethiopian Sugar Corporation, Wonji Research interaction of five factors, namely molasses concentration and Development Center, Addis Ababa, Ethiopia. and mixing rate, molasses concentration and incubation period, mixing rate and incubation period, temperature Received: 24 October 2022 Accepted: 29 November 2022 and mixing rate, molasses concentration and tempera- ture, and temperature and incubation period, showed a significant effect on the ethanol production. Hence, −1 References the obtained bioethanol concentration of 56 g L and Arshad M, Khan Z, Shah F, Rajoka M (2008) Optimization of process vari- bioethanol yield of 86% were comparable with the pre- ables for minimization of byproduct formation during fermentation of −1 dicted results 49 g L and 78.6% of bioethanol concen- blackstrap molasses to ethanol at industrial scale. Lett Appl Microbiol 47(5):410–414. https:// doi. org/ 10. 1111/j. 1472- 765X. 2008. 02446.x tration and bioethanol yield, respectively, reflecting the Atitallah IB, Ntaikou I, Antonopoulou G, Alexandropoulou M, Brysch-Herzberg accuracy and applicability of RSM to optimize bioethanol M, Nasri M, Lyberatos G, Mechichi T (2020) Evaluation of the non-con- production from molasses. Thus, further optimization ventional yeast strain Wickerhamomyces anomalus (Pichia anomala) X19 for enhanced bioethanol production using date palm sap as renewable under large-scale bioreactors with controlled fermenta- feedstock. Renew Energy 154:71–81. https:// doi. org/ 10. 1016/j. renene. tion parameters should be conducted. 2020. 03. 010 Azhar SHM, Abdulla R, Jambo SA, Marbawi H, Gansau JA, Faik AAM, Rodrigues KF (2017) Yeasts in sustainable bioethanol production: a review. Biochem Supplementary Information Biophys Rep 10:52–61. https:// doi. org/ 10. 1016/j. bbrep. 2017. 03. 003 The online version contains supplementary material available at https:// doi. Basso LC, De Amorim HV, De Oliveira AJ, Lopes ML (2008) Yeast selection for org/ 10. 1186/ s13213- 022- 01706-3. fuel ethanol production in Brazil. FEMS Yeast Res 8(7):1155–1163. https:// doi. org/ 10. 1111/j. 1567- 1364. 2008. 00428.x Additional file 1: Supplementary data. Experimental design matrix Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response sur- using Response surface central composite design (CCD) and response for face methodology (RSM) as a tool for optimization in analytical chemistry. each experimental trial. Talanta 76(5):965–977. https:// doi. org/ 10. 1016/j. talan ta. 2008. 05. 019 Boboye B, Dayo-Owoyemi I (2009) Evaluation of dough sensory properties impacted by yeasts isolated from cassava. J Appl Sci 9(4):771–776 ISSN Acknowledgements 1812-5654. We are grateful to the Fermentation Laboratory, Department of Biotechnol- Bouallagui H, Touhami Y, Hanafi N, Ghariani A, Hamdi M (2013) Performances ogy, Addis Ababa Science and Technology University, Ethiopia, for generous comparison between three technologies for continuous ethanol produc- support of consumables and equipment for this research. tion from molasses. Biomass Bioenergy 48:25–32. https:// doi. org/ 10. 1016/j. biomb ioe. 2012. 10. 018 Authors’ contributions Bušić A, Marđetko N, Kundas S, Morzak G, Belskaya H, Ivančić Šantek M, Komes EH, MT, AT, DB, SK, AA, AD, SD, GK, and DM wrote the proposal and collected D, Novak S, Šantek B (2018) Bioethanol production from renewable raw the samples. EH conducted the data analysis and prepared the manuscript. materials and its separation and purification: a review. Food Technol DM, AT, SK, MG, and TB edited the manuscript. MG preserved and collected Biotechnol 56(3):289–311. https:// doi. org/ 10. 17113/ ftb. 56. 03. 18. 5546 yeast cultures at the Westerdijk Fungal Biodiversity Institute in the Nether- Campbell J, Block E (2010) Land-use and alternative bioenergy pathways for lands. The author(s) read and approved the final manuscript. waste biomass. Environ Sci Technol 44(22):8665–8669. https:// doi. org/ 10. 1021/ es100 681g Funding Cavalaglio G, Gelosia M, Ingles D, Pompili E, D’Antonio S, Cotana F (2016) Not applicable. Response surface methodology for the optimization of cellulosic ethanol production from Phragmites australis through pre-saccharification Availability of data and materials and simultaneous saccharification and fermentation. Ind Crop Prod The data used to support the finding are available from the corresponding 83:431–437. https:// doi. org/ 10. 1016/j. indcr op. 2015. 12. 089 author upon request. Cazetta M, Celligoi M, Buzato J, Scarmino I (2007) Fermentation of molasses by Zymomonas mobilis: effects of temperature and sugar concentration on ethanol production. Bioresour Technol 98(15):2824–2828. https:// doi. org/ Declarations 10. 1016/j. biort ech. 2006. 08. 026 Chang Y-H, Chang K-S, Chen C-Y, Hsu C-L, Chang T-C, Jang H-D (2018) Ethics approval and consent to participate Enhancement of the efficiency of bioethanol production by Saccharo - Not applicable. myces cerevisiae via gradually batch-wise and fed-batch increasing the glucose concentration. Fermentation 4(2):45. https:// doi. org/ 10. 3390/ Consent for publication ferme ntati on402 0045 The submitted manuscript has a final version that all authors have read and De Vasconcelos J, Lopes C, De Franca F (2004) Continuous ethanol produc- approved. tion using yeast immobilized on sugar-cane stalks. Braz J Chem Eng 21:357–365. https:// doi. org/ 10. 1590/ S0104- 66322 00400 03000 02 Competing interests Duarte WF, Dias DR, de Melo Pereira GV, Gervásio IM, Schwan RF (2009) Indig- The authors declare that they have no competing interests. enous and inoculated yeast fermentation of gabiroba (Campomanesia pubescens) pulp for fruit wine production. J Ind Microbiol Biotechnol Author details 1 36(4):557–569. https:// doi. org/ 10. 1007/ s10295- 009- 0526-y Institute of Biotechnology, Addis Ababa University, Addis Ababa, Ethiopia. 2 El-Gendy NS, Madian HR, Amr SSA (2013) Design and optimization of a process Center of Excellence for Biotechnology and Bioprocess, Addis Ababa Science 3 for sugarcane molasses fermentation by Saccharomyces cerevisiae using and Technology University, Addis Ababa, Ethiopia. I nstitute of T echnology, 4 response surface methodology. Int J Microbiol 2013. https:// doi. org/ 10. Addis Ababa University, Addis Ababa, Ethiopia. Department of Microbial, 1155/ 2013/ 815631 Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia. 5 6 Ergun M, Mutlu SF (2000) Application of a statistical technique to the produc- Westerdijk Fungal Biodiversity Institute, Utrecht, the Netherlands. I nstitute tion of ethanol from sugar beet molasses by Saccharomyces cerevisiae. of Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Bioresour Technol 73(3):251–255. https:// doi. org/ 10. 1016/ S0960- 8524(99) 00140-6 Ha waz et al. Annals of Microbiology (2023) 73:2 Page 15 of 15 Flayeh HM (2017) Optimization of process parameters for bioethanol produc- Mukhtar K, Asgher M, Afghan S, Hussain K, Zia-Ul-Hussnain S (2010) Compara- tion from low grade Iraqi dates. Al-Nahrain J Eng Sci 20(3):647–656 tive study on two commercial strains of Saccharomyces cerevisiae for https:// nahje. com/ index. php/ main/ artic le/ view/ 257 optimum ethanol production on industrial scale. J Biomed Biotechnol Gebreegziabher Z, Mekonnen A, Ferede T, Kohlin G (2017) Profitability of 2010. https:// doi. org/ 10. 1155/ 2010/ 419586 bioethanol production: the case of Ethiopia. Ethiop J Econ 26(1):101–122 Mussatto SI, Dragone G, Guimarães PM, Silva JPA, Carneiro LM, Roberto IC, eISSN: 1993-3681 Vicente A, Domingues L, Teixeira JA (2010) Technological trends, global Ghosh P, Ghose TK (2003) Bioethanol in India: recent past and emerging future. market, and challenges of bio-ethanol production. Biotechnol Adv Biotechnol India II:1–27. https:// doi. org/ 10. 1007/3- 540- 36466-8_1 28(6):817–830. https:// doi. org/ 10. 1016/j. biote chadv. 2010. 07. 001 Hamouda HI, Nassar HN, Madian HR, Amr SSA, El-Gendy NS (2015) Response Mussatto SI, Machado EM, Carneiro LM, Teixeira JA (2012) Sugars metabolism surface optimization of bioethanol production from sugarcane molasses and ethanol production by different yeast strains from coffee industry by Pichia veronae strain HSC-22. Biotechnol Res Int 2015. https:// doi. org/ wastes hydrolysates. Appl Energy 92:763–768. https:// doi. org/ 10. 1016/j. 10. 1155/ 2015/ 905792apene rgy. 2011. 08. 020 Hansen AC, Zhang Q, Lyne PW (2005) Ethanol–diesel fuel blends––a review. Phisalaphong M, Srirattana N, Tanthapanichakoon W (2006) Mathematical Bioresour Technol 96(3):277–285. https:// doi. org/ 10. 1016/j. biort ech. 2004. modeling to investigate temperature effect on kinetic parameters of 04. 007 ethanol fermentation. Biochem Eng J 28(1):36–43. https:// doi. org/ 10. Hawaz E, Tafesse M, Tesfaye A, Beyene D, Kiros S, Kebede G, Boekhout T, 1016/j. bej. 2005. 08. 039 Theelen B, Groenewald M, Degefe A, Degu S, Admas A, Muleta D (2022) Rahman SS, Hossain MM, Choudhury N (2013) Eec ff t of various parameters Isolation and characterization of bioethanol producing wild yeasts from on the growth and ethanol production by yeasts isolated from natural bio-wastes and co-products of sugar factories. Ann Microbiol 72(1):39. sources. Bangladesh J Microbiol 30(1-2):49–54. https:// doi. org/ 10. 3329/ https:// doi. org/ 10. 1186/ s13213- 022- 01695-3bjm. v30i1-2. 28453 Jargalsaikhan O, Saraçoğlu N (2008) Application of experimental design Ramos CL, Duarte WF, Freire AL, Dias DR, Eleutherio ECA, Schwan RF (2013) method for ethanol production by fermentation of sunflower seed hull Evaluation of stress tolerance and fermentative behavior of indigenous hydrolysate using Pichia stipitis NRRL-124. Chem Eng Commun 196(1- Saccharomyces cerevisiae. Braz J Microbiol 44(3):935–944. https:// doi. org/ 2):93–103. https:// doi. org/ 10. 1080/ 00986 44080 23009 9210. 1590/ S1517- 83822 01300 50000 51 John RP, Anisha G, Nampoothiri KM, Pandey A (2011) Micro and macroal- Rattanapan A, Limtong S, Phisalaphong M (2011) Ethanol production by gal biomass: a renewable source for bioethanol. Bioresour Technol repeated batch and continuous fermentations of blackstrap molasses 102(1):186–193. https:// doi. org/ 10. 1016/j. biort ech. 2010. 06. 139 using immobilized yeast cells on thin-shell silk cocoons. Appl Energy Kamal S, Rehman S, Rehman K, Ghaffar A, Bibi I, Ahmed T, Maqsood S, Nazish 88(12):4400–4404. https:// doi. org/ 10. 1016/j. apene rgy. 2011. 05. 020 N, Iqbal HM (2021) Sustainable and optimized bioethanol production Razmovski R, Vučurović V (2011) Ethanol production from sugar beet molasses using mix microbial consortium of Saccharomyces cerevisiae and Can- by S. cerevisiae entrapped in an alginate–maize stem ground tissue dida cantarelli. Fuel:122763. https:// doi. org/ 10. 1016/j. fuel. 2021. 122763 matrix. Enzym Microb Technol 48(4-5):378–385. https:// doi. org/ 10. 1016/j. Khuong L, Masjuki H, Zulkifli N, Mohamad EN, Kalam M, Alabdulkarem A, enzmi ctec. 2010. 12. 015 Arslan A, Mosarof M, Syahir A, Jamshaid M (2017) Eec ff t of gasoline– Sarris D, Matsakas L, Aggelis G, Koutinas AA, Papanikolaou S (2014) Aerated vs bioethanol blends on the properties and lubrication characteristics of non-aerated conversions of molasses and olive mill wastewaters blends commercial engine oil. RSC Adv 7(25):15005–15019. https:// doi. org/ 10. into bioethanol by Saccharomyces cerevisiae under non-aseptic condi- 1039/ C7RA0 0357A tions. Ind Crop Prod 56:83–93. https:// doi. org/ 10. 1016/j. indcr op. 2014. 02. Kopsahelis N, Agouridis N, Bekatorou A, Kanellaki M (2007) Comparative study 040 of spent grains and delignified spent grains as yeast supports for alcohol Shafaghat H, Najafpour GD, Rezaei SP, Sharifzadeh M (2010) Optimal growth of production from molasses. Bioresour Technol 98(7):1440–1447. https:// Saccharomyces cerevisiae (PTCC 24860) on pretreated molasses for etha- doi. org/ 10. 1016/j. biort ech. 2006. 03. 030 nol production: application of response surface methodology. Chem Ind Laopaiboon L, Nuanpeng S, Srinophakun P, Klanrit P, Laopaiboon P (2009) Chem Eng Q 16(2):199–206. https:// doi. org/ 10. 2298/ CICEQ 10020 1029S Ethanol production from sweet sorghum juice using very high gravity Turhan O, Isci A, Mert B, Sakiyan O, Donmez S (2015) Optimization of ethanol technology: effects of carbon and nitrogen supplementations. Bioresour production from microfluidized wheat straw by response surface meth- Technol 100(18):4176–4182. https:// doi. org/ 10. 1016/j. biort ech. 2009. 03. odology. Prep Biochem Biotechnol 45(8):785–795. https:// doi. org/ 10. 0461080/ 10826 068. 2014. 958164 Lin Y, Zhang W, Li C, Sakakibara K, Tanaka S, Kong H (2012) Factors affecting Uncu ON, Cekmecelioglu D (2011) Cost-effective approach to ethanol produc- ethanol fermentation using Saccharomyces cerevisiae BY4742. Biomass tion and optimization by response surface methodology. Waste Manag Bioenergy 47:395–401. https:// doi. org/ 10. 1016/j. biomb ioe. 2012. 09. 019 31(4):636–643 Liu R, Shen F (2008) Impacts of main factors on bioethanol fermentation from Yüksel F, Yüksel B (2004) The use of ethanol–gasoline blend as a fuel in an SI stalk juice of sweet sorghum by immobilized Saccharomyces cerevisiae engine. Renew Energy 29(7):1181–1191. https:// doi. org/ 10. 1016/j. was- (CICC 1308). Bioresour Technol 99(4):847–854. https:// doi. org/ 10. 1016/j. man. 2010. 12. 007 biort ech. 2007. 01. 009 Zabed H, Faruq G, Sahu JN, Azirun MS, Hashim R, Nasrulhaq Boyce A (2014) Malik H (2016) Utilization of agro-industrial wastes for the biomass production Bioethanol production from fermentable sugar juice. Sci World J 2014. of baker’s yeast. M. Sc. Thesis, Punjab Agricultural University, Punjabhttps:// doi. org/ 10. 1155/ 2014/ 957102 Miguel GA, Carlsen S, Arneborg N, Saerens SM, Laulund S, Knudsen GM (2022) Zohri AE-NA, Soliman MF, Ibrahim OM, Abdelaziz AM (2022) Reducing heavy Non-Saccharomyces yeasts for beer production: insights into safety metals content in sugarcane molasses and its effect on ethanol fermen- aspects and considerations. Int J Food Microbiol:109951. https:// doi. org/ tation efficiency. Egypt Sugar J 18(0). https:// doi. org/ 10. 21608/ ESUGJ. 10. 1016/j. ijfoo dmicro. 2022. 1099512022. 143054. 1011 Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31(3):426–428. https:// doi. org/ 10. 1021/ ac601 Publisher’s Note 47a030 Springer Nature remains neutral with regard to jurisdictional claims in pub- Mohammed AH, Mohammed AK, Kamar FH, Abbas AA, Nechifor G (2018) lished maps and institutional affiliations. Determination of ethanol in fermented broth bu headspace gas chromatography using capillary column. Rev Chim Burcharest-Orig Ed 69(11):2969–2972. https:// doi. org/ 10. 37358/ RC. 18. 11. 6664 Morimura S, Ling ZY, Kida K (1997) Ethanol production by repeated-batch fer- mentation at high temperature in a molasses medium containing a high concentration of total sugar by a thermotolerant flocculating yeast with improved salt-tolerance. J Ferment Bioeng 83(3):271–274. https:// doi. org/ 10. 1016/ S0922- 338X(97) 80991-9
Journal
Annals of Microbiology – Springer Journals
Published: Jan 4, 2023
Keywords: Yeast; Optimization; Response surface method; Fermentation; Molasses; Bioethanol