Isoamyl alcohol and isobutanol production in sugarcane molasses fermentation in a microdistillery: pH, refrigeration, and supplementation effects

Isoamyl alcohol and isobutanol production in sugarcane molasses fermentation in a microdistillery: pH, refrigeration, and supplementation effects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Isoamyl alcohol and isobutanol production in sugarcane molasses fermentation in a microdistillery: pH, refrigeration, and supplementation effects Renan Atanázio dos Santos, Yeda Almeida, Samara Andrade, Celso Caldas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4397899/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fusel oil, a blend of higher alcohols generated during fermentation, predominantly comprises isoamyl alcohol and isobutanol. Despite their adverse effects on distillation and ethanol quality, these alcohols find widespread use, notably in the fine chemical industry. Fusel oil quality and quantity vary due to multiple factors, including raw materials and fermentation conditions. This study aimed to investigate the effects of pH, refrigeration, and supplementation on isoamyl alcohol and isobutanol formation during molasses must fermentation in a microdistillery. The fermentations were conducted in batches that were fed with 25 °Brix must and 25% v/v commercial dry yeast for 10 hours. A complete 2³ factorial design was used to assess the effects of the studied factors and their interactions on the response variables: fermentation efficiency (n f ), process efficiency (n p ), ethanol productivity (P), substrate-to-cell conversion factor (Y X/S ), isoamyl alcohol produced (A), isobutanol produced (B) and the A/B Ratio between these alcohols. Statistical analysis employed ANOVA and Tukey’s test. The results of the substrate-to-cell conversion factor (Y X/S ) indicated good yeast performance under different fermentation conditions. The interaction effects among the evaluated factors significantly influenced the formation of isoamyl alcohol and isobutanol, as well as the A/B Ratio. Physical sciences/Engineering/Chemical engineering Biological sciences/Biotechnology/Industrial microbiology Fusel oil. Higher alcohols. Fermentation. Ethanol. Molasses. Microdistillery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The Brazilian sugarcane industry stands out globally due to the raw material used for ethanol production, which is primarily derived from sugarcane juice and molasses. The latter, in particular, has been increasingly utilized in must formulations. Additionally, the industry has efficiently explored the residues of the production process [ 1 ]. One of the few byproducts that are not utilized by industry itself is fusel oil, a mixture of higher alcohols that can be generated during fermentation and obtained during distillation. Its main constituents are isoamyl alcohol and isobutanol [ 2 , 3 , 4 ]. The average production of fusel oil from sugarcane molasses is 2.5 L for every 1000 L of ethanol produced [ 5 ]. Based on the ethanol production from the 2022/2023 harvest, which reached 28.91 billion liters [ 6 ], it is possible to estimate that the fusel oil production was approximately 72.28 million liters. Considering the estimated ethanol production of 41.857 billion liters for the 2028/2029 harvest, fusel oil production may reach 104.64 million liters [ 7 ]. The presence of fusel oil during fermentation adversely affects the distillation and ethanol quality [ 8 ]. According to Patil et al. [ 9 ], the literature reports research aimed at reducing the formation of higher alcohols during fermentation. However, decreasing fusel oil formation without negatively impacting ethanol production remains a challenge. According to the authors, there are already some mutant yeasts that produce only traces of higher alcohols, but they do not produce sufficient ethanol to be taken as industrially feasible. In this context, research efforts have been initiated with the aim of harnessing fusel oil, which has become an object of interest due to its broad applicability, especially in the chemical industry where isoamyl alcohol and isobutanol are recovered for the production of biosolvents, extractors, flavorings, medicine, and plasticizers. This is particularly relevant in countries where large-scale production is prevalent [ 10 , 11 , 12 , 13 ]. The industries that use fusel oil for the recovery of higher alcohols mostly employ the distillation process for isoamyl alcohol and isobutanol [ 14 ]. However, the recovery of isobutanol involves the additional use of one or more distillation columns, leading to increased energy expenditure. This enhances the value of fusel oil, which has a higher concentration of isoamyl alcohol than isobutanol. This has attracted increased interest in the development of processes that lead to the formation of fusel oil with a greater ratio of these alcohols. In the literature, studies indicate the interference of reaction factors in the formation of higher alcohols, which can result in higher or lower quantities of these compounds, possibly leading to the formation of isoamyl alcohol and isobutanol in varying proportions. Among other factors, the type of raw material used, the method of must preparation, and the conditions under which fermentation takes place can contribute to these differences [ 15 , 16 , 17 ]. Patil et al. [ 9 ] reported that the composition of fusel oil varies widely, especially concerning the isoamyl alcohol content, which can vary by 45%. Recent research has focused on the isoamyl alcohol present in fusel oil, both during the distillation process for extraction and in various applications [ 18 , 19 , 20 , 13 ]. It should be emphasized that the studies reported in the literature [ 21 , 22 , 23 ] were mostly conducted with beverages and other synthetic media distinct from sugarcane molasses at the laboratory scale and under optimized conditions, unlike industrial conditions, especially in Brazilian sugar and ethanol mills. In light of this context, it becomes essential to investigate conditions closer to those used by the industry, evaluating fermentations with must formulated from the most commonly used raw materials and factors that can be easily manipulated under industrial conditions. Therefore, this study aimed to assess the potential influence of pH, refrigeration, and supplementation factors on the formation of isoamyl alcohol and isobutanol during the fermentation of sugarcane molasses must in a microdistillery. The results obtained can contribute to understanding the factors that affect the production of these alcohols, and they can be useful for optimizing fermentation processes on an industrial scale. Materials and methods Inoculum and fermentation medium The sugarcane molasses was obtained from a sugarcane industry in Alagoas, Brazil, for use in both inoculum preparation and fermentations. Initially, the inoculum was prepared by suspending 800 g of commercial dry Saccharomyces cerevisiae (Fleischmann) in 10 L of sugarcane molasses must at 5 °Brix, and supplemented with 1 g/L ammonium sulfate. This procedure resulted in enough cells to reach a concentration between 10 8 and 10 9 cells/mL, with a cellular viability exceeding 85%. After that, the must used in fermentations was prepared by diluting the molasses with water to a concentration of 25 °Brix. The pH of this mixture was adjusted using sulfuric acid (H 2 SO 4 ). No sterilization was performed. Microdistillery The fermentation experiments were conducted in a microdistillery for ethanol production located in the Laboratory of Industrial Processes (LAPIND) at the Federal Institute of Alagoas, Brazil – Penedo campus. Fermentation conditions The fermentations were carried out in triplicate in 60 L fermentation vessels equipped with refrigeration coils. Ten liters of inoculum were transferred to each vessel, followed by the addition of 30 L of must at a concentration of 25°Brix over a feeding time of 1 hour. The experiments were conducted at room temperature, and refrigeration, when necessary, was achieved by cold water circulation. Fermentation was carried out for 10 hours, and Brix, pH, and temperature were monitored every hour. Samples were taken at the beginning and at the end of the fermentations and were subsequently centrifuged, and the supernatant was stored in the refrigerator for subsequent analysis. Analytical methods The cell concentration was determined through spectrophotometry by measuring the absorbance of the samples diluted at a 1:50 ratio in a UV-visible spectrophotometer (NOVAINSTRUMENTS SERIES 2000) at a wavelength of 600 nm. The absorbance values were converted to a cell concentration in g/L using a standard curve that relates the absorbance to the dry mass of the yeast. The total soluble solids (°Brix) were determined by refractometry using a portable digital refractometer (HANNA HI 96801) with automatic temperature compensation and a measurement range from 0 to 85 °Brix. The pH was measured using a digital benchtop pH meter, (a PHTER PHS-3B model), which was previously calibrated with pH 4.0 and pH 7.0 standard solutions, with temperature control. The concentrations of total reducing sugars (TRS), including glucose, fructose, and sucrose, were determined by high-performance liquid chromatography (HPLC) using a SHIMADZU LC-20AT chromatograph equipped with a refractive index detector (RID) and an Aminex HPX-87H column (300 x 7.8 mm). The column oven and detector temperatures were maintained at 65°C and 50°C, respectively. A 5 mM sulfuric acid (H 2 SO 4 ) solution was used as the mobile phase at a flow rate of 0.6 mL/min, and the sample injection volume was 25 µL. The concentrations of ethanol, isoamyl alcohol (A), and isobutanol (B) were determined by gas chromatography (GC) on a SHIMADZU QP2010S chromatograph equipped with a flame ionization detector (FID). The column used was a Sh-Rtx-2330 (Restek), with a length of 30 m, an internal diameter of 0.32 mm, and a 0.20 µm film. The carrier gas flow (N 2 /Ar) was set at 1.30 mL/min. The injection volume was 1 µL, and a "split" injection system with a 1:50 ratio was used. The temperature program applied was as follows: 50°C for 2 min, from 50 to 190°C at 10°C/min, from 190°C for 5 min, and from 190 to 230°C at 20°C/min for 7 min. The injector and detector temperatures were set at 250°C. Performance parameters of the fermentation process The fermentation efficiency (η f , in %) was calculated considering the theoretical yield of 0.511 g ethanol /g TRS (100%), according to Eq. (1), where ΔE is the ethanol produced (final ethanol concentration – initial ethanol concentration) and ΔS is the consumed sugar (TRS initial – TRS final), both in g/L. η f = (ΔE/(ΔS. 0.511). 100) (1) The process efficiency (η p , in %) was calculated considering the initial sugar concentration (TRS initial), according to Eq. (2). η p = ΔE/(0.511. TRS initial). 100 (2) The ethanol productivity (P, in g ethanol /L.h) was calculated with Eq. (3), considering a fermentation time (t) of 10 h. P = ΔE/t (3) The substrate-to-cell conversion factor (Y X/S , in g yeast /g TRS ) was calculated with Eq. (4), where ΔX is the cells produced (final cell concentration – initial cell concentration), in g/L. Y X/S = ΔX/ΔS (4) Experimental design and statistical analysis A complete 2³ factorial design was used (for a total of 8 experiments) with the following independent variables: pH, refrigeration of the vessel, and supplementation of the must with ammonium sulfate. The dependent variables included fermentation efficiency (n f , in %), process efficiency (n p , in %), ethanol productivity (P, in g/L.h), substrate-to-cell conversion factor (Y X/S , in g yeast /g TRS ), isoamyl alcohol produced (A, in g/L), isobutanol produced (B, in g/L) and the A/B ratio. All the experiments were conducted in triplicate in a random order. The obtained data for isoamyl alcohol produced (A, in g/L), isobutanol produced (B, in g/L), and the A/B ratio were adjusted to the polynomial of Eq. (5), where Y represents the response variable, βi and βij are the regression coefficients, and PH, R and S are the independent variables, pH, refrigeration, and supplementation, respectively. Y = β 0 + β 1 PH + β 2 R + β 3 S + β 12 PH*R + β 13 PH*S + β 23 R*S + β 123 PH*R*S (5) Table 1 shows the real and coded independent variables used in the experimental design. Independent Variable Levels -1 1 pH 3.5 5.0 Supplementation (g/L) 0.0 1.0 Refrigeration without with Table 1 . Real and coded levels of the independent variables used in the experimental design. For the statistical analysis of the data, analysis of variance (ANOVA) and Tukey's test were used for the comparison of means. The adopted significance level was 5%. Results and discussion Performance parameters of the fermentation process The assessment of higher alcohols formation must be conducted under fermentation conditions that provide performances comparable to those found in industry and the scientific literature. Thus, some parameters were calculated to evaluate the performance of the fermentation process concerning the microorganism employed and the target product of interest for the industry. Table 2 presents the analysis of variance (ANOVA) of 2³ factorial design for the responses η f , η p , P, and Y X/S . The results indicate that the treatments were statistically significant (p-value < 0.05), suggesting the presence of significant differences in the means of fermentation conditions concerning these response variables. Y X/S Source of Variation Degrees of freedom Sum of squares Mean square F p-value Treatment 7 0.021567 0.003081 8.85 0.000 Error 16 0.00557 0.000348 Total 23 0.027137 n f Source of Variation Degrees of freedom Sum of squares Mean square F p-value Treatment 7 4348.01 621.14 21.26 0.000 Error 16 467.55 29.22 Total 23 4815.56 n p Source of Variation Degrees of freedom Sum of squares Mean square F p-value Treatment 7 5947.36 849.62 33.43 0.000 Error 16 406.69 25.42 Total 23 6354.04 P Source of Variation Degrees of freedom Sum of squares Mean square F p-value Treatment 7 13.4103 1.91575 13.7 0.000 Error 16 2.2381 0.13988 Total 23 15.6483 Y X/S (g yeast /g TRS ) substrate-to-cell conversion factor; n f (%) fermentation efficiency; n p (%) process efficiency; P (g/L.h) ethanol productivity Table 2. ANOVA of the factorial design for the response variables η f , η p , P and Y X/S . The means of the results obtained under the different fermentation conditions used are presented in Table 3. Fermentation condition Independent variables Results (mean ± sd) Coded values Real values pH Sup. Ref. pH Sup. (g/L) Ref. Y X/S (g yeast /g TRS ) n f (%) n p (%) P (g/L.h) 1 -1 -1 -1 3.5 0.0 without 0.21 ± 0.02 a 45.65 ± 5.19 c 35.68 ± 3.30 d 3.92 ± 0.14 d 2 1 -1 -1 5.0 0.0 without 0.18 ± 0.01 a b 77.24 ± 2.49 a b 75.35 ± 3.37 a b c 5.23 ± 0.44 b c 3 -1 1 -1 3.5 1.0 without 0.22 ± 0.03 a 88.85 ± 5.67 a 75.86 ± 5.39 a b 5.40 ± 0.43 a b c 4 1 1 -1 5.0 1.0 without 0.11 ± 0.00 c 87.31 ± 5.10 a 87.17 ± 5.06 a 6.03 ± 0.09 a b 5 -1 -1 1 3.5 0.0 with 0.15 ± 0.00 b c 90.23 ± 5.93 a 88.77 ± 5.77 a 6.44 ± 0.16 a 6 1 -1 1 5.0 0.0 with 0.18 ± 0.03 a b 70.97 ± 7.32 b 61.21 ± 6.26 c 4.66 ± 0.51 c d 7 -1 1 1 3.5 1.0 with 0.18 ± 0.01 a b 78.72 ± 5,14 a b 69.76 ± 5.04 b c 4.68 ± 0.54 c d 8 1 1 1 5.0 1.0 with 0.17 ± 0.03 a b 79.40 ± 5.24 a b 75.25 ± 5.34 a b c 5.21 ± 0.37 b c Means followed by the same lowercase letter in the column do not differ by Tukey's test at 5% significance level Y X/S (g yeast /g TRS ) substrate-to-cell conversion factor; n f (%) fermentation efficiency; n p (%) process efficiency; P (g/L.h) ethanol productivity Table 3. Results obtained by 2³ factorial design. The substrate-to-cell conversion factor (Y X/S ) met the criteria established in the literature [24,25], indicating successful yeast development under the adopted fermentation conditions (Table 3). The fermentation conditions 4 and 5 (Table 3) resulted in lower values for Y X/S (0.11 ± 0.00 g yeast /g TRS and 0.15 ± 0.00 g yeast /g TRS , respectively). It is important to highlight that fermentation condition 4 differed significantly from the other conditions (p < 0,05), except for condition 5. In the literature, a wide range of Y X/S values obtained in fermentations with ethanol-producing yeasts has been reported. According to Stroppa et al. [24], the reported values range from 0.03 to 0.28 g yeast /g TRS . The authors conducted fermentations with yeasts isolated from distilleries in sugarcane must at a concentration of 9.4 °Brix, temperature of 30°C for 24 hours, and obtained values of 0.179 and 0.185 g yeast /g TRS for the strains RM01 and CV01, respectively. Colombi et al. [25] evaluated the influence of different compounds, such as vanillin, acetic acid, vanillic acid, and 4-hydroxybenzoic acid, on the fermentation of glucose at 40 g/L by the yeast Saccharomyces cerevisiae JP1. The authors conducted fermentation at 30°C and 150 rpm for 22 hours, the pH was adjusted to 4.9, and Y X/S values ranging from 0.00 to 0.22 g yeast /g TRS were obtained. Alves [26] conducted fermentations with molasses must at 40 g/L supplemented with 2.5 g/L yeast extract, using an industrial strain of Saccharomyces cerevisiae within a temperature range varying from 28 to 38°C. The obtained values ranged between 0.087 and 0.099 g yeast /g TRS . The wide variation in Y X/S values reported in the literature resulted from different process conditions and raw materials used, as well as yeast strains. According to Table 3, fermentation condition 1, with a pH of 3.5, and without the addition of ammonium sulfate and without refrigeration, exhibited unsatisfactory performance compared to the other tested conditions, as fermentation efficiency, process efficiency, and ethanol productivity were low. Fermentation conditions 3, 4, and 5 showed better performance with higher fermentation and process efficiencies, and ethanol productivity. When comparing these three experiments, it is evident that condition 5, with a pH of 3.5, no ammonium sulfate supplementation, and must refrigeration, achieved the highest process performance indicators. In the case of the need to achieve good yeast productivity, the optimal condition was number 3 (pH 3.5, supplementation of 1 g/L ammonium sulfate, and no refrigeration), which significantly differed (p < 0.05) from conditions 4 and 5 (Table 3). An analysis of the data presented in Table 3 suggested consistency with the results described in the scientific literature (Table 4). Furthermore, the fermentation conditions 3, 4, and 5 were appropriate, resulting in satisfactory fermentation performance. The differences in the results reported in the literature (Table 4) are attributed to the different conditions adopted by the authors, including must composition, fermentation time, and yeast strain used. Reference Medium Microorganism Temperature Fermentation time n f n p P [27] sugarcane synthetic must (160 g/L of sugars) Saccharomyces cerevisiae CAT-1 30°C 72 h 90.20% nr nr [28] non-sterile molasses must (26°Brix) Saccharomyces cerevisiae CAT-1 30°C 24 h 79.88% nr 4.27 g/Lh [29] molasses + sugarcane juice must (270 g/L of sugars) Saccharomyces cerevisiae Y-904 32°C 24 h nr 92.80% 4.27 g/Lh [30] synthetic must (250 g/L of sugars) Saccharomyces cerevisiae flocculant 28°C 12 h nr 82.58% 9.6 g/Lh [31] sterile sugarcane juice must (25°Brix) Saccharomyces cerevisiae CAT-1 30°C 24 h 92.73% nr 4.69 g/Lh nr not reported Table 4. Performance parameters of the fermentation process, which are reported in the literature. Isoamyl alcohol production The significant effects on isoamyl alcohol production (A) can be seen in the Pareto chart in Figure 1. There was no significant third-order interaction effect. Among the main effects, only supplementation had a significant effect (p<0.05) on A, but the second-order interaction effects, pH*Ref. and Sup.*Ref., were also significant. Therefore, factors should be evaluated together, as the response obtained by varying one factor depends on the levels of the other factors. The regression model with coded units, considering only the significant effects and relating isoamyl alcohol production to the factors pH, refrigeration, and supplementation is given by Equation (5). The corresponding graphs can be viewed in Figures 2a and 2b. ISOAM. (A) = 0.2548 + 0.0488 Sup. – 0.0424 pH*Ref. – 0.0310 Sup.*Ref. (5) The ANOVA results for the regression model of Equation (5) are presented in Table 5. Source of Variation Degrees of freedom Sum of squares Mean square F p-value Regression 3 0.12359 0.041197 10.04 0.000 Lack of fit 4 0.02715 0.006786 1.98 0.147 Pure error 16 0.05495 0.003434 Total 23 0.20568 R² = 60.09% Table 5. ANOVA results for the regression model for the response variable isoamyl alcohol produced (A). When evaluating the pH*Ref. interaction (Figures. 2a and 2b), it was observed that, in fermentations conducted without refrigeration, an increase in pH and must supplementation led to an increase in isoamyl alcohol production (A). In these same figures, it is noted that by maintaining the absence of refrigeration and decreasing both the pH and must supplementation, there is a decrease in isoamyl alcohol production (A). In the Sup.*Ref. interaction (Figure 2b), it was observed that must supplementation increased isoamyl alcohol production (A), both in fermentations conducted without refrigeration and those with refrigeration, confirming the significant effect of supplementation (Figure 1). Isobutanol production The Pareto chart in Figure 3 shows that the main effects - pH, refrigeration, and supplementation - were significant, and additionally, the second-order interaction effects - pH*Ref. and Sup.*Ref. - were also significant. Therefore, these factors should be evaluated together. Considering only significant effects, the regression model with uncoded units relates isobutanol production to the factors pH, refrigeration, and supplementation, as represented in Equation (6). The corresponding graphs can be viewed in Figures 4a and 4b. ISOBU. (B) = – 0.0222 + 0.03120 pH + 0.0549 Sup. + 0.1269 Ref. – 0.02964 pH*Ref. – 0.0394 Sup.*Ref. (6) The ANOVA results for the regression model of Eq. 6 are presented in Table 6. Source of Variation Degrees of freedom Sum of squares Mean square F p-value Regression 5 0.06082 0.012163 11.75 0.000 Lack of fit 2 0.00359 0.001796 1.91 0.180 Pure error 16 0.01504 0.000940 Total 23 0.07945 R² = 76.55% Table 6. ANOVA of the regression model for the response variable isobutanol produced (B). An examination of Figures 4a and 4b shows that the production of isobutanol (B) exhibited a similar trend to that of isoamyl alcohol production (A) (Figures 2a and 2b) concerning the interaction effects of pH*Ref. and Sup.*Ref. In other words, in fermentations conducted without refrigeration, an increase in pH and must supplementation increased isobutanol production (B). As shown in Figures 4a and 4b, must supplementation and an increase in pH increased isobutanol production (B), both in fermentations conducted without refrigeration and in those conducted with refrigeration, confirming the significant effects of supplementation and pH (Figure 3). However, must refrigeration resulted in a less significant increase in isobutanol production (B), also confirming the important effect of refrigeration (Figure 3). A/B ratio between isoamyl alcohol (A) and isobutanol (B) produced As shown in the Pareto chart in Figure 5, only the main effects of pH and refrigeration were significant. Additionally, the second-order interaction effects pH*Ref. and pH*Sup., as well as the third-order effect pH*Sup.*Ref. were also significant. Therefore, these factors should be evaluated together. The regression model with coded units, considering only the significant effects and relating the A/B ratio to the factors pH, refrigeration, and supplementation, is represented in Equation (7). The corresponding graphs can be viewed in Figures 6a and 6b. A/B RATIO = 1.8828 – 0.1482 pH + 0.0955 Ref. + 0.0274 pH*Sup. - 0.0524 pH*Ref. + 0.0778 pH*Sup.*Ref. (7) The ANOVA results for the regression model of Eq. 7 are presented in Table 7. Source of Variation Degrees of freedom Sum of squares Mean square F p-value Regression 5 0.97470 0.19494 73.30 0.000 Lack of fit 2 0.00457 0.002283 0.84 0.448 Pure error 16 0.04330 0.002706 Total 23 1.02257 R² = 95.32% Table 7. ANOVA of the regression model for the response variable A/B ratio. When evaluating the pH*Ref. interaction (Figure 6a), it was observed that the peak of the A/B ratio occurred at a lower pH and under refrigeration, while the lowest A/B ratio was obtained at a higher pH and without refrigeration. In the pH*Sup. interaction (Figure 6b), the peak of A/B ratio occurred at a lower pH and without must supplementation. Conversely, the opposite trend was observed with higher pH and must supplementation. As shown in Figures 6a and 6b, the influence of pH on the A/B ratio is clear, with a lower pH favoring an increase in this ratio. Comprehensive analysis of the results: isoamyl alcohol produced (A), isobutanol produced (B) and the A/B ratio The results presented in Table 8 indicate significant differences among the fermentation conditions for the evaluated response variables (production of isoamyl alcohol (A), isobutanol (B), and the A/B ratio). Fermentation condition Independent variables Results (mean ± sd) Coded values Real values pH Sup. Ref. pH Sup. (g/L) Ref. Isoamyl alcohol (A) (g/L) Isobutanol (B) (g/L) A/B Ratio 1 -1 -1 -1 3.5 0.0 without 0.1487 ± 0.0189 b 0.0811 ± 0.0113 b 1.84 ± 0.03 c d 2 1 -1 -1 5.0 0.0 without 0.2406 ± 0.0040 b 0.1379 ± 0.0046 b 1.75 ± 0.03 d e 3 -1 1 -1 3.5 1.0 without 0.2717 ± 0.0034 a b 0.1409 ± 0.0055 b 1.93 ± 0.06 c 4 1 1 -1 5.0 1.0 without 0.4372 ± 0.0445 a 0.2666 ± 0.0180 a 1.64 ± 0.06 e 5 -1 -1 1 3.5 0.0 with 0.2514 ± 0.0746 b 0.1119 ± 0.0373 b 2.26 ± 0.09 a 6 1 -1 1 5.0 0.0 with 0.1831 ± 0.0873 b 0.1109 ± 0.0529 b 1.65 ± 0.02 e 7 -1 1 1 3.5 1.0 with 0.2598 ± 0.1047 b 0.1240 ± 0.0493 b 2.09 ± 0.04 b 8 1 1 1 5.0 1.0 with 0.2460 ± 0.0309 b 0.1297 ± 0.0198 b 1.90 ± 0.05 c Means followed by the same lowercase letter in the column do not differ by Tukey's test at 5% significance level. Table 8. Isoamyl alcohol and isobutanol produced under the different fermentation conditions. The fermentations conducted at pH 5.0, with must supplementation and without refrigeration (fermentation condition 4), resulted in greater formation of isoamyl alcohol and isobutanol. However, the A/B ratio was low under this condition. Despite a low substrate-to-cell conversion (Y X/S = 0.11 g yeasts /g TRS ), both the fermentation and process efficiencies were satisfactory, as was the high ethanol productivity (87.31%, 87.17%, and 6.03 g/L.h, respectively). These results are consistent with previous studies by Pons et al. [32] and Cachot et al. [33], which demonstrated a positive correlation between the formation of these alcohols and ethanol production. In fact, our results indicate that fermentation conditions leading to higher ethanol production also resulted in increased formation of isoamyl alcohol and isobutanol. The evaluated factors (pH, refrigeration, and supplementation) simultaneously affect the production of isoamyl alcohol and isobutanol. However, at pH 3.5, the formation of isobutanol was lower than that at other pH values, resulting in a higher A/B ratio between these alcohols. Therefore, a lower pH is suggested to favor an increase in the A/B ratio. Fermentation condition 5 exhibited the highest A/B ratio (2.26) and significantly differed from the other conditions. Despite a low substrate-to-cell conversion (Y X/S =0.15 g yeasts /g TRS ), both fermentation and process efficiencies, as well as ethanol productivity, were high (90.23%, 88.77%, and 6.44 g/L.h, respectively). Even though it was not possible to establish a condition that reduces fusel oil formation, the obtained results are consistent with previous studies [34,35,36], which report the influence of different factors on the production of higher alcohols during fermentation. These results underscore the importance of jointly evaluating the studied factors (pH, refrigeration, and supplementation), considering the interaction effects that occur among them. Therefore, the results provide a better understanding of these interactions and their impact on the formation of isoamyl alcohol and isobutanol, which can contribute to the development of more efficient processes. Conclusions The factors pH, refrigeration, and supplementation with ammonium sulfate significantly influenced the formation of isoamyl alcohol and isobutanol during the fermentation of sugarcane molasses must in a microdistillery. The different fermentation conditions tested showed significant differences in terms of the response variables η f , η p , P and Y X/S . Specifically, fermentation conditions 3, 4, and 5 demonstrated better performance in terms of fermentation and process efficiencies (η f , η p ), while the substrate-to-cell conversion factor (Y X/S ) was considered satisfactory under all tested conditions, indicating that the yeasts developed well under these circumstances. The interaction effects among the factors were significant for the production of isoamyl alcohol, isobutanol, and the A/B ratio. Ammonium sulfate supplementation promoted an increase in the production of both isoamyl alcohol and isobutanol, regardless of the pH. On the other hand, refrigeration increased isobutanol production at pH 3.5 and decreased at pH 5.0, with a reduction in the A/B ratio observed as the pH increased from 3.5 to 5.0. Therefore, the selection of the best fermentation condition will depend on the specific needs of the process, such as yeast productivity, and the feasibility of refrigeration and must supplementation. Declarations Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to R.A.S. Funding No funding was received for conducting this study. Author Contribution All authors reviewed the manuscript. R.S., S.A., and Y.A. wrote the main manuscript text. R.S. and C.C. performed experiments. Y.A., C.S.C., S.A., and J.F. contributed to the analysis methodologies and supervised the experiments. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Ribeiro, E. J. & Reis, H. B. Influência conjunta do pH, temperatura e concentração de sulfito em fermentação alcoólica de mostos de sacarose. IX encontro e XIII seminário de iniciação cientifica. (2009). Suslick, K. S. Kirk-Othmer encyclopedia of chemical technology. J. Wiley & Sons. 26 , 517-541 (1998). Qian, Y., Ouyang, L, Wang, X., Zhu, L. & Lu, X. Experimental studies on combustion and emissions of RCCI fueled with n-heptane/alcohols fuels. Fuel. 162 , 239-250. https://doi.org/10.1016/j.fuel.2015.09.022 (2015). Ardebili, S. M. S., Solmaz, H. & Mostafaei, M. Optimization of fusel oil - gasoline blend ratio to enhance the performance and reduce emissions. Applied Thermal Engineering . 148 , 1334-1345. https://doi.org/10.1016/j.applthermaleng.2018.12.005 (2019). Da Silveira, E. Resíduos Reciclados: Óleo fúsel e CO 2 gerados na fabricação de etanol podem ser aproveitados na produção de substâncias químicas de uso industrial. Revista Pesquisa Fapesp . 240 , 68-69. https://revistapesquisa.fapesp.br/wp-content/uploads/2016/02/068-069_%C3%93leo-fusel_240.pdf. (2016). União da Indústria de Cana-de-Açúcar (UNICA) Números finais da safra de cana-de-açúcar 2022/23. https://unicadata.com.br/arquivos/pdfs/2023/04/44e3035ee7fde214d3a491d84b291fe5.pdf (2023). FEDERAÇÃO DAS INDÚSTRIAS DO ESTADO DE SÃO PAULO (FIESP). Outlook Fiesp 2028.http://outlookdeagro.azurewebsites.net/OutLookDeagro/pt-BR/Publicacao/Etanol/Brasil (2020). Batista, F. R. M., Follegatti-Romero, L. A., Bessa, L. C. B. A. & Meirelles, A. J. A. Computational simulation applied to the investigation of industrial plants for bioethanol distillation. Computers and Chemical Engineering , 46 , 1-16. https://doi.org/10.1016/j.compchemeng.2012.06.004 (2012). Patil, A. G., Koolwal, S. M. & Butala, H. D. Fusel oil: composition, removal and potential utilization. International Sugar Journal , 104 , 51-58. (2002) Güvenç, A., Kapucu, N., Kapucu, H., Aydoğan, Ö. & Mehmetoğlu, Ü. Enzymatic esterification of isoamyl alcohol obtained from fusel oil: optimization by response surface methodology. Enzyme and Microbial Technology, 40 (4) , 778-785. https://doi.org/10.1016/j.enzmictec.2006.06.010 (2007). Montoya, N. et al. Colombian fusel oil. Ingeniería e Investigación , 36(2) , 21-27. http://dx.doi.org/10.15446/ing.investig.v36n2.52369 (2016). Kirdi, R., Ben Akacha, N., Messaoudi, Y. & Gargouri, M. Enhanced synthesis of isoamyl acetate using liquid‒gas biphasic system by the transesterification reaction of isoamyl alcohol obtained from fusel oil. Biotechnology and Bioprocess Engineering , 22(4) , 413-422. https://doi.org/10.1007/s12257-016-0616-4 (2017). Massa, T. B., Raspe, D. T., Feiten, M. C., Cardozo-Filho, L. & Silva, C. Fusel oil: chemical composition and an overview of its potential application. Journal of the Brazilian Chemical Society , 34 , 153-166. https://doi.org/10.21577/0103-5053.20220145 (2023). Ferreira, M. C., Meirelles, A. J. A & Batista, E. A. C. Study of the fusel oil distillation process. Industrial & Engineering Chemistry Research , 52 , 2336−2351. https://doi.org/10.1021/ie300665z (2013). Vidal, E. E. et al. A. Influence of nitrogen supply on the production of higher alcohols/esters and expression of flavour-related genes in cachaca fermentation. Food Chemistry , 138(1) , 701-708. https://doi.org/10.1016/j.foodchem.2012.10.147 (2013). Luo, H. B., Wang, Y., Wang, C. H., Yang, X. D. & Li, D. Y. Metabolic control of higher alcohols producing by Saccharomyces cerevisiae in mulberry wine brewing. Advanced Materials Research , 781-784 , 1861-1865. https://doi.org/10.4028/www.scientific.net/AMR.781-784.1861 (2013). Yuan, J., Chen, X., Mishra, P. & Ching, C. B. Metabolically engineered Saccharomyces cerevisiae for enhanced isoamyl alcohol production. Applied Microbiology and Biotechnology , 101 , 464-474. https://doi.org/10.1007/s00253-016-7970-1 (2016). Tebas, S. O. G. at al. Purificação do álcool isoamílico a partir do óleo fúsel. Blucher Chemical Engineering Proceedings , 4(1) , 105-107. (2017). Tebas, S. O. G., Barañano, A. G., Pinheiro, P. F. & Júnior, V. L. Mild conditions in the transesterification reaction of babassu oil and purified fusel oil. Chemical Engineering Communications , 208(3) , 432-440. https://doi.org/10.1080/00986445.2019.1710495 (2020). Eustácio, R. S. et al. Simulação do processo de destilação da mistura etanol-óleo fúsel utilizando o simulador de processos ProSimPlus. Revista Teccen , 11(1) , 61-67 (2018). Stribny, J., Gamero, A., Perez-Torrado, R. & Querol, A. Saccharomyces kudriavzevii and Saccharomyces uvarum differ from Saccharomyces cerevisiae during the production of aroma-active higher alcohols and acetate esters using their amino acidic precursors. International Journal of Food Microbiology , 205 , 41-46. https://doi.org/10.1016/j.ijfoodmicro.2015.04.003 (2015). Li, P. et al. A seamless gene deletion method and its application for regulation of higher alcohols and ester in baijiu Saccharomyces cerevisiae . Biomed Research International , 2019 . https://doi.org/10.1155/2019/6723849 (2019). Zhong, X. et al. Reducing higher alcohols by nitrogen compensation during fermentation of Chinese rice wine. Food Science and Biotechnology, 29 , 805-816. https://doi.org/10.1007/s10068-019-00718-1 (2019). Stroppa, C. T., Alves, J. G. L. F., Figueiredo, A. L. F. D.& Castro, C., C. Parâmetros cinéticos de linhagens de levedura isoladas de alambiques mineiros. Ciência e Agrotecnologia , 33 , 1978-1983. https://doi.org/10.1590/S1413-70542009000700048 (2009). Colombi, B. L., Ortiz, M. A, Zanoni, P. R. S., Magalhães, W. L. E. & Tavares, L. B. B. Efeito de compostos inibidores na bioconversão de glicose em etanol por levedura Saccharomyces cerevisiae . Engevista , 19(2) , 339-352. https://doi.org/10.22409/engevista.v19i2.838 (2017). Alves, J. G. L. F. Estudo da influência da temperatura na cinética de crescimento anaeróbico de Saccharomyces cerevisiae . Dissertação (Mestrado em Engenharia de Alimentos) – Universidade Estadual de Campinas, Campinas. 69 f. https://doi.org/10.47749/t/unicamp.1996.108219 (1996). Santos, C. O. Diagnóstico e avaliação da influência de contaminantes selvagens durante etapas do processo produtivo do etanol. Dissertação (Mestrado em Engenharia Química) - Universidade Federal de Goiás, Goiânia. http://repositorio.bc.ufg.br/tede/handle/tede/11355 (2021). Cabral, G. B. Fermentação alcoólica de melaço com alta concentração de açúcar: efeito da esterilização do mosto e tratamento ácido da levedura. Tese de Doutorado - Universidade de São Paulo. https://doi.org/10.11606/D.11.2020.tde-14082020-081941 (2020). Cruz, M. L., De Resende, M. M. & Ribeiro, E. J. Improvement of ethanol production in fed-batch fermentation using a mixture of sugarcane juice and molasse under very high-gravity conditions. Bioprocess and Biosystems Engineering , 44 , 617-625. https://doi.org/10.1007/s00449-020-02462-x (2021). Brandão, A. C. T., de Resende, M. M. & Ribeiro, E. J. Alcoholic fermentation with high sugar and cell concentration at moderate temperatures using flocculant yeasts. Korean Journal of Chemical Engineering , 37 , 1717-1725. https://doi.org/10.1007/s11814-020-0589-z (2020). Cerqueira, D. P. Fermentação alcoólica de mosto com alta concentração de açúcar. Tese de Doutorado - Universidade de São Paulo. https://doi.org/10.11606/d.11.2013.tde-19122013-085208 (2013). Pons, Marie-Noëlle & Schutze, S. On-line monitoring of volatile compounds in honey fermentation. Journal of Fermentation and Bioengineering , 78(6) , 450-454. https://doi.org/10.1016/0922-338X(94)90045-0 (1994). Cachot, T., Müller, M. & Pons, Marie-Nöelle. Kinetics of volatile metabolites during alcoholic fermentation of cane molasses by Saccharomyces cerevisiae . Applied Microbiology and Biotechnology, 35 , 450-454. https://doi.org/10.1007/BF00169748 (1991). Sanchez, N., Ruiz, R. Y., Infante, N. & Cobo, M. Bioethanol production from cachaza as hydrogen feedstock: effect of ammonium sulfate during fermentation. Energies , 10(12) , 2112. https://doi.org/10.3390/en10122112 (2017). Rollero, S. et al. Combined effects of nutrients and temperature on the production of fermentative aromas by Saccharomyces cerevisiae during wine fermentation. Applied Microbiology and Biotechnology , 99 , 2291-2304. https://doi.org/10.1007/s00253-014-6210-9 (2015). Arshad, M., Khan, Z. M., Khalil‐ur‐Rehman, Shah, F. A. & Rajoka, M. I. Optimization of process variables for minimization of byproduct formation during fermentation of blackstrap molasses to ethanol at industrial scale. Letters in Applied Microbiology , 47(5) , 410-414. https://doi.org/10.1111/j.1472-765X.2008.02446.x (2008) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4397899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304594812,"identity":"74aeb4b9-9416-4930-954f-f87ff885a902","order_by":0,"name":"Renan Atanázio dos Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYNCDDwZyDHxAWoKB4QARytkYGBhnGBiDaBK0MPMwEKHFvL334OcChjp5c/nmw59tCgzk2RiYD97mYbiTj0uLzJlzydIzGA4b7mxjS5POMTAwbGNgS7bmYXhm2YBDi4REjoE0D8MBxg3HeMyYcwz+MLYx8JgBRQ4b4LJFQv6N8W8ehjp7oBbjzxYGBvZtDPzf8GuRAJvJnAjUYiDNYGCQCLSFDb8Wnhwzax6Dw8kbjqWlSfYYGCS3MbMZW84xeIZbC/sZ49s8FXW2Gw4fPvzhxx8D23725oc33lTcwakFAlCkmTFERsEoGAWjYBSQCgCEXkaLDm20zwAAAABJRU5ErkJggg==","orcid":"","institution":"Federal Institute of Education, Science and Technology Alagoas","correspondingAuthor":true,"prefix":"","firstName":"Renan","middleName":"Atanázio dos","lastName":"Santos","suffix":""},{"id":304594813,"identity":"dddbab44-3e6f-4bc8-935b-c271a44c1486","order_by":1,"name":"Yeda Almeida","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Yeda","middleName":"","lastName":"Almeida","suffix":""},{"id":304594814,"identity":"3fcd17eb-dbd7-44aa-a2c4-23e861ac274f","order_by":2,"name":"Samara Andrade","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Samara","middleName":"","lastName":"Andrade","suffix":""},{"id":304594815,"identity":"51bf46a8-e76b-4dd3-82a6-44c910d0efbd","order_by":3,"name":"Celso Caldas","email":"","orcid":"","institution":"Federal Institute of Education, Science and Technology Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Celso","middleName":"","lastName":"Caldas","suffix":""},{"id":304594816,"identity":"54ff907c-f47a-4577-abd3-d2331cb9453d","order_by":4,"name":"Jonnathan Freitas","email":"","orcid":"","institution":"Federal Institute of Education, Science and Technology Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Jonnathan","middleName":"","lastName":"Freitas","suffix":""},{"id":304594817,"identity":"7a5cd8a2-2f7b-41c3-a53d-eeb7b9e8ee22","order_by":5,"name":"Clara Costa","email":"","orcid":"","institution":"Federal Institute of Education, Science and Technology Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2024-05-10 03:24:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4397899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4397899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56939243,"identity":"b1e58b94-6e78-4dcb-90a1-405a272e2854","added_by":"auto","created_at":"2024-05-22 11:48:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63628,"visible":true,"origin":"","legend":"\u003cp\u003ePareto chart of standardized effects on isoamyl alcohol production.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/c14493cac9c55925e69f7d32.png"},{"id":56939247,"identity":"a1c3bffa-627d-4848-ab42-ad4d9c466676","added_by":"auto","created_at":"2024-05-22 11:48:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":675222,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface for isoamyl alcohol (A, in g/L) depending on the pH and refrigeration (\u003cstrong\u003ea\u003c/strong\u003e) and supplementation and refrigeration (\u003cstrong\u003eb\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/63eb702e7d957cf33908f75c.png"},{"id":56939849,"identity":"bd5adeab-cc53-494f-ba8d-c7787771c663","added_by":"auto","created_at":"2024-05-22 11:56:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68220,"visible":true,"origin":"","legend":"\u003cp\u003ePareto chart of the standardized effects on isobutanol production.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/63d673cc48f46d9360b3f8e8.png"},{"id":56939246,"identity":"b5b9b508-c03f-4141-bf14-d2d39c70b821","added_by":"auto","created_at":"2024-05-22 11:48:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":742559,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface for isobutanol (B, in g/L) according to pH and refrigeration (\u003cstrong\u003ea\u003c/strong\u003e) and supplementation and refrigeration (b).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/1b55d3bc4314f2600ffc8a92.png"},{"id":56939244,"identity":"3028b842-82f1-4e22-983d-8921383d3342","added_by":"auto","created_at":"2024-05-22 11:48:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55673,"visible":true,"origin":"","legend":"\u003cp\u003ePareto chart of the standardized effects on the A/B ratio.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/c7d7d44b95cb20fa463d5901.png"},{"id":56939248,"identity":"b8c9dfd1-da03-40fd-869c-440d302b32cf","added_by":"auto","created_at":"2024-05-22 11:48:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":573223,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface for the A/B ratio according to pH and refrigeration (\u003cstrong\u003ea\u003c/strong\u003e) and pH and supplementation (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/6f8be539f0e338782a475daf.png"},{"id":64066437,"identity":"432f31b0-8391-4bb7-9013-9a770e5083d4","added_by":"auto","created_at":"2024-09-06 04:52:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3544656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4397899/v1/299ec7bb-bf9f-4da0-b12f-66fbd7bb1be6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Isoamyl alcohol and isobutanol production in sugarcane molasses fermentation in a microdistillery: pH, refrigeration, and supplementation effects","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Brazilian sugarcane industry stands out globally due to the raw material used for ethanol production, which is primarily derived from sugarcane juice and molasses. The latter, in particular, has been increasingly utilized in must formulations. Additionally, the industry has efficiently explored the residues of the production process [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the few byproducts that are not utilized by industry itself is fusel oil, a mixture of higher alcohols that can be generated during fermentation and obtained during distillation. Its main constituents are isoamyl alcohol and isobutanol [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe average production of fusel oil from sugarcane molasses is 2.5 L for every 1000 L of ethanol produced [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Based on the ethanol production from the 2022/2023 harvest, which reached 28.91\u0026nbsp;billion liters [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], it is possible to estimate that the fusel oil production was approximately 72.28\u0026nbsp;million liters. Considering the estimated ethanol production of 41.857\u0026nbsp;billion liters for the 2028/2029 harvest, fusel oil production may reach 104.64\u0026nbsp;million liters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of fusel oil during fermentation adversely affects the distillation and ethanol quality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. According to Patil et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the literature reports research aimed at reducing the formation of higher alcohols during fermentation. However, decreasing fusel oil formation without negatively impacting ethanol production remains a challenge. According to the authors, there are already some mutant yeasts that produce only traces of higher alcohols, but they do not produce sufficient ethanol to be taken as industrially feasible.\u003c/p\u003e \u003cp\u003eIn this context, research efforts have been initiated with the aim of harnessing fusel oil, which has become an object of interest due to its broad applicability, especially in the chemical industry where isoamyl alcohol and isobutanol are recovered for the production of biosolvents, extractors, flavorings, medicine, and plasticizers. This is particularly relevant in countries where large-scale production is prevalent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe industries that use fusel oil for the recovery of higher alcohols mostly employ the distillation process for isoamyl alcohol and isobutanol [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the recovery of isobutanol involves the additional use of one or more distillation columns, leading to increased energy expenditure. This enhances the value of fusel oil, which has a higher concentration of isoamyl alcohol than isobutanol. This has attracted increased interest in the development of processes that lead to the formation of fusel oil with a greater ratio of these alcohols.\u003c/p\u003e \u003cp\u003eIn the literature, studies indicate the interference of reaction factors in the formation of higher alcohols, which can result in higher or lower quantities of these compounds, possibly leading to the formation of isoamyl alcohol and isobutanol in varying proportions. Among other factors, the type of raw material used, the method of must preparation, and the conditions under which fermentation takes place can contribute to these differences [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Patil et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported that the composition of fusel oil varies widely, especially concerning the isoamyl alcohol content, which can vary by 45%.\u003c/p\u003e \u003cp\u003eRecent research has focused on the isoamyl alcohol present in fusel oil, both during the distillation process for extraction and in various applications [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt should be emphasized that the studies reported in the literature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] were mostly conducted with beverages and other synthetic media distinct from sugarcane molasses at the laboratory scale and under optimized conditions, unlike industrial conditions, especially in Brazilian sugar and ethanol mills.\u003c/p\u003e \u003cp\u003eIn light of this context, it becomes essential to investigate conditions closer to those used by the industry, evaluating fermentations with must formulated from the most commonly used raw materials and factors that can be easily manipulated under industrial conditions.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to assess the potential influence of pH, refrigeration, and supplementation factors on the formation of isoamyl alcohol and isobutanol during the fermentation of sugarcane molasses must in a microdistillery.\u003c/p\u003e \u003cp\u003eThe results obtained can contribute to understanding the factors that affect the production of these alcohols, and they can be useful for optimizing fermentation processes on an industrial scale.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInoculum and fermentation medium\u003c/h2\u003e \u003cp\u003eThe sugarcane molasses was obtained from a sugarcane industry in Alagoas, Brazil, for use in both inoculum preparation and fermentations. Initially, the inoculum was prepared by suspending 800 g of commercial dry \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e (Fleischmann) in 10 L of sugarcane molasses must at 5 \u0026deg;Brix, and supplemented with 1 g/L ammonium sulfate. This procedure resulted in enough cells to reach a concentration between 10\u003csup\u003e8\u003c/sup\u003e and 10\u003csup\u003e9\u003c/sup\u003e cells/mL, with a cellular viability exceeding 85%.\u003c/p\u003e \u003cp\u003eAfter that, the must used in fermentations was prepared by diluting the molasses with water to a concentration of 25 \u0026deg;Brix. The pH of this mixture was adjusted using sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e). No sterilization was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMicrodistillery\u003c/h2\u003e \u003cp\u003eThe fermentation experiments were conducted in a microdistillery for ethanol production located in the Laboratory of Industrial Processes (LAPIND) at the Federal Institute of Alagoas, Brazil \u0026ndash; Penedo campus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFermentation conditions\u003c/h2\u003e \u003cp\u003eThe fermentations were carried out in triplicate in 60 L fermentation vessels equipped with refrigeration coils. Ten liters of inoculum were transferred to each vessel, followed by the addition of 30 L of must at a concentration of 25\u0026deg;Brix over a feeding time of 1 hour.\u003c/p\u003e \u003cp\u003eThe experiments were conducted at room temperature, and refrigeration, when necessary, was achieved by cold water circulation. Fermentation was carried out for 10 hours, and Brix, pH, and temperature were monitored every hour.\u003c/p\u003e \u003cp\u003eSamples were taken at the beginning and at the end of the fermentations and were subsequently centrifuged, and the supernatant was stored in the refrigerator for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical methods\u003c/h2\u003e \u003cp\u003eThe cell concentration was determined through spectrophotometry by measuring the absorbance of the samples diluted at a 1:50 ratio in a UV-visible spectrophotometer (NOVAINSTRUMENTS SERIES 2000) at a wavelength of 600 nm. The absorbance values were converted to a cell concentration in g/L using a standard curve that relates the absorbance to the dry mass of the yeast.\u003c/p\u003e \u003cp\u003eThe total soluble solids (\u0026deg;Brix) were determined by refractometry using a portable digital refractometer (HANNA HI 96801) with automatic temperature compensation and a measurement range from 0 to 85 \u0026deg;Brix.\u003c/p\u003e \u003cp\u003eThe pH was measured using a digital benchtop pH meter, (a PHTER PHS-3B model), which was previously calibrated with pH 4.0 and pH 7.0 standard solutions, with temperature control.\u003c/p\u003e \u003cp\u003eThe concentrations of total reducing sugars (TRS), including glucose, fructose, and sucrose, were determined by high-performance liquid chromatography (HPLC) using a SHIMADZU LC-20AT chromatograph equipped with a refractive index detector (RID) and an Aminex HPX-87H column (300 x 7.8 mm). The column oven and detector temperatures were maintained at 65\u0026deg;C and 50\u0026deg;C, respectively. A 5 mM sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) solution was used as the mobile phase at a flow rate of 0.6 mL/min, and the sample injection volume was 25 \u0026micro;L.\u003c/p\u003e \u003cp\u003eThe concentrations of ethanol, isoamyl alcohol (A), and isobutanol (B) were determined by gas chromatography (GC) on a SHIMADZU QP2010S chromatograph equipped with a flame ionization detector (FID). The column used was a Sh-Rtx-2330 (Restek), with a length of 30 m, an internal diameter of 0.32 mm, and a 0.20 \u0026micro;m film. The carrier gas flow (N\u003csub\u003e2\u003c/sub\u003e/Ar) was set at 1.30 mL/min. The injection volume was 1 \u0026micro;L, and a \"split\" injection system with a 1:50 ratio was used. The temperature program applied was as follows: 50\u0026deg;C for 2 min, from 50 to 190\u0026deg;C at 10\u0026deg;C/min, from 190\u0026deg;C for 5 min, and from 190 to 230\u0026deg;C at 20\u0026deg;C/min for 7 min. The injector and detector temperatures were set at 250\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePerformance parameters of the fermentation process\u003c/h2\u003e \u003cp\u003eThe fermentation efficiency (η\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, in %) was calculated considering the theoretical yield of 0.511 g\u003csub\u003eethanol\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e (100%), according to Eq.\u0026nbsp;(1), where ΔE is the ethanol produced (final ethanol concentration \u0026ndash; initial ethanol concentration) and ΔS is the consumed sugar (TRS initial \u0026ndash; TRS final), both in g/L.\u003c/p\u003e \u003cp\u003eη\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e = (ΔE/(ΔS. 0.511). 100) (1)\u003c/p\u003e \u003cp\u003eThe process efficiency (η\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, in %) was calculated considering the initial sugar concentration (TRS initial), according to Eq.\u0026nbsp;(2).\u003c/p\u003e \u003cp\u003eη\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔE/(0.511. TRS initial). 100 (2)\u003c/p\u003e \u003cp\u003eThe ethanol productivity (P, in g\u003csub\u003eethanol\u003c/sub\u003e/L.h) was calculated with Eq.\u0026nbsp;(3), considering a fermentation time (t) of 10 h.\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;ΔE/t (3)\u003c/p\u003e \u003cp\u003eThe substrate-to-cell conversion factor (Y\u003csub\u003eX/S\u003c/sub\u003e, in g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e) was calculated with Eq.\u0026nbsp;(4), where ΔX is the cells produced (final cell concentration \u0026ndash; initial cell concentration), in g/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eY\u003csub\u003eX/S\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔX/ΔS (4)\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eExperimental design and statistical analysis\u003c/h2\u003e \u003cp\u003eA complete 2\u0026sup3; factorial design was used (for a total of 8 experiments) with the following independent variables: pH, refrigeration of the vessel, and supplementation of the must with ammonium sulfate. The dependent variables included fermentation efficiency (n\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, in %), process efficiency (n\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, in %), ethanol productivity (P, in g/L.h), substrate-to-cell conversion factor (Y\u003csub\u003eX/S\u003c/sub\u003e, in g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e), isoamyl alcohol produced (A, in g/L), isobutanol produced (B, in g/L) and the A/B ratio. All the experiments were conducted in triplicate in a random order. The obtained data for isoamyl alcohol produced (A, in g/L), isobutanol produced (B, in g/L), and the A/B ratio were adjusted to the polynomial of Eq.\u0026nbsp;(5), where Y represents the response variable, \u003cem\u003eβi\u003c/em\u003e and \u003cem\u003eβij\u003c/em\u003e are the regression coefficients, and PH, R and S are the independent variables, pH, refrigeration, and supplementation, respectively.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003ePH\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003eR\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003eS\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e12\u003c/em\u003e\u003c/sub\u003ePH*R\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e13\u003c/em\u003e\u003c/sub\u003ePH*S\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e23\u003c/em\u003e\u003c/sub\u003eR*S\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e123\u003c/em\u003e\u003c/sub\u003ePH*R*S (5)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows the real and coded independent variables used in the experimental design.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplementation (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefrigeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewith\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Real and coded levels of the independent variables used in the experimental design.\u003c/p\u003e \u003cp\u003eFor the statistical analysis of the data, analysis of variance (ANOVA) and Tukey's test were used for the comparison of means. The adopted significance level was 5%.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003ePerformance parameters of the fermentation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of higher alcohols formation must be conducted under fermentation conditions that provide performances comparable to those found in industry and the scientific literature. Thus, some parameters were calculated to evaluate the performance of the fermentation process concerning the microorganism employed and the target product of interest for the industry.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the analysis of variance (ANOVA) of 2\u0026sup3; factorial design for the responses\u0026nbsp;\u0026eta;\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e,\u0026nbsp;\u0026eta;\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e, P, and Y\u003csub\u003eX/S\u003c/sub\u003e. The results indicate that the treatments were statistically significant (p-value \u0026lt; 0.05), suggesting the presence of significant differences in the means of fermentation conditions concerning these response variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eY\u003csub\u003eX/S\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e0.021567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e0.003081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e0.00557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e0.000348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e0.027137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e4348.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e621.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e21.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e467.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e29.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e4815.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e5947.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e849.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e33.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e406.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e25.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e6354.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e13.4103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e1.91575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e2.2381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e0.13988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.820037105751393%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.19109461966605%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.923933209647494%\"\u003e\n \u003cp\u003e15.6483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.769944341372913%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.059369202226344%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eY\u003csub\u003eX/S\u003c/sub\u003e (g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e) substrate-to-cell conversion factor; n\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e (%) fermentation efficiency; n\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e (%) process efficiency; P (g/L.h) ethanol productivity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e ANOVA of the factorial design for the response variables\u0026nbsp;\u0026eta;\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e,\u0026nbsp;\u0026eta;\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e, P and Y\u003csub\u003eX/S\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe means of the results obtained under the different fermentation conditions used are presented in Table 3.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" rowspan=\"3\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003eFermentation condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.15435139573071%\" colspan=\"6\" style=\"width: 17.7591%;\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.216748768472904%\" colspan=\"4\" style=\"width: 28.9978%;\"\u003e\n \u003cp\u003eResults (mean \u0026plusmn; sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.799256505576208%\" colspan=\"3\" style=\"width: 1.7006%;\"\u003e\n \u003cp\u003eCoded values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.144981412639407%\" colspan=\"3\" style=\"width: 16.0585%;\"\u003e\n \u003cp\u003eReal values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.4089219330855%\" colspan=\"4\" style=\"width: 28.9978%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.267161410018553%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.565862708719852%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003eSup.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.380333951762523%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.452690166975882%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8645640074211505%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003eSup.\u003cbr\u003e\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.955473098330241%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003eY\u003csub\u003eX/S\u003c/sub\u003e (g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.213358070500927%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003en\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.84230055658627%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003en\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471243042671615%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003eP (g/L.h)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e45.65 \u0026plusmn; 5.19\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e35.68 \u0026plusmn; 3.30\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e3.92 \u0026plusmn; 0.14\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.18 \u0026plusmn; 0.01\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e77.24 \u0026plusmn; 2.49\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e75.35 \u0026plusmn; 3.37\u003csup\u003ea b c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e5.23 \u0026plusmn; 0.44\u003csup\u003eb c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.22 \u0026plusmn; 0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e88.85 \u0026plusmn; 5.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e75.86 \u0026plusmn; 5.39\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e5.40 \u0026plusmn; 0.43\u003csup\u003ea b c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.00\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e87.31 \u0026plusmn; 5.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e87.17 \u0026plusmn; 5.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e6.03 \u0026plusmn; 0.09\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.15 \u0026plusmn; 0.00\u003csup\u003eb c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e90.23 \u0026plusmn; 5.93\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e88.77 \u0026plusmn; 5.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e6.44 \u0026plusmn; 0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.18 \u0026plusmn; 0.03\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e70.97 \u0026plusmn; 7.32\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e61.21 \u0026plusmn; 6.26\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e4.66 \u0026plusmn; 0.51\u003csup\u003ec d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.18 \u0026plusmn; 0.01\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e78.72 \u0026plusmn; 5,14\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e69.76 \u0026plusmn; 5.04\u003csup\u003eb c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e4.68 \u0026plusmn; 0.54\u003csup\u003ec d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.494252873563218%\" style=\"width: 12.1305%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.7766830870279144%\" style=\"width: 2.8882%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.926108374384237%\" style=\"width: 4.159%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.761904761904762%\" style=\"width: 3.928%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9408866995073892%\" style=\"width: 3.8125%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.075533661740558%\" style=\"width: 5.3143%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.22495894909688%\" style=\"width: 7.0473%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.12151067323481%\" style=\"width: 11.784%;\"\u003e\n \u003cp\u003e0.17 \u0026plusmn; 0.03\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" style=\"width: 5.5454%;\"\u003e\n \u003cp\u003e79.40 \u0026plusmn; 5.24\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.136288998357964%\" style=\"width: 5.1988%;\"\u003e\n \u003cp\u003e75.25 \u0026plusmn; 5.34\u003csup\u003ea b c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.807881773399014%\" style=\"width: 6.4696%;\"\u003e\n \u003cp\u003e5.21 \u0026plusmn; 0.37\u003csup\u003eb c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeans followed by the same lowercase letter in the column do not differ by Tukey\u0026apos;s test at 5% significance level\u003c/p\u003e\n\u003cp\u003eY\u003csub\u003eX/S\u003c/sub\u003e (g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e) substrate-to-cell conversion factor; n\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e (%) fermentation efficiency; n\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e (%) process efficiency; P (g/L.h) ethanol productivity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eResults obtained by 2\u0026sup3; factorial design.\u003c/p\u003e\n\u003cp\u003eThe substrate-to-cell conversion factor (Y\u003csub\u003eX/S\u003c/sub\u003e) met the criteria established in the literature [24,25], indicating successful yeast development under the adopted fermentation conditions (Table 3).\u003c/p\u003e\n\u003cp\u003eThe fermentation conditions 4 and 5 (Table 3) resulted in lower values for Y\u003csub\u003eX/S\u003c/sub\u003e (0.11 \u0026plusmn; 0.00 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e and 0.15 \u0026plusmn; 0.00 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e, respectively). It is important to highlight that fermentation condition 4 differed significantly from the other conditions (p \u0026lt; 0,05), except for condition 5.\u003c/p\u003e\n\u003cp\u003eIn the literature, a wide range of Y\u003csub\u003eX/S\u003c/sub\u003e values obtained in fermentations with ethanol-producing yeasts has been reported. According to Stroppa et al. [24], the reported values range from 0.03 to 0.28 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e. The authors conducted fermentations with yeasts isolated from distilleries in sugarcane must at a concentration of 9.4 \u0026deg;Brix, temperature of 30\u0026deg;C for 24 hours, and obtained values of 0.179 and 0.185 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e for the strains RM01 and CV01, respectively.\u003c/p\u003e\n\u003cp\u003eColombi et al. [25] evaluated the influence of different compounds, such as vanillin, acetic acid, vanillic acid, and 4-hydroxybenzoic acid, on the fermentation of glucose at 40 g/L by the yeast \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e JP1. The authors conducted fermentation at 30\u0026deg;C and 150 rpm for 22 hours, the pH was adjusted to 4.9, and Y\u003csub\u003eX/S\u003c/sub\u003e values ranging from 0.00 to 0.22 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u0026nbsp;\u003c/sub\u003ewere obtained.\u003c/p\u003e\n\u003cp\u003eAlves [26] conducted fermentations with molasses must at 40 g/L supplemented with 2.5 g/L yeast extract, using an industrial strain of \u003cem\u003eSaccharomyces cerevisiae\u0026nbsp;\u003c/em\u003ewithin a temperature range varying from 28 to 38\u0026deg;C. The obtained values ranged between 0.087 and 0.099 g\u003csub\u003eyeast\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe wide variation in Y\u003csub\u003eX/S\u003c/sub\u003e values reported in the literature resulted from different process conditions and raw materials used, as well as yeast strains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Table 3, fermentation condition 1, with a pH of 3.5, and without the addition of ammonium sulfate and without refrigeration, exhibited unsatisfactory performance compared to the other tested conditions, as fermentation efficiency, process efficiency, and ethanol productivity were low.\u003c/p\u003e\n\u003cp\u003eFermentation conditions 3, 4, and 5 showed better performance with higher fermentation and process efficiencies, and ethanol productivity. When comparing these three experiments, it is evident that condition 5, with a pH of 3.5, no ammonium sulfate supplementation, and must refrigeration, achieved the highest process performance indicators.\u003c/p\u003e\n\u003cp\u003eIn the case of the need to achieve good yeast productivity, the optimal condition was number 3 (pH 3.5, supplementation of\u0026nbsp;1 g/L ammonium sulfate, and no refrigeration), which significantly differed (p \u0026lt; 0.05) from conditions 4 and 5 (Table 3).\u003c/p\u003e\n\u003cp\u003eAn analysis of the data presented in Table 3\u0026nbsp;suggested\u0026nbsp;consistency with the results described in the scientific literature (Table 4). Furthermore, the fermentation conditions 3, 4, and 5 were appropriate, resulting in satisfactory fermentation performance.\u003c/p\u003e\n\u003cp\u003eThe differences in the results reported in the literature (Table 4) are attributed to the different conditions adopted by the authors, including must composition, fermentation time, and yeast strain used.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003eMicroorganism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003eFermentation time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003en\u003cem\u003e\u003csub\u003ef\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003en\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003e[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003esugarcane synthetic must (160 g/L of sugars)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e CAT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003e30\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003e72 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e90.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003e[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003enon-sterile molasses must (26\u0026deg;Brix)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e CAT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003e30\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003e24 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e79.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003e4.27 g/Lh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003e[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003emolasses + sugarcane juice must (270 g/L of sugars)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u0026nbsp;\u003c/em\u003eY-904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003e32\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003e24 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003e92.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003e4.27 g/Lh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003e[30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003esynthetic must (250 g/L of sugars)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e flocculant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003e28\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003e12 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003e82.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003e9.6 g/Lh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.379310344827585%\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.20689655172414%\"\u003e\n \u003cp\u003esterile sugarcane juice must (25\u0026deg;Brix)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.75862068965517%\"\u003e\n \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e CAT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.551724137931034%\"\u003e\n \u003cp\u003e30\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\"\u003e\n \u003cp\u003e24 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e92.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.758620689655173%\"\u003e\n \u003cp\u003enr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.655172413793103%\"\u003e\n \u003cp\u003e4.69 g/Lh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003enr not reported\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003ePerformance parameters of the fermentation process,\u0026nbsp;which are reported in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsoamyl alcohol production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant effects on isoamyl alcohol production (A) can be seen in the Pareto chart in Figure 1.\u003c/p\u003e\n\u003cp\u003eThere was no significant third-order interaction effect. Among the main effects, only supplementation had a significant effect (p\u0026lt;0.05) on A, but the second-order interaction effects, pH*Ref. and Sup.*Ref., were also significant. Therefore, factors should be evaluated together, as the response obtained by varying one factor depends on the levels of the other factors.\u003c/p\u003e\n\u003cp\u003eThe regression model with coded units, considering only the significant effects and relating isoamyl alcohol production to the factors pH, refrigeration, and supplementation is given by Equation (5). The corresponding graphs can be viewed in Figures 2a and 2b.\u003c/p\u003e\n\u003cp\u003eISOAM. (A) = 0.2548 + 0.0488 Sup. \u0026ndash; 0.0424 pH*Ref. \u0026ndash; 0.0310 Sup.*Ref.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(5)\u003c/p\u003e\n\u003cp\u003eThe ANOVA results for the regression model of Equation (5) are presented in Table 5.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.12359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.041197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eLack of fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.02715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003ePure error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.05495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.20568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eR\u0026sup2; = 60.09%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e ANOVA results for the regression model for the response variable isoamyl alcohol produced (A).\u003c/p\u003e\n\u003cp\u003eWhen evaluating the pH*Ref. interaction (Figures. 2a and 2b), it\u0026nbsp;was\u0026nbsp;observed that, in fermentations conducted without refrigeration, an increase in pH and must supplementation led to an increase in isoamyl alcohol production (A). In these same figures, it is noted that by maintaining the absence of refrigeration and decreasing both the pH and must supplementation, there is a decrease in isoamyl alcohol production (A).\u003c/p\u003e\n\u003cp\u003eIn the Sup.*Ref. interaction (Figure 2b), it was observed that must supplementation increased isoamyl alcohol production (A), both in fermentations conducted without refrigeration and those with refrigeration, confirming the significant effect of supplementation (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsobutanol production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pareto chart in Figure 3 shows that the main effects - pH, refrigeration, and supplementation - were significant, and additionally, the second-order interaction effects - pH*Ref. and Sup.*Ref. - were also significant. Therefore, these factors should be evaluated together.\u003c/p\u003e\n\u003cp\u003eConsidering only significant effects, the regression model with uncoded units relates isobutanol production to the factors pH, refrigeration, and supplementation, as represented in Equation (6). The corresponding graphs can be viewed in Figures 4a and 4b.\u003c/p\u003e\n\u003cp\u003eISOBU. (B) = \u0026ndash; 0.0222 + 0.03120 pH + 0.0549 Sup. + 0.1269 Ref. \u0026ndash; 0.02964 pH*Ref. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ndash; 0.0394 Sup.*Ref.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(6)\u003c/p\u003e\n\u003cp\u003eThe ANOVA\u0026nbsp;results for\u0026nbsp;the regression model of Eq. 6\u0026nbsp;are\u0026nbsp;presented in Table 6.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.251417769376182%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.251417769376182%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76937618147448%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.068052930056712%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.3724007561436675%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287334593572778%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76937618147448%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.06082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.068052930056712%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.012163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.3724007561436675%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287334593572778%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003eLack of fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76937618147448%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.068052930056712%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.3724007561436675%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287334593572778%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003ePure error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76937618147448%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.068052930056712%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.3724007561436675%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287334593572778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.251417769376182%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.76937618147448%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.07945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.068052930056712%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.3724007561436675%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287334593572778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eR\u0026sup2; = 76.55%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e ANOVA of the regression model for the response variable isobutanol produced (B).\u003c/p\u003e\n\u003cp\u003eAn examination of Figures 4a and 4b shows that the production of isobutanol (B) exhibited a similar trend to that of isoamyl alcohol production (A) (Figures 2a and 2b) concerning the interaction effects of pH*Ref. and Sup.*Ref. In other words, in fermentations conducted without refrigeration, an increase in pH and must supplementation increased isobutanol production (B).\u003c/p\u003e\n\u003cp\u003eAs shown in Figures 4a and 4b, must supplementation and\u0026nbsp;an\u0026nbsp;increase in pH increased isobutanol production (B), both in fermentations conducted without refrigeration and in those conducted with refrigeration, confirming the significant effects of supplementation and pH (Figure 3). However, must refrigeration resulted in a less significant increase in isobutanol production (B), also confirming the important effect of refrigeration (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA/B ratio between isoamyl alcohol (A) and isobutanol (B) produced\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in the Pareto chart in Figure 5, only the main effects of pH and refrigeration were significant. Additionally, the second-order interaction effects pH*Ref. and pH*Sup., as well as the third-order effect pH*Sup.*Ref. were also significant. Therefore, these factors should be evaluated together.\u003c/p\u003e\n\u003cp\u003eThe regression model with coded units, considering only the significant effects and relating the A/B ratio to the factors pH, refrigeration, and supplementation, is represented in Equation (7). The corresponding graphs can be viewed in Figures 6a and 6b.\u003c/p\u003e\n\u003cp\u003eA/B RATIO = 1.8828 \u0026ndash; 0.1482 pH + 0.0955 Ref. + 0.0274 pH*Sup. - 0.0524 pH*Ref. + 0.0778 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;pH*Sup.*Ref.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(7)\u003c/p\u003e\n\u003cp\u003eThe ANOVA\u0026nbsp;results for\u0026nbsp;the regression model of Eq. 7 are presented in Table 7.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\"\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\"\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.97470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.19494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eLack of fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003ePure error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.04330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.69942196531792%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.11175337186898%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.02257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.377649325626205%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.514450867052023%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.597302504816955%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eR\u0026sup2; = 95.32%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u003c/strong\u003e ANOVA of the regression model for the response variable A/B ratio.\u003c/p\u003e\n\u003cp\u003eWhen evaluating the pH*Ref. interaction (Figure 6a), it was observed that the peak of the A/B ratio occurred at a lower pH and under refrigeration, while the lowest A/B ratio was obtained at a higher pH and without refrigeration.\u003c/p\u003e\n\u003cp\u003eIn the pH*Sup. interaction (Figure 6b), the peak of A/B ratio occurred at a lower pH and without must supplementation. Conversely, the opposite trend\u0026nbsp;was\u0026nbsp;observed with higher pH and must supplementation.\u003c/p\u003e\n\u003cp\u003eAs shown in Figures 6a and 6b, the influence of pH on the A/B ratio is clear, with a lower pH favoring an increase in this ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive analysis of the results: isoamyl alcohol produced (A), isobutanol produced (B) and the A/B ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results presented in Table 8 indicate significant differences among the fermentation conditions for the evaluated response variables (production of isoamyl alcohol (A), isobutanol (B), and the A/B ratio).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" rowspan=\"3\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003eFermentation condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.521815008726%\" colspan=\"6\" style=\"width: 20.5342%;\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.167539267015705%\" colspan=\"3\" style=\"width: 28.5092%;\"\u003e\n \u003cp\u003eResults (mean \u0026plusmn; sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.733067729083665%\" colspan=\"3\" style=\"width: 12.1101%;\"\u003e\n \u003cp\u003eCoded values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.709163346613547%\" colspan=\"3\" style=\"width: 16.7775%;\"\u003e\n \u003cp\u003eReal values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.9800796812749%\" colspan=\"3\" style=\"width: 28.5092%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.572564612326044%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.361829025844931%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003eSup.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.7654075546719685%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.343936381709742%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003eSup.\u003cbr\u003e\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.747514910536779%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47713717693837%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003eIsoamyl alcohol (A)\u003cbr\u003e\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.08548707753479%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003eIsobutanol (B)\u003cbr\u003e\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.308151093439363%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003eA/B Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.1487 \u0026plusmn; 0.0189\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.0811 \u0026plusmn; 0.0113\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.84 \u0026plusmn; 0.03\u003csup\u003ec d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.2406 \u0026plusmn; 0.0040\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1379 \u0026plusmn; 0.0046\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.75 \u0026plusmn; 0.03\u003csup\u003ed e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.2717 \u0026plusmn; 0.0034\u003csup\u003ea b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1409 \u0026plusmn; 0.0055\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.93 \u0026plusmn; 0.06\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.4372 \u0026plusmn; 0.0445\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.2666 \u0026plusmn; 0.0180\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.64 \u0026plusmn; 0.06\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.2514 \u0026plusmn; 0.0746\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1119 \u0026plusmn; 0.0373\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e2.26 \u0026plusmn; 0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.1831 \u0026plusmn; 0.0873\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1109 \u0026plusmn; 0.0529\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 0.02\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.2598 \u0026plusmn; 0.1047\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1240 \u0026plusmn; 0.0493\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e2.09 \u0026plusmn; 0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.216404886561955%\" style=\"width: 12.9931%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.013961605584642%\" style=\"width: 3.1537%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5846422338568935%\" style=\"width: 4.6674%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.061082024432809%\" style=\"width: 4.289%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.18848167539267%\" style=\"width: 3.2798%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.202443280977313%\" style=\"width: 5.9289%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.678883071553229%\" style=\"width: 7.4427%;\"\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.975567190226876%\" style=\"width: 11.7317%;\"\u003e\n \u003cp\u003e0.2460 \u0026plusmn; 0.0309\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" style=\"width: 11.3532%;\"\u003e\n \u003cp\u003e0.1297 \u0026plusmn; 0.0198\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" style=\"width: 5.4243%;\"\u003e\n \u003cp\u003e1.90 \u0026plusmn; 0.05\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeans followed by the same lowercase letter in the column do not differ by Tukey\u0026apos;s test at 5% significance level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e8.\u003c/strong\u003e Isoamyl alcohol and isobutanol produced under the different fermentation conditions.\u003c/p\u003e\n\u003cp\u003eThe fermentations conducted at pH 5.0, with must supplementation and without refrigeration (fermentation condition 4), resulted in greater formation of isoamyl alcohol and isobutanol. However, the A/B ratio was low under this condition. Despite a low substrate-to-cell conversion (Y\u003csub\u003eX/S\u003c/sub\u003e = 0.11 g\u003csub\u003eyeasts\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e), both the fermentation and process efficiencies were satisfactory, as was the high ethanol productivity (87.31%, 87.17%, and 6.03 g/L.h, respectively). These results are consistent with previous studies by Pons et al. [32] and Cachot et al. [33], which demonstrated a positive correlation between the formation of these alcohols and ethanol production. In fact, our results indicate that fermentation conditions leading to higher ethanol production also resulted in increased formation of isoamyl alcohol and isobutanol.\u003c/p\u003e\n\u003cp\u003eThe evaluated factors (pH, refrigeration, and supplementation) simultaneously affect the production of isoamyl alcohol and isobutanol. However, at pH 3.5, the formation of isobutanol was lower than that at other pH values, resulting in a higher A/B ratio between these alcohols. Therefore, a lower pH is suggested to favor an increase in the A/B ratio.\u003c/p\u003e\n\u003cp\u003eFermentation condition 5 exhibited the highest A/B ratio (2.26) and significantly differed from the other conditions. Despite a low substrate-to-cell conversion (Y\u003csub\u003eX/S\u003c/sub\u003e =0.15 g\u003csub\u003eyeasts\u003c/sub\u003e/g\u003csub\u003eTRS\u003c/sub\u003e), both fermentation and process efficiencies, as well as ethanol productivity, were high (90.23%, 88.77%, and 6.44 g/L.h, respectively).\u003c/p\u003e\n\u003cp\u003eEven though it was not possible to establish a condition that reduces fusel oil formation, the obtained results are consistent with previous studies [34,35,36], which report the influence of different factors on the production of higher alcohols during fermentation. These results underscore the importance of jointly evaluating the studied factors (pH, refrigeration, and supplementation), considering the interaction effects that occur among them.\u003c/p\u003e\n\u003cp\u003eTherefore, the results provide a better understanding of these interactions and their impact on the formation of isoamyl alcohol and isobutanol, which can contribute to the development of more efficient processes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe factors pH, refrigeration, and supplementation with ammonium sulfate significantly influenced the formation of isoamyl alcohol and isobutanol during the fermentation of sugarcane molasses must in a microdistillery.\u003c/p\u003e \u003cp\u003eThe different fermentation conditions tested showed significant differences in terms of the response variables η\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, η\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, P and Y\u003csub\u003eX/S\u003c/sub\u003e. Specifically, fermentation conditions 3, 4, and 5 demonstrated better performance in terms of fermentation and process efficiencies (η\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, η\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), while the substrate-to-cell conversion factor (Y\u003csub\u003eX/S\u003c/sub\u003e) was considered satisfactory under all tested conditions, indicating that the yeasts developed well under these circumstances.\u003c/p\u003e \u003cp\u003eThe interaction effects among the factors were significant for the production of isoamyl alcohol, isobutanol, and the A/B ratio. Ammonium sulfate supplementation promoted an increase in the production of both isoamyl alcohol and isobutanol, regardless of the pH. On the other hand, refrigeration increased isobutanol production at pH 3.5 and decreased at pH 5.0, with a reduction in the A/B ratio observed as the pH increased from 3.5 to 5.0.\u003c/p\u003e \u003cp\u003eTherefore, the selection of the best fermentation condition will depend on the specific needs of the process, such as yeast productivity, and the feasibility of refrigeration and must supplementation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to R.A.S.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors reviewed the manuscript. R.S., S.A., and Y.A. wrote the main manuscript text. R.S. and C.C. performed experiments. Y.A., C.S.C., S.A., and J.F. contributed to the analysis methodologies and supervised the experiments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRibeiro, E. J. \u0026amp; Reis, H. B. Influ\u0026ecirc;ncia conjunta do pH, temperatura e concentra\u0026ccedil;\u0026atilde;o de sulfito em fermenta\u0026ccedil;\u0026atilde;o alco\u0026oacute;lica de mostos de sacarose. IX encontro e XIII semin\u0026aacute;rio de inicia\u0026ccedil;\u0026atilde;o cientifica. (2009).\u003c/li\u003e\n\u003cli\u003eSuslick, K. S. Kirk-Othmer encyclopedia of chemical technology. \u003cem\u003eJ. Wiley \u0026amp; Sons.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 517-541 (1998).\u003c/li\u003e\n\u003cli\u003eQian, Y., Ouyang, L, Wang, X., Zhu, L. \u0026amp; Lu, X. Experimental studies on combustion and emissions of RCCI fueled with n-heptane/alcohols fuels. \u003cem\u003eFuel.\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, 239-250. https://doi.org/10.1016/j.fuel.2015.09.022 (2015).\u003c/li\u003e\n\u003cli\u003eArdebili, S. M. S., Solmaz, H. \u0026amp; Mostafaei, M. Optimization of fusel oil - gasoline blend ratio to enhance the performance and reduce emissions. \u003cem\u003eApplied Thermal Engineering\u003c/em\u003e. \u003cstrong\u003e148\u003c/strong\u003e, 1334-1345. https://doi.org/10.1016/j.applthermaleng.2018.12.005 (2019).\u003c/li\u003e\n\u003cli\u003eDa Silveira, E. Res\u0026iacute;duos Reciclados: \u0026Oacute;leo f\u0026uacute;sel e CO\u003csub\u003e2\u003c/sub\u003e gerados na fabrica\u0026ccedil;\u0026atilde;o de etanol podem ser aproveitados na produ\u0026ccedil;\u0026atilde;o de subst\u0026acirc;ncias qu\u0026iacute;micas de uso industrial. \u003cem\u003eRevista Pesquisa Fapesp\u003c/em\u003e. \u003cstrong\u003e240\u003c/strong\u003e, 68-69. https://revistapesquisa.fapesp.br/wp-content/uploads/2016/02/068-069_%C3%93leo-fusel_240.pdf. (2016).\u003c/li\u003e\n\u003cli\u003eUni\u0026atilde;o da Ind\u0026uacute;stria de Cana-de-A\u0026ccedil;\u0026uacute;car (UNICA) N\u0026uacute;meros finais da safra de cana-de-a\u0026ccedil;\u0026uacute;car 2022/23. https://unicadata.com.br/arquivos/pdfs/2023/04/44e3035ee7fde214d3a491d84b291fe5.pdf (2023).\u003c/li\u003e\n\u003cli\u003eFEDERA\u0026Ccedil;\u0026Atilde;O DAS IND\u0026Uacute;STRIAS DO ESTADO DE S\u0026Atilde;O PAULO (FIESP). Outlook Fiesp 2028.http://outlookdeagro.azurewebsites.net/OutLookDeagro/pt-BR/Publicacao/Etanol/Brasil (2020).\u003c/li\u003e\n\u003cli\u003eBatista, F. R. M., Follegatti-Romero, L. A., Bessa, L. C. B. A. \u0026amp; Meirelles, A. J. A. Computational simulation applied to the investigation of industrial plants for bioethanol distillation. \u003cem\u003eComputers and Chemical Engineering\u003c/em\u003e, \u003cstrong\u003e46\u003c/strong\u003e, 1-16. https://doi.org/10.1016/j.compchemeng.2012.06.004 (2012).\u003c/li\u003e\n\u003cli\u003ePatil, A. G., Koolwal, S. M. \u0026amp; Butala, H. D. Fusel oil: composition, removal and potential utilization. \u003cem\u003eInternational Sugar Journal\u003c/em\u003e, \u003cstrong\u003e104\u003c/strong\u003e, 51-58. (2002)\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;ven\u0026ccedil;, A., Kapucu, N., Kapucu, H., Aydoğan, \u0026Ouml;. \u0026amp; Mehmetoğlu, \u0026Uuml;. Enzymatic esterification of isoamyl alcohol obtained from fusel oil: optimization by response surface methodology. \u003cem\u003eEnzyme and Microbial Technology,\u003c/em\u003e \u003cstrong\u003e40 (4)\u003c/strong\u003e, 778-785. https://doi.org/10.1016/j.enzmictec.2006.06.010 (2007).\u003c/li\u003e\n\u003cli\u003eMontoya, N. et al. Colombian fusel oil. \u003cem\u003eIngenier\u0026iacute;a e Investigaci\u0026oacute;n\u003c/em\u003e, \u003cstrong\u003e36(2)\u003c/strong\u003e, 21-27. http://dx.doi.org/10.15446/ing.investig.v36n2.52369 (2016).\u003c/li\u003e\n\u003cli\u003eKirdi, R., Ben Akacha, N., Messaoudi, Y. \u0026amp; Gargouri, M. Enhanced synthesis of isoamyl acetate using liquid‒gas biphasic system by the transesterification reaction of isoamyl alcohol obtained from fusel oil. \u003cem\u003eBiotechnology and Bioprocess Engineering\u003c/em\u003e, \u003cstrong\u003e22(4)\u003c/strong\u003e, 413-422. https://doi.org/10.1007/s12257-016-0616-4 (2017).\u003c/li\u003e\n\u003cli\u003eMassa, T. B., Raspe, D. T., Feiten, M. C., Cardozo-Filho, L. \u0026amp; Silva, C. Fusel oil: chemical composition and an overview of its potential application. \u003cem\u003eJournal of the Brazilian Chemical Society\u003c/em\u003e, \u003cstrong\u003e34\u003c/strong\u003e, 153-166. https://doi.org/10.21577/0103-5053.20220145 (2023).\u003c/li\u003e\n\u003cli\u003eFerreira, M. C., Meirelles, A. J. A \u0026amp; Batista, E. A. C. Study of the fusel oil distillation process. \u003cem\u003eIndustrial \u0026amp; Engineering Chemistry Research\u003c/em\u003e, \u003cstrong\u003e52\u003c/strong\u003e, 2336\u0026minus;2351. https://doi.org/10.1021/ie300665z (2013).\u003c/li\u003e\n\u003cli\u003eVidal, E. E. et al. A. Influence of nitrogen supply on the production of higher alcohols/esters and expression of flavour-related genes in cachaca fermentation. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cstrong\u003e138(1)\u003c/strong\u003e, 701-708. https://doi.org/10.1016/j.foodchem.2012.10.147 (2013).\u003c/li\u003e\n\u003cli\u003eLuo, H. B., Wang, Y., Wang, C. H., Yang, X. D. \u0026amp; Li, D. Y. Metabolic control of higher alcohols producing by \u003cem\u003eSaccharomyces cerevisiae \u003c/em\u003ein mulberry wine brewing. \u003cem\u003eAdvanced Materials Research\u003c/em\u003e, \u003cstrong\u003e781-784\u003c/strong\u003e, 1861-1865. https://doi.org/10.4028/www.scientific.net/AMR.781-784.1861 (2013).\u003c/li\u003e\n\u003cli\u003eYuan, J., Chen, X., Mishra, P. \u0026amp; Ching, C. B. Metabolically engineered \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e for enhanced isoamyl alcohol production. \u003cem\u003eApplied Microbiology and Biotechnology\u003c/em\u003e, \u003cstrong\u003e101\u003c/strong\u003e, 464-474. https://doi.org/10.1007/s00253-016-7970-1 (2016).\u003c/li\u003e\n\u003cli\u003eTebas, S. O. G. at al. Purifica\u0026ccedil;\u0026atilde;o do \u0026aacute;lcool isoam\u0026iacute;lico a partir do \u0026oacute;leo f\u0026uacute;sel. \u003cem\u003eBlucher Chemical Engineering Proceedings\u003c/em\u003e, \u003cstrong\u003e4(1)\u003c/strong\u003e, 105-107. (2017).\u003c/li\u003e\n\u003cli\u003eTebas, S. O. G., Bara\u0026ntilde;ano, A. G., Pinheiro, P. F. \u0026amp; J\u0026uacute;nior, V. L. Mild conditions in the transesterification reaction of babassu oil and purified fusel oil. \u003cem\u003eChemical Engineering Communications\u003c/em\u003e, \u003cstrong\u003e208(3)\u003c/strong\u003e, 432-440. https://doi.org/10.1080/00986445.2019.1710495 (2020).\u003c/li\u003e\n\u003cli\u003eEust\u0026aacute;cio, R. S. et al. Simula\u0026ccedil;\u0026atilde;o do processo de destila\u0026ccedil;\u0026atilde;o da mistura etanol-\u0026oacute;leo f\u0026uacute;sel utilizando o simulador de processos ProSimPlus. \u003cem\u003eRevista Teccen\u003c/em\u003e\u003cstrong\u003e, 11(1)\u003c/strong\u003e, 61-67 (2018).\u003c/li\u003e\n\u003cli\u003eStribny, J., Gamero, A., Perez-Torrado, R. \u0026amp; Querol, A. \u003cem\u003eSaccharomyces kudriavzevii\u003c/em\u003e and \u003cem\u003eSaccharomyces uvarum\u003c/em\u003e differ from \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e during the production of aroma-active higher alcohols and acetate esters using their amino acidic precursors. International \u003cem\u003eJournal of Food Microbiology\u003c/em\u003e, \u003cstrong\u003e205\u003c/strong\u003e, 41-46. https://doi.org/10.1016/j.ijfoodmicro.2015.04.003 (2015).\u003c/li\u003e\n\u003cli\u003eLi, P. et al. A seamless gene deletion method and its application for regulation of higher alcohols and ester in baijiu \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. \u003cem\u003eBiomed Research International\u003c/em\u003e, \u003cstrong\u003e2019\u003c/strong\u003e. https://doi.org/10.1155/2019/6723849 (2019).\u003c/li\u003e\n\u003cli\u003eZhong, X. et al. Reducing higher alcohols by nitrogen compensation during fermentation of Chinese rice wine. Food \u003cem\u003eScience and Biotechnology,\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 805-816. https://doi.org/10.1007/s10068-019-00718-1 (2019).\u003c/li\u003e\n\u003cli\u003eStroppa, C. T., Alves, J. G. L. F., Figueiredo, A. L. F. D.\u0026amp; Castro, C., C. Par\u0026acirc;metros cin\u0026eacute;ticos de linhagens de levedura isoladas de alambiques mineiros. \u003cem\u003eCi\u0026ecirc;ncia e Agrotecnologia\u003c/em\u003e, \u003cstrong\u003e33\u003c/strong\u003e, 1978-1983. https://doi.org/10.1590/S1413-70542009000700048 (2009).\u003c/li\u003e\n\u003cli\u003eColombi, B. L., Ortiz, M. A, Zanoni, P. R. S., Magalh\u0026atilde;es, W. L. E. \u0026amp; Tavares, L. B. B. Efeito de compostos inibidores na bioconvers\u0026atilde;o de glicose em etanol por levedura \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. \u003cem\u003eEngevista\u003c/em\u003e, \u003cstrong\u003e19(2)\u003c/strong\u003e, 339-352. https://doi.org/10.22409/engevista.v19i2.838 (2017).\u003c/li\u003e\n\u003cli\u003eAlves, J. G. L. F. Estudo da influ\u0026ecirc;ncia da temperatura na cin\u0026eacute;tica de crescimento anaer\u0026oacute;bico de \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. Disserta\u0026ccedil;\u0026atilde;o (Mestrado em Engenharia de Alimentos) \u0026ndash; Universidade Estadual de Campinas, Campinas. 69 f. https://doi.org/10.47749/t/unicamp.1996.108219 (1996).\u003c/li\u003e\n\u003cli\u003eSantos, C. O. Diagn\u0026oacute;stico e avalia\u0026ccedil;\u0026atilde;o da influ\u0026ecirc;ncia de contaminantes selvagens durante etapas do processo produtivo do etanol. Disserta\u0026ccedil;\u0026atilde;o (Mestrado em Engenharia Qu\u0026iacute;mica) - Universidade Federal de Goi\u0026aacute;s, Goi\u0026acirc;nia. http://repositorio.bc.ufg.br/tede/handle/tede/11355 (2021).\u003c/li\u003e\n\u003cli\u003eCabral, G. B. Fermenta\u0026ccedil;\u0026atilde;o alco\u0026oacute;lica de mela\u0026ccedil;o com alta concentra\u0026ccedil;\u0026atilde;o de a\u0026ccedil;\u0026uacute;car: efeito da esteriliza\u0026ccedil;\u0026atilde;o do mosto e tratamento \u0026aacute;cido da levedura. Tese de Doutorado - Universidade de S\u0026atilde;o Paulo. https://doi.org/10.11606/D.11.2020.tde-14082020-081941 (2020).\u003c/li\u003e\n\u003cli\u003eCruz, M. L., De Resende, M. M. \u0026amp; Ribeiro, E. J. Improvement of ethanol production in fed-batch fermentation using a mixture of sugarcane juice and molasse under very high-gravity conditions. \u003cem\u003eBioprocess and Biosystems Engineering\u003c/em\u003e, \u003cstrong\u003e44\u003c/strong\u003e, 617-625. https://doi.org/10.1007/s00449-020-02462-x (2021).\u003c/li\u003e\n\u003cli\u003eBrand\u0026atilde;o, A. C. T., de Resende, M. M. \u0026amp; Ribeiro, E. J. Alcoholic fermentation with high sugar and cell concentration at moderate temperatures using flocculant yeasts. \u003cem\u003eKorean Journal of Chemical Engineering\u003c/em\u003e, \u003cstrong\u003e37\u003c/strong\u003e, 1717-1725. https://doi.org/10.1007/s11814-020-0589-z (2020).\u003c/li\u003e\n\u003cli\u003eCerqueira, D. P. Fermenta\u0026ccedil;\u0026atilde;o alco\u0026oacute;lica de mosto com alta concentra\u0026ccedil;\u0026atilde;o de a\u0026ccedil;\u0026uacute;car. Tese de Doutorado - Universidade de S\u0026atilde;o Paulo. https://doi.org/10.11606/d.11.2013.tde-19122013-085208 (2013).\u003c/li\u003e\n\u003cli\u003ePons, Marie-No\u0026euml;lle \u0026amp; Schutze, S. On-line monitoring of volatile compounds in honey fermentation. \u003cem\u003eJournal of Fermentation and Bioengineering\u003c/em\u003e, \u003cstrong\u003e78(6)\u003c/strong\u003e, 450-454. https://doi.org/10.1016/0922-338X(94)90045-0 (1994).\u003c/li\u003e\n\u003cli\u003eCachot, T., M\u0026uuml;ller, M. \u0026amp; Pons, Marie-N\u0026ouml;elle. Kinetics of volatile metabolites during alcoholic fermentation of cane molasses by \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. \u003cem\u003eApplied Microbiology and Biotechnology,\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 450-454. https://doi.org/10.1007/BF00169748 (1991).\u003c/li\u003e\n\u003cli\u003eSanchez, N., Ruiz, R. Y., Infante, N. \u0026amp; Cobo, M. Bioethanol production from cachaza as hydrogen feedstock: effect of ammonium sulfate during fermentation. \u003cem\u003eEnergies\u003c/em\u003e, \u003cstrong\u003e10(12)\u003c/strong\u003e, 2112. https://doi.org/10.3390/en10122112 (2017).\u003c/li\u003e\n\u003cli\u003eRollero, S. et al. Combined effects of nutrients and temperature on the production of fermentative aromas by \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e during wine fermentation. \u003cem\u003eApplied Microbiology and Biotechnology\u003c/em\u003e, \u003cstrong\u003e99\u003c/strong\u003e, 2291-2304. https://doi.org/10.1007/s00253-014-6210-9 (2015).\u003c/li\u003e\n\u003cli\u003eArshad, M., Khan, Z. M., Khalil‐ur‐Rehman, Shah, F. A. \u0026amp; Rajoka, M. I. Optimization of process variables for minimization of byproduct formation during fermentation of blackstrap molasses to ethanol at industrial scale. \u003cem\u003eLetters in Applied Microbiology\u003c/em\u003e, \u003cstrong\u003e47(5)\u003c/strong\u003e, 410-414. https://doi.org/10.1111/j.1472-765X.2008.02446.x (2008)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fusel oil. Higher alcohols. Fermentation. Ethanol. Molasses. Microdistillery","lastPublishedDoi":"10.21203/rs.3.rs-4397899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4397899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFusel oil, a blend of higher alcohols generated during fermentation, predominantly comprises isoamyl alcohol and isobutanol. Despite their adverse effects on distillation and ethanol quality, these alcohols find widespread use, notably in the fine chemical industry. Fusel oil quality and quantity vary due to multiple factors, including raw materials and fermentation conditions. This study aimed to investigate the effects of pH, refrigeration, and supplementation on isoamyl alcohol and isobutanol formation during molasses must fermentation in a microdistillery. The fermentations were conducted in batches that were fed with 25 \u0026deg;Brix must and 25% v/v commercial dry yeast for 10 hours. A complete 2\u0026sup3; factorial design was used to assess the effects of the studied factors and their interactions on the response variables: fermentation efficiency (n\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e), process efficiency (n\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), ethanol productivity (P), substrate-to-cell conversion factor (Y\u003csub\u003e\u003cem\u003eX/S\u003c/em\u003e\u003c/sub\u003e), isoamyl alcohol produced (A), isobutanol produced (B) and the A/B Ratio between these alcohols. Statistical analysis employed ANOVA and Tukey\u0026rsquo;s test. The results of the substrate-to-cell conversion factor (Y\u003csub\u003eX/S\u003c/sub\u003e) indicated good yeast performance under different fermentation conditions. The interaction effects among the evaluated factors significantly influenced the formation of isoamyl alcohol and isobutanol, as well as the A/B Ratio.\u003c/p\u003e","manuscriptTitle":"Isoamyl alcohol and isobutanol production in sugarcane molasses fermentation in a microdistillery: pH, refrigeration, and supplementation effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 11:48:17","doi":"10.21203/rs.3.rs-4397899/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce1f1f5f-b54a-46d6-b507-705be57a4acb","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32156690,"name":"Physical sciences/Engineering/Chemical engineering"},{"id":32156691,"name":"Biological sciences/Biotechnology/Industrial microbiology"}],"tags":[],"updatedAt":"2024-09-06T04:43:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 11:48:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4397899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4397899","identity":"rs-4397899","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}