Enhancing Saccharomyces cerevisiae alcohol production by Discoloring red sorghum (Sorghum bicolor L. Moench) malt: use of Gompertz 3-parameters Model

Enhancing Saccharomyces cerevisiae alcohol production by Discoloring red sorghum (Sorghum bicolor L. 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Moench) malt: use of Gompertz 3-parameters Model Arthur Kapepa Amisi, Jean-Claude T. Bwanganga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5311747/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 The fermentation of sorghum wort is one of the fundamental steps that determine the quality of this beer. It is influenced by several parameters, among others: temperature, pH, sugar content, amino acid profile, phenolic compound content, and redox potential. Controlling these parameters will therefore make it possible to optimize the production of alcohol and the growth of the yeast during fermentation. Three parameters were considered in this work, namely: The polyphenol content in the grain (Discolored and non-discolored sorghum), temperature (10 and 25°C) and fermentation time (in hours). The results obtained give a better alcohol content of 4.8% for the E1T1 (Discolored sorghum, fermentation at 10°C). The comparison of the means confirms that, and simply means that the extraction of the phenolic compounds before malting as well as the fermentation temperature had a significant effect on the production of alcohol and the growth of the yeast. The results of this work open the door to many other studies to provide to the industrial and artisanal brewers with scientific data that will allow them to better integrate red sorghum malt as raw material into brewing. Sorghum bicolor fermentation beer optimization brewing Saccharomyces cerevisiae Figures Figure 1 Figure 2 Figure 3 Introduction Traditional sorghum beer production processes are long, complex and vary by country or region (Coulibaly et al. 2014 ). However, they are based on the same principles as the ones used for classic beers. Sorghum has plenty of advantages such the absence of gluten, it does not compete with human food, and above all, it is cultived even in semi-arid areas. (Boffill-Rodríguez and Gallardo-Aguilar 2014 ). The three main operations are malting, brewing and fermentation (Chevassus –Agnès et al. 1976; Sawadogo-Lingani et al. 2007 ). The integration of sorghum in modern brewing is therefore faced with the optimization of these different stages, especially since the brewing processes and the machines used have been developed for the use of barley malt (Amisi et al. 2019 , 2020 ; Bwanganga et al. 2018 ). Fermentation is one of the most important phases during beer production as it determines the quality of the final product. It is during this stage that the cooled wort is inoculated with yeast at a rate of 15 to 25.10 6 cells/ml. Yeast strains are selected according to the organoleptic and technological criteria of the beer to be produced (Jeantet et al. 2007 ; Amisi 2010 ). A wort may have a better profile, with the desired physico-chemical properties, but sometimes with a yeast response below the brewer's expectations. Indeed, certain molecules, sometimes present in very small quantities, can strongly influence the metabolism of the yeast and give rise to a beer whose sensory, even hedonic, appreciation is less considerable (Bwanganga et al. 2013a ). Fermentation is accordingly then far from being summed up by the sole action of the yeast to transform the fermentable or reducing sugars coming from the malt wort (with or without a corrected density) into alcohol. It is actually a phase that also determines the aromatic and sensory profile of the future beer (Jeantet et al. 2007 ; Amisi 2010 ). In Western breweries, the fermentation process is started by selected yeast strains ( S. cerevisae or S. carlsbergensis ) and the fermentation time ranges between 8–15 days at 10–16°C (Moll 1991 ; Waites et al. 2001 ). In the case of African traditional sorghum beers, sorghum wort is inoculated with a traditional leaven, and fermentation time varies between 10 and 24 h in ambient temperature. As one of the phases that determines the quality of the final product, several of its factors will therefore influence the final profile of the beer. Among these factors, the most important are: temperature, pH, pressure, redox potential, agitation, good composition (density, soluble nitrogen content measured in FAN, amino acid composition, bitter acids, fatty acids in solution, presence of mineral ions, etc.) and the number, type and physiological state of the yeast seed cells (Bwanganga et al. 2013b ). Differing from barley in its composition with a high content of phenolic compounds and tannins, ground sorghum, malted or not, also differs from conventional barley. Consequently, beer obtained from sorghum hulls and used as a raw grain with exogenous enzymes will contain low levels of ethyl acetate and high levels of 2- and 3-methyl butanol as reported by Bajomo and Young ( 1994 ), and Lyumugabe et al. ( 2012 ). According to the latter, the aromatic profile is highly dependent on the group of amino acids (quantity and type), which itself depends on protease activity during malting and tempering (Bwanganga et al. 2013b ). Materials and Methods Extraction of phenolic compounds The sorghum used in this work is an ecotype of red sorghum sold locally in the city of Goma in North Kivu and intended for traditional brewing. In order to obtain a certain aggregation of the grains, a visual control had been imposed. Bitten grains, insects attacked and any other harvest residues were eliminated from the lot. While waiting to develop physical methods for the elimination of phenolic compounds, inhibitors of amylase activity in sorghum, a mixture of acetone/water 70/30 (V/V) solvents was used as described by Bwanganga et al. ( 2012 ) and Amisi et al. ( 2019 , 2020 , 2021 , 2023 ) with some modifications. To do this, 200g of manually sorted red sorghum grains were soaked in the aforementioned solution and the mixture was left to stand for 20 minutes then centrifuged at 5000 rpm for 5 min. After centrifugation, the sorghum grains were rinsed with distilled water until a clear rinse water was obtained in order to ensure the elimination of almost all of the phenolic compounds (Fig. 1 ). Malting and brewing Discolored and non-discolored red sorghum kernels were steeped separately in distilled water for 48 hours and then germinated for 72 hours in the dark. The green malts obtained were dried in an oven at 40˚C for 48 hours, before being ground and sieved (diameter 1mm). Moreover, one hundred and sixty grams (160 g) of decolorized and non-decolorized sorghum malt flour obtained were separately placed in a 1000 ml thermostatically controlled stainless steel metal container, and brought to the mark with distilled water where the pH was adjusted at 5.5. After pasting, the temperature was brought to 50°C for 10 min then subjected to three temperature stages (63°C for 17.5 min, 72.5°C for 17.5 min and 100°C for 90 min) as described by Amisi et al. ( 2023 ). Wort specific gravity was adjusted to 15° Plato using sugar (approximately 1.059 specific gravity). The boiled wort was then cooled to 10°C and inoculated with 2 ml of a 24-h Saccharomyces ANGEL BF-16 strain culture (Angel Yeast Co., Ltd, China) 0.01 OD at 600 nm, then left to ferment for 96 hours in a fermenter set on the one hand at 10°C (T1), and on the other at 25°C (T2). During the 96 hours of fermentation, the alcohol content of each wort resulting from the different sorghum malt treatments was calculated using the density of the beer following a regular time interval of 12 hours. Density was taken at 20° C. using a Brewferm brand two-scale beer hydrometer type densimeter. The values obtained were converted into degree plato (°P) then calculated into alcohol content according to the following equivalences: Density (°P) = \(\:\frac{\left(d-1\right)\:*\:\text{258,5}}{\text{0,12}\:+\:\text{0,88}.d}\) (1) With d: density Real extract = 0.1808. (° P e ) + 0.8198 (°P b ) (2) With ° P e : highest density in degree plato, °P b : lowest density in degree plato % alcohol (m/v: g/100ml) = \(\:\frac{\left((^\circ\:Pe-^\circ\:Pb\right)*\text{1,05})x100}{^\circ\:Pb}\) (3) % alcohol (v/v: ml/100ml) = \(\:\frac{\%\:alcool\:en\:masse}{\text{0,789}}\) (4) Attenuation = \(\:\frac{^\circ\:Pe-^\circ\:Pb}{^\circ\:Pe}\) x 100 (5) Modeling of alcohol content as a Gompertz growth function of musts and fermentation time (h) The alcohol content during the beer production process can be affected by several factors. In this study, the malting and mashing conditions were set as previously described and for the fermentation conditions, two factors were considered: fermentation time (hour) and grain treatment during malting of sorghum. The alcohol content was modeled as a 3-parameter Gomperz growth as follows: Alcohol (g /100 g) = a 1 ×exp (– exp (a 2 × (Time (h)'˗a 3 ))) (6) where a 1 , a 2 and a 3 are the parameters of the model. Eq. 6 can be rewritten as follows: ln [ln (a 1 × (Alcohol (g / 100 g)) –1 ] = a 3 –a 2 * Time (h) (7) Equation 7 is a straight line whose slope is (a 2 ) and the intersection at the origin is a 3 /a 2 . The parameters of the model were obtained by adjusting the experimental data in accordance with Eq. 2 by considering for each treatment that (a 1 ) is the highest value. The true values of the model parameters were obtained using the Gauss–Newton algorithm and a convergence tolerance of 0.00001 after setting the parameter values as equal to those obtained experimentally using the Minitab17 software. Results The alcohol contents were modeled as a function of the fermentation time for the different treatments and the results are presented in Fig. 2 a-d. The highest alcohol content was obtained at E1T1 (Discolored sorghum, fermentation at 10°C) and the lowest was obtained at E2T2 (non-discolored sorghum, fermentation at 25°C) (see Table 1 and the supplementary table 1 ). The analysis of variance of the different alcohol production treatments was studied and the result presented in Table 2 . Table 1 Initial density (D 0 ), final density (D f ), real extract (RE) (where RE = 0.1808 × D0 (°P) + 0.8198 × D f (°P) and where °P = g of sugar per 100 g of wort), and attenuation and percentage of alcohol of the fermented malted sorghum wort. a Treatments D 0 D f RE Attenuation (%) Alcohol (%: v/v) E1T1 15 ± 0.00 4.99 ± 1.86a 6.80 ± 1.52a 66.75 ± 12.38a 4.61 ± 0.2a E1T2 15 ± 0.06 9.10 ± 0.36b 10.17 ± 0.30b 39.34 ± 2.43b 3.45 ± 0.18b E2T1 15 ± 0.00 7.38 ± 0.28c 8.76 ± 0.23c 50.80 ± 1.87c 4.31 ± 0.14a E2T2 15 ± 0.00 9.95 ± 0.20d 10.87 ± 0.16b 33.69 ± 1.30d 3.02 ± 0.10c aValues are mean ± StDev. Treatments having a letter in common are not statistically different. Grouping information is obtained using the Tukey Method and 95% Confidence. b E1T1 : Discolored sorghum, fermentation at 10°C; E1T2 : Discolored sorghum, fermentation at 25°C; E2T1: non-discolored sorghum, fermentation at 10°C; E2T2 : non-discolored sorghum, fermentation at 25°C; Table 2 Regression analysis of the two-factor model (grain treatment and fermentation temperature) Source DF SS MS F P Regression 1 102,523 102,523 1174.15 0.000 Error 31 2,707 0.087 Total 32 105,230 S = 0.295495 R- Sq = 97.4% R- Sq (adj ) = 97.3% The effect of different treatments and fermentation temperature on alcohol production was studied and the analysis of variance is presented in Table 3 . Table 3 Two-way ANOVA (grain treatment and fermentation temperature) Source DF SS WO MS WO F-Value P value Regression E1T1 E1T2 E2T1 E2T2 4 1 1 1 1 33480.9 37.9 28.4 32.0 178.3 8370.24 37.86 28.36 31.99 178.31 464.37 2.10 1.57 1.77 9.89 0.000 0.158 0.220 0.194 0.004 Error Lack of Fit Pure Error 28 23 5 504.7 450.7 54.0 18.02 19.60 10.80 1.81 0.264 Total 32 33985.6 S = 4.24556 R- sq = 98.51% R - sq (adj) = 98.30% R- sq ( pred ) = 97.58% Where E1T1 : Discolored sorghum, fermentation at 10°C; E1T2 : Discolored sorghum, fermentation at 25°C; E2T1: non-discolored sorghum, fermentation at 10°C; E2T2 : non-discolored sorghum, fermentation at 25°C; Table 4 Effect of fermentation time (in hours) on the alcohol content of E1T1 (Discolored sorghum, fermentation at 10°C): Regression analysis Source DF SS MS F P Error 30 0.553880 0.0184627 Lack of Fit 8 0.230472 0.0288090 1.96 0.101 Pure Error 22 0.323408 0.0147004 The regression analysis of the model shows that the two factors studied (grain treatment and fermentation temperature) as well as their interactions have significant effects at the 5% threshold on alcohol production. The validity of this model is confirmed by the normal and zero-mean distribution of the residuals (Online Supplementary Figs. 1, 2, 3 and 4) The analysis of variance indicated that the E1T1 (discolored sorghum, fermentation at 10°C) treatment of the alcohol level the two factors studied (grain treatment and fermentation temperature) as well as their interactions have significant effects at the 5% threshold on the production of alcohol. The fitted values present a zero mean distribution as can be seen in Fig. 2 (a,b,c,d); which proves the adequacy of the model used for the nonlinear adjustment. The analysis of the inadequacy of the model for E1T1 (discolored sorghum, fermentation at 10°C) gives the p-value higher than 5%, (ie 0.101). This indicates that the regression model used is a very good for the data. This is not only due to the right choice of variables but also because of its good experimental design. A “Lack Fit” with a P-value of less than 5% demonstrates the presence of residuals or unusually large errors, which will require readjustment of the model. E1T1 = Theta1 * exp (- exp (Theta2 - Theta3 * Time)) (8) E1T1 = 5.4385 * exp (- exp (1.84432–0.0399696 * Time)) (9) The P-value obtained (0.081 greater than 0.05 or 5%) in the Table 5 from the analysis of the inadequacy of the model for the E1T2 (discolored sorghum, fermentation at 25°C), demonstrates once again that the model applied corresponds to the data processed. Table 5 Effect of fermentation time (in hours) on the alcohol content of E1T2 (Discolored sorghum, fermentation at 25°C): Regression analysis Source DF SS MS F P Error 30 0.385810 0.0128603 Lack of Fit 8 0.166760 0.0208450 2.09 0.081 Pure Error 22 0.219050 0.0099568 Table 6 Effect of fermentation time (in hours) on the alcohol content of E2T1 (non-discolored sorghum, fermentation at 10°C): Regression analysis Source DF SS MS F P Error 30 0.594809 0.0198270 Lack of Fit 8 0.268560 0.0335700 2.26 0.062 Pure Error 22 0.326249 0.0148295 Table 7 Effect of fermentation time (h) on the alcohol content of E2T2 (non-discolored sorghum, fermentation at 25°C): Regression analysis Source DF SS MS F P Error 30 0.193129 0.0064376 Lack of Fit 8 0.100669 0.0125836 2.99 0.020 Pure Error 22 0.092460 0.0042027 E1T2 = 4.34477 * exp (- exp (1.64008–0.0338638 * Time)) (10) The analysis of the inadequacy of the model for E2T1 (Non-discolored sorghum, fermentation carried out at 10°C) gives the value of P-value greater than 5%, (i.e. 0.062), which confirms once again the reliability of the model used in relation to the analyzed data. E2T1 = 5.48092 * exp (- exp (1.58602–0.0323062 * Time)) (11) The inadequacy of the model, compared to the processed data, shows significant errors at the 5% threshold (P-value = 0.020 less than 0.05). This means that this model is not suitable for this type of data. E2T2 = 3.37613 * exp (- exp (1.65395–0.0424684 * Time)) (12) This nonlinear model shows that the alcohol content increases in all treatments over time. This growth is not fully uniform; it goes through moments of stagnation before resuming growth. However, it is clear from this figure that the E1T1 (Discolored sorghum, fermentation at 10°C) showed strong growth compared to the others (Fig. 3 ). Discussion The fermentation process of sorghum wort is one of the fundamental and crucial steps that determine the quality of its beer. Its evaluation through physical parameters such as pH, temperature and the alcohol produced (ethanol) will make it possible to capitalize values during the experiments. This will allow its full integration into modern and/or artisanal brewing as a substitute for malting barley. The results obtained give a better alcohol level of 4.8, and this, at the E1T1 treatment (discolored sorghum, fermentation at 10°C). The comparison of the means further demonstrate that the best treatment is E1T1 (Discolored sorghum, fermentation at 10°C) and that it is not significantly different from E2T1 (non-discolored sorghum, fermentation at 10°C), but is significantly different from E1T2 (Discolored sorghum, fermentation at 25°C) and E2T2 (non-discolored sorghum, fermentation at 25°C) at the 5% threshold. This is confirmed by the model’s mismatch values (Lack of Fit) obtained. Indeed, the E1T1 gave a P-value 0.101 higher than that of all the other treatments. This means that there are no significant model errors with respect to the analyzed data. The high alcohol content in the E1T1 shows the high speed of action of the yeast in the presence of simple sugars after the hydrolysis of the starch by the amylases. This speed is to be correlated with the chemical composition of malt (Bwanganga 2012). Indeed, the synthesis of amylase enzymes during sorghum malting is one of the concerns of sorghum maltsters (Amisi et al. 2020 ), because the latter would contain up to 6% of phenolic compounds (phenolic acids, flavonoids and tannins) (Beta et al. 1999 ; Awika and Rooney 2004 ; Dicko et al. 2006 ). Omnipresent and the most represented in all plants, phenolic compounds are therefore secondary metabolites of considerable interest in agronomy (Dicko et al. 2006 ). They would therefore be the cause of the weak response in the induction of the synthesis of sorghum amylases during malting (Bwanganga et al. 2013a , b ). It has certainly already been demonstrated that malting has a positive effect on the reduction of the synthesis of total polyphenols (Bwanganga 2012), but does not eliminate them all. The use of an acetone-distilled water (70/30) solvent system – volume to volume percentage – before malting eliminated a significant amount of the phenolic compounds from the sorghum grains and thus increased the induction of synthesis of amylases (Amisi et al. 2021 ; Ba et al. 2010 ). By really understanding its role in alcoholic fermentation thanks to Louis Pasteur in 1857 (Bwanganga et al. 2015 ), the yeast Saccharomyces cerevisiae is essential for the production of beer. It allows the transformation of sugars into alcohol and plays an essential role in the development of the aromatic properties of beer, often constituting the most secret ingredient of brewers (Rodriguez-Perez et al. 2015 ; Nguyen 2016 ). According to Breeuwer & Abee ( 2000 ), it can grow between 0° to 55°C with a growth pH between 2.8 and 8. However, its optimum growth temperature is around 8°C. The results of this study indicated that alcohol content lagged at the start of fermentation, increasing quickly to reach a maximum value at approximately 96h as obtained by Amisi et al. ( 2023 ). The highest alcohol content was obtained at E1T1 (discolored sorghum, fermentation at 10°C). This has been justified by the fact that the fermentation had been carried out around 10°C, a temperature close to the optimum for the type of yeast used (12°C). The comparison of the means showed that there is no significant difference at the 5% threshold between E1T1 (Discolored sorghum, fermentation at 10°C) and E2T1 (non-discolored sorghum, fermentation at 10°C). This is confirmed by the analysis of the main effects that the extraction of phenolic compounds from sorghum grains before malting as well as the fermentation temperature had effects on the final alcohol content. Even though the extraction of the phenolic compounds before malting as well as the fermentation temperature according to the variety used, would influence the alcohol content of the drink, the brewing temperature as well as the pH of the wort would also play a key role in affecting the alcohol content of the beer. Even though the extraction of the phenolic compounds before malting as well as the fermentation temperature according to the variety used, would influence the alcohol content of the drink, the brewing temperature as well as the pH of the wort would also play a key role in affecting the alcohol content of the beer. Amisi’s work et al. (sp) confirm this by determining the optimal mixing conditions. These are 63° C for 17.5 minutes, for β-amylase and 72.5° C for 17.5 minutes, for α-amylase; the best pH for both is 5. However, these results are unlike Amisi 's results et al. (2021), where the best temperature for the work of α-amylase is at 75°C, much higher than that of 70°C obtained by Egwim and Oloyele (2006). It must be noted that the temperature of 70° C. obtained by these authors was obtained during a test in which the pH, not indicated, was kept constant. In conclusion, the context of the global economic crisis is pushing developing countries like the DR Congo to advance the development of their local resources in order to limit imports of raw materials. This involves integrating these local resources into the product manufacturing process. The integration of sorghum in modern and artisanal brewing as a substitute for malting barley is therefore one of the challenges for the development of the country. This substitution can be difficult given the low enzymatic activity due to the high content of phenolic compounds and tannins, and requires optimization of the brewing (especially boiling) and fermentation parameters. In this study, the alcohol content was monitored during fermentation of sorghum malt wort using a strain of Saccharomyces. Phenolic compound content also affected yeast growth. This preliminary study indicates that the focus for further studies should be on how to optimize fermentation conditions to achieve high quality malted sorghum beer and how Phenolic compound content can affect quality of beer during boiling, fermentation and aging. Declarations Ethics approval and Consent to participate Authors of this research did not involve human or animal subjects. So, no ethical approval is required. Authors also declare that this study has not required a consent to participate because it does not involve human subjects, Consent to publish Authors declare that the manuscript does not contain any individual person’s data in any form (including any individual details, images or videos). No consent to publish is required Availability of data and materials The authors declare that additional data in this manuscript are available at the complementary information. They will also be available on request from the first author. Competing Interests The authors have no relevant financial or non-financial interests to disclose.” They also declare that they have no conflict of interest. Funding The authors declare no funds, grants, or other support were received during the preparation of this manuscript. Authors’ contributions Arthur Kapepa Amisi contributed to conception and design, and/or acquisition of data, and/or Analysis and interpretation of data, and in drafting the article. He also contributed in reviewing critically for significant intellectual content. Jean-Claude Tawaba Bwanganga has supervised the study and gave the final approval of the version to be submitted and any revised version ORCID Arthur Amisi Kapepa http://orcid.org/0000-0001-5796-3156 References Amisi AK, Bimpi LD, Kibi KS, Benge AR, Bwanganga TJC (2023) Study of a Sorghum Malt Wort Supplemented with Vernonia amygdalina Extract, a Substitute Hop Compound for Bitterness. J Americ Soc Brew Chem 81(4):508–513. 10.1080/03610470.2022.2150994 Amisi AK, Kasonga TK, Mbwanganga KB, Bwanganga JCT (eds) (sp) Optimization of red sorghum ( Sorghum bicolor (L.) Moench) malt mashing using Response Surface Methodology. A Journ Appl Sc. in press Amisi AK, Mava D, Kasonga TR, Makaba MER, Bwanganga JCT (2020) Effects of gibberellic acid on the synthesis of alpha and beta amylase during sorghum malting ( Sorghum bicolor L. Moench). 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Edition Technique et Documentation Lavoisier, Paris, p 515 Nguyen TD (2016) Protection of Saccharomyces cerevisiae yeast by a multilayered biopolymeric system: effect on its metabolic activity in response to environmental conditions. Microbiology and parasitology. University of Burgundy, Edition, pp 120–169 Rodriguez-Perez C, Quirantes R, Fernandez-Gutiérrez A, Carretero AS (2015) Optimization of extraction method to obtain a phenolic compounds-rich extract from Moringa oleifera Lam leaves. Ind Crops Prod 66246–254. 10.1016 Sawadogo-Lingani H, Lei V, Diawara B, Nielsen DS, Moller P, Traoré AS, Jakobsen M (2007) The biodiversity of predominant lactic acid bacteria in dolo and pito wort for the production of sorghum beer. J Appl Microbiol 103:765–777 Waites MJ, Morgan NL, Rockey JS, Higton G (2001) Industrial microbiology: an introduction. Blackwell Science, London, p 287 Supplementary Files Supplementarymaterials.pdf 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-5311747","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369681435,"identity":"6a157ccf-dbd9-401f-9f17-d40357f4a360","order_by":0,"name":"Arthur Kapepa Amisi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHACNgjFzMBw4AMDA48EM0ziAC4dzAgtB2eQpgXE5AESEnAuDi38s88fe8y7h0Fet5394WHbtjsyku28Bxh+1DDI8+HQInEumd2Y5xmD4bbDPAaHc9ue8Ugz8yUw9hxjMJyJy2FnmNmkeQ4wMAK1MAC1HOaRY+YxYOBtYEgwwKFFHqrFftth9geHLaFaGP/i0WIA1ZK47TCDwWFGoBZpoBZmfLYYnmE2k5xzQCIZ5JeDPecO80g2Az0lc0wCp1/kzjA+k3hzwMZ22/njjz/8KDtsL3H+jOHDNzU2OEMMCiRQuQcwREbBKBgFo2AUkAQAXtpTQlCE1BkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5796-3156","institution":"University of Kinshasa, Faculty of Agriculture Sciences and Environment","correspondingAuthor":true,"prefix":"","firstName":"Arthur","middleName":"Kapepa","lastName":"Amisi","suffix":""},{"id":369681436,"identity":"520539f2-7146-472e-9246-c8763c806669","order_by":1,"name":"Jean-Claude T. Bwanganga","email":"","orcid":"","institution":"University of Kinshasa, Faculty of Agriculture Sciences and Environment","correspondingAuthor":false,"prefix":"","firstName":"Jean-Claude","middleName":"T.","lastName":"Bwanganga","suffix":""}],"badges":[],"createdAt":"2024-10-22 12:27:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5311747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5311747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67436104,"identity":"36f94646-a5da-4fa7-8406-3f20e809e8b0","added_by":"auto","created_at":"2024-10-25 04:07:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71886,"visible":true,"origin":"","legend":"\u003cp\u003eExtraction of phenolic compounds\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5311747/v1/19c1f872de3ad4fede9824e6.png"},{"id":67436102,"identity":"e10def11-144c-4fb4-b91c-24fd6cda89cf","added_by":"auto","created_at":"2024-10-25 04:07:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63031,"visible":true,"origin":"","legend":"\u003cp\u003eFitted line: alcohol (v/v: mL/mL) versus fermentation time (h) for (a): E1T1 (discolored sorghum, fermentation at 10°C), (b): E1T2 (discolored sorghum, fermentation at 25°C), (c): E2T1 (non-discolored sorghum, fermentation at 10°C) and (d): E2T2 (non-discolored sorghum, fermentation at 25°C)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5311747/v1/023c3941e5656a2fb5bc429e.png"},{"id":67436103,"identity":"2e430c0f-e521-4107-ade7-03eb8ad50736","added_by":"auto","created_at":"2024-10-25 04:07:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9216,"visible":true,"origin":"","legend":"\u003cp\u003eAlcohol content (%V/V) (values are mean ± std) versus fermentation time (h) for E1T1, E1T2, E2T1 and E2T2 of malted sorghum wort.\u003c/p\u003e\n\u003cp\u003eWhere E1T1: discolored sorghum, fermentation at 10°C; E1T2: discolored sorghum, fermentation at 25°C; E2T1: non-discolored sorghum, fermentation at 10°C; E2T2: non-discolored sorghum, fermentation at 25°C\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5311747/v1/e660a7e7a99dabbc67a363e6.png"},{"id":73470641,"identity":"4e59bb89-0e51-4e79-a5b3-2adb741c72c8","added_by":"auto","created_at":"2025-01-10 09:27:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5311747/v1/d246dbd6-78db-46fc-aef3-ad413aeff4c1.pdf"},{"id":67436101,"identity":"445af2a8-f11e-40bd-b62a-a1f711a116ad","added_by":"auto","created_at":"2024-10-25 04:07:17","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":530249,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5311747/v1/589c6ca461b03b0ce9f01248.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEnhancing \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e alcohol production by Discoloring red sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench) malt: use of Gompertz 3-parameters Model\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraditional sorghum beer production processes are long, complex and vary by country or region (Coulibaly et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, they are based on the same principles as the ones used for classic beers. Sorghum has plenty of advantages such the absence of gluten, it does not compete with human food, and above all, it is cultived even in semi-arid areas. (Boffill-Rodr\u0026iacute;guez and Gallardo-Aguilar \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The three main operations are malting, brewing and fermentation (Chevassus \u0026ndash;Agn\u0026egrave;s et al. 1976; Sawadogo-Lingani et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of sorghum in modern brewing is therefore faced with the optimization of these different stages, especially since the brewing processes and the machines used have been developed for the use of barley malt (Amisi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bwanganga et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFermentation is one of the most important phases during beer production as it determines the quality of the final product. It is during this stage that the cooled wort is inoculated with yeast at a rate of 15 to 25.10 6 cells/ml. Yeast strains are selected according to the organoleptic and technological criteria of the beer to be produced (Jeantet et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Amisi \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA wort may have a better profile, with the desired physico-chemical properties, but sometimes with a yeast response below the brewer's expectations. Indeed, certain molecules, sometimes present in very small quantities, can strongly influence the metabolism of the yeast and give rise to a beer whose sensory, even hedonic, appreciation is less considerable (Bwanganga et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFermentation is accordingly then far from being summed up by the sole action of the yeast to transform the fermentable or reducing sugars coming from the malt wort (with or without a corrected density) into alcohol. It is actually a phase that also determines the aromatic and sensory profile of the future beer (Jeantet et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Amisi \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Western breweries, the fermentation process is started by selected yeast strains (\u003cem\u003eS. cerevisae\u003c/em\u003e or \u003cem\u003eS. carlsbergensis\u003c/em\u003e) and the fermentation time ranges between 8\u0026ndash;15 days at 10\u0026ndash;16\u0026deg;C (Moll \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Waites et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In the case of African traditional sorghum beers, sorghum wort is inoculated with a traditional leaven, and fermentation time varies between 10 and 24 h in ambient temperature.\u003c/p\u003e \u003cp\u003eAs one of the phases that determines the quality of the final product, several of its factors will therefore influence the final profile of the beer. Among these factors, the most important are: temperature, pH, pressure, redox potential, agitation, good composition (density, soluble nitrogen content measured in FAN, amino acid composition, bitter acids, fatty acids in solution, presence of mineral ions, etc.) and the number, type and physiological state of the yeast seed cells (Bwanganga et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiffering from barley in its composition with a high content of phenolic compounds and tannins, ground sorghum, malted or not, also differs from conventional barley. Consequently, beer obtained from sorghum hulls and used as a raw grain with exogenous enzymes will contain low levels of ethyl acetate and high levels of 2- and 3-methyl butanol as reported by Bajomo and Young (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), and Lyumugabe et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). According to the latter, the aromatic profile is highly dependent on the group of amino acids (quantity and type), which itself depends on protease activity during malting and tempering (Bwanganga et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExtraction of phenolic compounds\u003c/h2\u003e \u003cp\u003eThe sorghum used in this work is an ecotype of red sorghum sold locally in the city of Goma in North Kivu and intended for traditional brewing. In order to obtain a certain aggregation of the grains, a visual control had been imposed. Bitten grains, insects attacked and any other harvest residues were eliminated from the lot.\u003c/p\u003e \u003cp\u003eWhile waiting to develop physical methods for the elimination of phenolic compounds, inhibitors of amylase activity in sorghum, a mixture of acetone/water 70/30 (V/V) solvents was used as described by Bwanganga et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Amisi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) with some modifications. To do this, 200g of manually sorted red sorghum grains were soaked in the aforementioned solution and the mixture was left to stand for 20 minutes then centrifuged at 5000 rpm for 5 min. After centrifugation, the sorghum grains were rinsed with distilled water until a clear rinse water was obtained in order to ensure the elimination of almost all of the phenolic compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMalting and brewing\u003c/h3\u003e\n\u003cp\u003eDiscolored and non-discolored red sorghum kernels were steeped separately in distilled water for 48 hours and then germinated for 72 hours in the dark. The green malts obtained were dried in an oven at 40˚C for 48 hours, before being ground and sieved (diameter 1mm).\u003c/p\u003e \u003cp\u003eMoreover, one hundred and sixty grams (160 g) of decolorized and non-decolorized sorghum malt flour obtained were separately placed in a 1000 ml thermostatically controlled stainless steel metal container, and brought to the mark with distilled water where the pH was adjusted at 5.5. After pasting, the temperature was brought to 50\u0026deg;C for 10 min then subjected to three temperature stages (63\u0026deg;C for 17.5 min, 72.5\u0026deg;C for 17.5 min and 100\u0026deg;C for 90 min) as described by Amisi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Wort specific gravity was adjusted to 15\u0026deg; Plato using sugar (approximately 1.059 specific gravity).\u003c/p\u003e \u003cp\u003eThe boiled wort was then cooled to 10\u0026deg;C and inoculated with 2 ml of a 24-h Saccharomyces ANGEL BF-16 strain culture (Angel Yeast Co., Ltd, China) 0.01 OD at 600 nm, then left to ferment for 96 hours in a fermenter set on the one hand at 10\u0026deg;C (T1), and on the other at 25\u0026deg;C (T2).\u003c/p\u003e \u003cp\u003eDuring the 96 hours of fermentation, the alcohol content of each wort resulting from the different sorghum malt treatments was calculated using the density of the beer following a regular time interval of 12 hours. Density was taken at 20\u0026deg; C. using a Brewferm brand two-scale beer hydrometer type densimeter. The values obtained were converted into degree plato (\u0026deg;P) then calculated into alcohol content according to the following equivalences:\u003c/p\u003e \u003cp\u003eDensity (\u0026deg;P) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\left(d-1\\right)\\:*\\:\\text{258,5}}{\\text{0,12}\\:+\\:\\text{0,88}.d}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWith d: density\u003c/p\u003e \u003cp\u003eReal extract\u0026thinsp;=\u0026thinsp;0.1808. (\u0026deg; P\u003csub\u003ee\u003c/sub\u003e)\u0026thinsp;+\u0026thinsp;0.8198 (\u0026deg;P\u003csub\u003eb\u003c/sub\u003e) (2)\u003c/p\u003e \u003cp\u003eWith \u0026deg; P\u003csub\u003ee\u003c/sub\u003e : highest density in degree plato, \u0026deg;P\u003csub\u003eb\u003c/sub\u003e : lowest density in degree plato\u003c/p\u003e \u003cp\u003e% alcohol (m/v: g/100ml) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\left((^\\circ\\:Pe-^\\circ\\:Pb\\right)*\\text{1,05})x100}{^\\circ\\:Pb}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003e% alcohol (v/v: ml/100ml) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\%\\:alcool\\:en\\:masse}{\\text{0,789}}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003eAttenuation =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{^\\circ\\:Pe-^\\circ\\:Pb}{^\\circ\\:Pe}\\)\u003c/span\u003e\u003c/span\u003e x 100 (5)\u003c/p\u003e\n\u003ch3\u003eModeling of alcohol content as a Gompertz growth function of musts and fermentation time (h)\u003c/h3\u003e\n\u003cp\u003eThe alcohol content during the beer production process can be affected by several factors. In this study, the malting and mashing conditions were set as previously described and for the fermentation conditions, two factors were considered: fermentation time (hour) and grain treatment during malting of sorghum.\u003c/p\u003e \u003cp\u003eThe alcohol content was modeled as a 3-parameter Gomperz growth as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlcohol (g /100 g)\u0026thinsp;=\u0026thinsp;a\u003c/b\u003e \u003csub\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e\u0026times;exp (\u0026ndash; exp (a\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e\u0026times; (Time (h)'˗a\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)))\u003c/b\u003e (6)\u003c/p\u003e \u003cp\u003ewhere a\u003csub\u003e1\u003c/sub\u003e, a\u003csub\u003e2\u003c/sub\u003e and a \u003csub\u003e3\u003c/sub\u003e are the parameters of the model. Eq.\u0026nbsp;6 can be rewritten as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eln [ln (a\u003c/b\u003e \u003csub\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e\u0026times; (Alcohol (g / 100 g))\u003c/b\u003e \u003csup\u003e\u003cb\u003e\u0026ndash;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e]\u0026thinsp;=\u0026thinsp;a\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e\u0026ndash;a\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e* Time (h)\u003c/b\u003e (7)\u003c/p\u003e \u003cp\u003eEquation 7 is a straight line whose slope is (a\u003csub\u003e2\u003c/sub\u003e) and the intersection at the origin is a\u003csub\u003e3\u003c/sub\u003e /a\u003csub\u003e2\u003c/sub\u003e. The parameters of the model were obtained by adjusting the experimental data in accordance with Eq.\u0026nbsp;2 by considering for each treatment that (a\u003csub\u003e1\u003c/sub\u003e) is the highest value. The true values of the model parameters were obtained using the Gauss\u0026ndash;Newton algorithm and a convergence tolerance of 0.00001 after setting the parameter values as equal to those obtained experimentally using the Minitab17 software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe alcohol contents were modeled as a function of the fermentation time for the different treatments and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d. The highest alcohol content was obtained at E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C) and the lowest was obtained at E2T2 (non-discolored sorghum, fermentation at 25\u0026deg;C) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The analysis of variance of the different alcohol production treatments was studied and the result presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eInitial density (D\u003csub\u003e0\u003c/sub\u003e), final density (D\u003csub\u003ef\u003c/sub\u003e), real extract (RE) (where RE\u0026thinsp;=\u0026thinsp;0.1808 \u0026times; D0 (\u0026deg;P)\u0026thinsp;+\u0026thinsp;0.8198 \u0026times; D\u003csub\u003ef\u003c/sub\u003e (\u0026deg;P) and where \u0026deg;P\u0026thinsp;=\u0026thinsp;g of sugar per 100 g of wort), and attenuation and percentage of alcohol of the fermented malted sorghum wort.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003csub\u003ef\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttenuation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlcohol (%: v/v)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.38a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.61 \u0026plusmn; 0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eaValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;StDev. Treatments having a letter in common are not statistically different. Grouping information is obtained using the Tukey Method and 95% Confidence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eb E1T1\u003c/b\u003e: Discolored sorghum, fermentation at 10\u0026deg;C; \u003cb\u003eE1T2\u003c/b\u003e: Discolored sorghum, fermentation at 25\u0026deg;C; E2T1: non-discolored sorghum, fermentation at 10\u0026deg;C; \u003cb\u003eE2T2\u003c/b\u003e: non-discolored sorghum, fermentation at 25\u0026deg;C;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis of the two-factor model (grain treatment and fermentation temperature)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1174.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eS\u0026thinsp;=\u0026thinsp;0.295495 R- Sq\u0026thinsp;=\u0026thinsp;97.4% R- Sq (adj )\u0026thinsp;=\u0026thinsp;97.3%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe effect of different treatments and fermentation temperature on alcohol production was studied and the analysis of variance is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-way ANOVA (grain treatment and fermentation temperature)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS WO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS WO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003cp\u003eE1T1\u003c/p\u003e \u003cp\u003eE1T2\u003c/p\u003e \u003cp\u003eE2T1\u003c/p\u003e \u003cp\u003eE2T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33480.9\u003c/p\u003e \u003cp\u003e37.9\u003c/p\u003e \u003cp\u003e28.4\u003c/p\u003e \u003cp\u003e32.0\u003c/p\u003e \u003cp\u003e178.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8370.24\u003c/p\u003e \u003cp\u003e37.86\u003c/p\u003e \u003cp\u003e28.36\u003c/p\u003e \u003cp\u003e31.99\u003c/p\u003e \u003cp\u003e178.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e464.37\u003c/p\u003e \u003cp\u003e2.10\u003c/p\u003e \u003cp\u003e1.57\u003c/p\u003e \u003cp\u003e1.77\u003c/p\u003e \u003cp\u003e9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.158\u003c/p\u003e \u003cp\u003e0.220\u003c/p\u003e \u003cp\u003e0.194\u003c/p\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504.7\u003c/p\u003e \u003cp\u003e450.7\u003c/p\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.02\u003c/p\u003e \u003cp\u003e19.60\u003c/p\u003e \u003cp\u003e10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33985.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eS\u0026thinsp;=\u0026thinsp;4.24556 R- sq\u0026thinsp;=\u0026thinsp;98.51% R - sq (adj)\u0026thinsp;=\u0026thinsp;98.30% R- sq ( pred )\u0026thinsp;=\u0026thinsp;97.58%\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eWhere \u003cb\u003eE1T1\u003c/b\u003e: Discolored sorghum, fermentation at 10\u0026deg;C; \u003cb\u003eE1T2\u003c/b\u003e: Discolored sorghum, fermentation at 25\u0026deg;C; E2T1: non-discolored sorghum, fermentation at 10\u0026deg;C; \u003cb\u003eE2T2\u003c/b\u003e: non-discolored sorghum, fermentation at 25\u0026deg;C;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of fermentation time (in hours) on the alcohol content of E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C): Regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.553880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0184627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0288090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.323408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0147004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression analysis of the model shows that the two factors studied (grain treatment and fermentation temperature) as well as their interactions have significant effects at the 5% threshold on alcohol production. The validity of this model is confirmed by the normal and zero-mean distribution of the residuals (Online Supplementary Figs.\u0026nbsp;1, 2, 3 and 4)\u003c/p\u003e \u003cp\u003eThe analysis of variance indicated that the E1T1 (discolored sorghum, fermentation at 10\u0026deg;C) treatment of the alcohol level the two factors studied (grain treatment and fermentation temperature) as well as their interactions have significant effects at the 5% threshold on the production of alcohol.\u003c/p\u003e \u003cp\u003eThe fitted values present a zero mean distribution as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a,b,c,d); which proves the adequacy of the model used for the nonlinear adjustment.\u003c/p\u003e \u003cp\u003eThe analysis of the inadequacy of the model for E1T1 (discolored sorghum, fermentation at 10\u0026deg;C) gives the p-value higher than 5%, (ie 0.101). This indicates that the regression model used is a very good for the data. This is not only due to the right choice of variables but also because of its good experimental design. A \u0026ldquo;Lack Fit\u0026rdquo; with a P-value of less than 5% demonstrates the presence of residuals or unusually large errors, which will require readjustment of the model.\u003c/p\u003e \u003cp\u003eE1T1\u0026thinsp;=\u0026thinsp;Theta1 * exp (- exp (Theta2 - Theta3 * Time)) (8)\u003c/p\u003e \u003cp\u003eE1T1\u0026thinsp;=\u0026thinsp;5.4385 * exp (- exp (1.84432\u0026ndash;0.0399696 * Time)) (9)\u003c/p\u003e \u003cp\u003eThe P-value obtained (0.081 greater than 0.05 or 5%) in the Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e from the analysis of the inadequacy of the model for the E1T2 (discolored sorghum, fermentation at 25\u0026deg;C), demonstrates once again that the model applied corresponds to the data processed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of fermentation time (in hours) on the alcohol content of E1T2 (Discolored sorghum, fermentation at 25\u0026deg;C): Regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.385810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0128603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0208450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.219050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0099568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of fermentation time (in hours) on the alcohol content of E2T1 (non-discolored sorghum, fermentation at 10\u0026deg;C): Regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.594809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0198270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.268560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0335700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0148295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of fermentation time (h) on the alcohol content of E2T2 (non-discolored sorghum, fermentation at 25\u0026deg;C): Regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.193129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0064376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0125836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.092460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0042027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eE1T2\u0026thinsp;=\u0026thinsp;4.34477 * exp (- exp (1.64008\u0026ndash;0.0338638 * Time)) (10)\u003c/p\u003e \u003cp\u003eThe analysis of the inadequacy of the model for E2T1 (Non-discolored sorghum, fermentation carried out at 10\u0026deg;C) gives the value of P-value greater than 5%, (i.e. 0.062), which confirms once again the reliability of the model used in relation to the analyzed data.\u003c/p\u003e \u003cp\u003eE2T1\u0026thinsp;=\u0026thinsp;5.48092 * exp (- exp (1.58602\u0026ndash;0.0323062 * Time)) (11)\u003c/p\u003e \u003cp\u003eThe inadequacy of the model, compared to the processed data, shows significant errors at the 5% threshold (P-value\u0026thinsp;=\u0026thinsp;0.020 less than 0.05). This means that this model is not suitable for this type of data.\u003c/p\u003e \u003cp\u003eE2T2\u0026thinsp;=\u0026thinsp;3.37613 * exp (- exp (1.65395\u0026ndash;0.0424684 * Time)) (12)\u003c/p\u003e \u003cp\u003eThis nonlinear model shows that the alcohol content increases in all treatments over time. This growth is not fully uniform; it goes through moments of stagnation before resuming growth. However, it is clear from this figure that the E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C) showed strong growth compared to the others (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe fermentation process of sorghum wort is one of the fundamental and crucial steps that determine the quality of its beer. Its evaluation through physical parameters such as pH, temperature and the alcohol produced (ethanol) will make it possible to capitalize values during the experiments. This will allow its full integration into modern and/or artisanal brewing as a substitute for malting barley.\u003c/p\u003e \u003cp\u003eThe results obtained give a better alcohol level of 4.8, and this, at the E1T1 treatment (discolored sorghum, fermentation at 10\u0026deg;C). The comparison of the means further demonstrate that the best treatment is E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C) and that it is not significantly different from E2T1 (non-discolored sorghum, fermentation at 10\u0026deg;C), but is significantly different from E1T2 (Discolored sorghum, fermentation at 25\u0026deg;C) and E2T2 (non-discolored sorghum, fermentation at 25\u0026deg;C) at the 5% threshold. This is confirmed by the model\u0026rsquo;s mismatch values (Lack of Fit) obtained. Indeed, the E1T1 gave a P-value 0.101 higher than that of all the other treatments. This means that there are no significant model errors with respect to the analyzed data. The high alcohol content in the E1T1 shows the high speed of action of the yeast in the presence of simple sugars after the hydrolysis of the starch by the amylases. This speed is to be correlated with the chemical composition of malt (Bwanganga 2012).\u003c/p\u003e \u003cp\u003eIndeed, the synthesis of amylase enzymes during sorghum malting is one of the concerns of sorghum maltsters (Amisi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), because the latter would contain up to 6% of phenolic compounds (phenolic acids, flavonoids and tannins) (Beta et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Awika and Rooney \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dicko et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Omnipresent and the most represented in all plants, phenolic compounds are therefore secondary metabolites of considerable interest in agronomy (Dicko et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). They would therefore be the cause of the weak response in the induction of the synthesis of sorghum amylases during malting (Bwanganga et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has certainly already been demonstrated that malting has a positive effect on the reduction of the synthesis of total polyphenols (Bwanganga 2012), but does not eliminate them all. The use of an acetone-distilled water (70/30) solvent system \u0026ndash; volume to volume percentage \u0026ndash; before malting eliminated a significant amount of the phenolic compounds from the sorghum grains and thus increased the induction of synthesis of amylases (Amisi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ba et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy really understanding its role in alcoholic fermentation thanks to Louis Pasteur in 1857 (Bwanganga et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the yeast Saccharomyces cerevisiae is essential for the production of beer. It allows the transformation of sugars into alcohol and plays an essential role in the development of the aromatic properties of beer, often constituting the most secret ingredient of brewers (Rodriguez-Perez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nguyen \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Breeuwer \u0026amp; Abee (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), it can grow between 0\u0026deg; to 55\u0026deg;C with a growth pH between 2.8 and 8. However, its optimum growth temperature is around 8\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe results of this study indicated that alcohol content lagged at the start of fermentation, increasing quickly to reach a maximum value at approximately 96h as obtained by Amisi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The highest alcohol content was obtained at E1T1 (discolored sorghum, fermentation at 10\u0026deg;C). This has been justified by the fact that the fermentation had been carried out around 10\u0026deg;C, a temperature close to the optimum for the type of yeast used (12\u0026deg;C). The comparison of the means showed that there is no significant difference at the 5% threshold between E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C) and E2T1 (non-discolored sorghum, fermentation at 10\u0026deg;C). This is confirmed by the analysis of the main effects that the extraction of phenolic compounds from sorghum grains before malting as well as the fermentation temperature had effects on the final alcohol content.\u003c/p\u003e \u003cp\u003eEven though the extraction of the phenolic compounds before malting as well as the fermentation temperature according to the variety used, would influence the alcohol content of the drink, the brewing temperature as well as the pH of the wort would also play a key role in affecting the alcohol content of the beer.\u003c/p\u003e \u003cp\u003eEven though the extraction of the phenolic compounds before malting as well as the fermentation temperature according to the variety used, would influence the alcohol content of the drink, the brewing temperature as well as the pH of the wort would also play a key role in affecting the alcohol content of the beer.\u003c/p\u003e \u003cp\u003eAmisi\u0026rsquo;s work et al. (sp) confirm this by determining the optimal mixing conditions. These are 63\u0026deg; C for 17.5 minutes, for β-amylase and 72.5\u0026deg; C for 17.5 minutes, for α-amylase; the best pH for both is 5. However, these results are unlike Amisi 's results et al. (2021), where the best temperature for the work of α-amylase is at 75\u0026deg;C, much higher than that of 70\u0026deg;C obtained by Egwim and Oloyele (2006). It must be noted that the temperature of 70\u0026deg; C. obtained by these authors was obtained during a test in which the pH, not indicated, was kept constant.\u003c/p\u003e \u003cp\u003eIn conclusion, the context of the global economic crisis is pushing developing countries like the DR Congo to advance the development of their local resources in order to limit imports of raw materials. This involves integrating these local resources into the product manufacturing process.\u003c/p\u003e \u003cp\u003eThe integration of sorghum in modern and artisanal brewing as a substitute for malting barley is therefore one of the challenges for the development of the country. This substitution can be difficult given the low enzymatic activity due to the high content of phenolic compounds and tannins, and requires optimization of the brewing (especially boiling) and fermentation parameters. In this study, the alcohol content was monitored during fermentation of sorghum malt wort using a strain of Saccharomyces. Phenolic compound content also affected yeast growth. This preliminary study indicates that the focus for further studies should be on how to optimize fermentation conditions to achieve high quality malted sorghum beer and how Phenolic compound content can affect quality of beer during boiling, fermentation and aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and\u003c/strong\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors of this research did not involve human or animal subjects. So, no ethical approval is required.\u003c/p\u003e\n\u003cp\u003eAuthors also declare that this study has not required a consent to participate because it does not involve human subjects,\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConsent to publish\u003c/h3\u003e\n\u003cp\u003eAuthors declare that the manuscript does not contain any individual person\u0026rsquo;s data in any form (including any individual details, images or videos). No consent to publish is required\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe authors declare that additional data in this manuscript are available at the complementary information. They will also be available on request from the first author.\u003c/p\u003e\n\u003ch3\u003eCompeting Interests\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThey also declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eArthur Kapepa Amisi contributed to conception and design, and/or acquisition of data, and/or Analysis and interpretation of data, and in drafting the article. He also contributed in reviewing critically for significant intellectual content.\u003c/p\u003e\n\u003cp\u003eJean-Claude Tawaba Bwanganga has\u0026nbsp;supervised the study and gave the final approval of the version to be submitted and any revised version\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArthur Amisi Kapepa http://orcid.org/0000-0001-5796-3156\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmisi AK, Bimpi LD, Kibi KS, Benge AR, Bwanganga TJC (2023) Study of a Sorghum Malt Wort Supplemented with \u003cem\u003eVernonia amygdalina\u003c/em\u003e Extract, a Substitute Hop Compound for Bitterness. J Americ Soc Brew Chem 81(4):508\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/03610470.2022.2150994\u003c/span\u003e\u003cspan address=\"10.1080/03610470.2022.2150994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmisi AK, Kasonga TK, Mbwanganga KB, Bwanganga JCT (eds) (sp) Optimization of red sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moench) malt mashing using Response Surface Methodology. A Journ Appl Sc. in press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmisi AK, Mava D, Kasonga TR, Makaba MER, Bwanganga JCT (2020) Effects of gibberellic acid on the synthesis of alpha and beta amylase during sorghum malting (\u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench). Rev Afr Envir Agric 3(3):89\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmisi AK (2010) Essai de d\u0026eacute;salcoolisation de la bi\u0026egrave;re et appr\u0026eacute;ciation de son aptitude au stockage, M\u0026eacute;moire de master. Universit\u0026eacute; S\u0026acirc;ad Dahlab of Blida, Alg\u0026eacute;rie, p 56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmisi AK, Baguma KP, Kibi KS, Mubiala KM, Kimbemuken TE, Bwanganga JCT (2019) Modeling of the inhibitory effect of phenolic compounds on the induction of α-amylase synthesis by gibberellic acid during malting of red sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench). Rev Afr Envir Agric 2(1):40\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmisi AK, Lunda NM, Kibi KS, Bwanganga JCT (2021) Contribution to the use of red sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L. Moench) in brewing: choice of brewing conditions (temperature and pH). Rev Afr Envir Agric 5(4):43\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwika JM, Rooney LW (2004) Sorghum phytochemicals and their potential impact on human health. Phytochem 65:1199\u0026ndash;1221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBa K, Tine E, Destain J, Cisse N, Thonart P (2010) Comparative study of phenolic compounds, the antioxidant power of different varieties of Senegalese sorghum and the amylolytic enzymes of their malt. Biotech Agro Soc Envir 14(1):131\u0026ndash;139\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajomo MF, Young TW (1994) Fermentation of worts made from 100% raw sorghum and enzymes. J Inst Brew 100:79\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeta T, Corke H, Taylor JRN, Rooney LW (1999) Effect of steeping treatment on pasting and thermal properties of sorghum starches. Cereal Chem 78(3):303\u0026ndash;306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoffill-Rodr\u0026iacute;guez Y, Gallardo-Aguilar I (2014) Ventajas de la producci\u0026oacute;n de cerveza \u0026agrave; partir de malta de sorgo. Rev Biblio Tecno Qu\u0026iacute;m 34(3):324\u0026ndash;334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreeuwer P, Abee T (2000) Assessment of viability of microorganisms employing fluorescence techniques. Int J food microbiol 55:193\u0026ndash;200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwanganga JCT, Ba K, Destain J, Malumba PK, B\u0026eacute;ra F, Thonart P (2013a) Towards an integration of sorghum as a raw material for modern brewing (bibliographical synthesis). Biotech Agro Soc Envir 17(4):622\u0026ndash;633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwanganga JCT, B\u0026eacute;ra F, Thonart P (2012) Optimizing red sorghum malt quality when \u003cem\u003eBacillus subtilis\u003c/em\u003e is used during steeping to control mold growth. J Inst Brew 118(3):295\u0026ndash;304\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwanganga JCT, B\u0026eacute;ra F, Thonart P (2013b) Modeling the β -amylase activity during red sorghum malting when \u003cem\u003eBacillus subtilis\u003c/em\u003e is used to control mold growth. J Cereal Sc 57:115\u0026ndash;119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwanganga JCT, Buetusiwa T, Minengu JM, Kibal I, Tshiala H (2015) Effects of phenolic compounds on the hydrolysis of red sorghum starch by extracted red sorghum malt - α - and β -amylases. Starch Journ, 201400220.R, 20p\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBwanganga JCT, Amisi AK, Kabiona F, Bguma KP (2018) Modeling of dehydration and destruction of α-amylase activity during kilning of a green malt of a red sorghum ecotype (\u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moench). Rev Afr Envir Agric 1(2):2\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChevassus-Agn\u0026egrave;s S, Favier JC, Joseph A (1976) Traditional technology and nutritional value of sorghum beers from Cameroon. Cah Nutr Diet 11:89\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoulibaly WH, N'guessan KF, Coulibaly I, Dj\u0026egrave; KM, Thonart F (2014) Yeasts and lactic acid bacteria in traditional sorghum-based beers produced in sub-Saharan Africa (bibliographic summary). 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Blackwell Science, London, p 287\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Sorghum bicolor, fermentation, beer, optimization, brewing, Saccharomyces cerevisiae","lastPublishedDoi":"10.21203/rs.3.rs-5311747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5311747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fermentation of sorghum wort is one of the fundamental steps that determine the quality of this beer. It is influenced by several parameters, among others: temperature, pH, sugar content, amino acid profile, phenolic compound content, and redox potential. Controlling these parameters will therefore make it possible to optimize the production of alcohol and the growth of the yeast during fermentation.\u003c/p\u003e \u003cp\u003eThree parameters were considered in this work, namely: The polyphenol content in the grain (Discolored and non-discolored sorghum), temperature (10 and 25\u0026deg;C) and fermentation time (in hours).\u003c/p\u003e \u003cp\u003eThe results obtained give a better alcohol content of 4.8% for the E1T1 (Discolored sorghum, fermentation at 10\u0026deg;C). The comparison of the means confirms that, and simply means that the extraction of the phenolic compounds before malting as well as the fermentation temperature had a significant effect on the production of alcohol and the growth of the yeast.\u003c/p\u003e \u003cp\u003eThe results of this work open the door to many other studies to provide to the industrial and artisanal brewers with scientific data that will allow them to better integrate red sorghum malt as raw material into brewing.\u003c/p\u003e","manuscriptTitle":"Enhancing Saccharomyces cerevisiae alcohol production by Discoloring red sorghum (Sorghum bicolor L. Moench) malt: use of Gompertz 3-parameters Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 04:07:12","doi":"10.21203/rs.3.rs-5311747/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":"7b968932-1674-4e29-b78f-3f2e6d41b501","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-27T17:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-25 04:07:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5311747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5311747","identity":"rs-5311747","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}