1. Introduction
Beer forms a complex chemical matrix of components that result from numerous metabolic pathways and chemical reactions. He et al. [1] underlined the importance of interaction among various biosynthetic pathways during the fermentation process in a living yeast cell. The acceptable sensory properties of beer depend greatly on the control of the formation of desired volatile compounds during fermentation that is also important for achieving a repeatable and balanced composition of the finished product [2].
Many compounds contribute to the flavor and aroma of beer. Due to the low taste thresholds for some of these substances, supposedly insignificant variations in their concentrations may produce an entirely different flavor of the final beer. Thus, for pilsner beer, at least twenty compounds are recognized as being important. These substances include several esters, fusel alcohols, vicinal diketones, and organic sulfur compounds [3]. The latter two, which are present in a fresh, ‘green’ beer, are significantly reduced during lagering. The following compounds are considered the most important: Isoamyl alcohol, ethyl acetate, isoamyl acetate, ethyl hexanoate, and ethyl octanoate. However, it would be an over-simplification to characterize the taste of beer just by the analytical determination of these five compounds. In practice, the flavor of one compound may easily be suppressed by high concentrations of other substances [4].
High-gravity brewing (HGB) employs worts at higher than normal concentrations (15–20 °P original gravity), and, therefore, to obtain beer of sales-gravity, dilution with deaerated water is required at a later stage of processing. HGB increases production without significant expansion of brewing, fermenting, and storage facilities. Therefore, the principal advantage of HGB is the more efficient use of the existing processes. The disadvantages of HGB include decreased foam stability of beer, a variety of stress effects on yeast, and problems with desirable flavor. Finally, difficulties encountered in HGB include the inability of yeast to completely utilize maltotriose, which is the most abundant fermentable sugar in the wort. This is particularly the case during beer production by continuous fermentation of wort under HGB conditions [5].
A factor of great importance in determining the flavor of beer is the composition of the wort. Small differences in wort composition can exert significant effects on the flavor of the resulting beer. Amino acids are among the wort components that may significantly influence beer flavor [6]. Volatile esters introduce fruity flavor notes and are considered highly positive flavor attributes of a fresh beer. Isoamyl acetate, for example, is a source of a banana-like flavor. However, during storage, the concentration of this ester can decrease to the levels that are well below its threshold level [7,8]. Among the volatiles, acetate esters like ethyl acetate, hexyl acetate, isoamyl acetate, and 2-phenylethyl acetate are recognized as a special and characteristic group of important flavor compounds of a lager beer [9].
Aroma-active esters are synthesized by yeast during fermentation in the intracellular space. It has been demonstrated that the esters partition between cells and fermenting medium depends mainly on yeast species and on the fermentation temperature. A higher proportion of esters that remain inside the cells are characteristic for lager yeasts (Saccharomyces pastorianus, Saccharomyces carlsbergensis, Saccharomyces uvarum) [10]. Moonjai with co-workers [11] reported that by the enrichment of wort with lipids, and also by increasing the level of wort aeration, synthesis of volatile esters was drastically reduced. The authors investigated the influence of the addition of unsaturated fatty acid (mainly linoleic acid) to harvested yeast prior to pitching, on the fermentation yield and on the synthesis of volatile flavor compounds, and found that the supplemented pitching yeast showed growth, attenuation, and ethanol formation profiles similar to those obtained with unsupplemented yeast in pre-aerated medium, which simulated the normal brewing practice. Compared to fermentations with unsaturated fatty acids added to the medium, the supplemented cropped yeast did not induce a reduction in acetate ester synthesis. Results indicated that the supplementation of cropped yeast with unsaturated fatty acids could be an interesting alternative to wort oxygenation to restore the optimal membrane fluidity of the yeast. Renger et al. [4], in turn, showed the importance of carbon dioxide for the growth and metabolism of fermenting yeasts. The excess of carbon dioxide had an inhibiting effect on the production of aroma compounds.
Amyl alcohol is reported to be the most present and quantitatively significant flavor compound of the higher alcohols group. Amyl, and its active isomer isoamyl alcohols, are, most of the time, described as amyl alcohols. These compounds affect beer drinkability as beer flavor is described by sensory analysis as “heavier” when the content of amyl alcohol increases. Another higher alcohol that affects the sensory quality of beer is isobutyl alcohol [10]. It may be stressed, therefore, that for the proper sensory characteristics of beer, the process optimization must ensure the maximization of ester concentration and, particularly, ethyl acetate that, in the right amounts, gives beer the fruity aroma impression, and also isoamyl acetate, which, in turn, produces a banana scent. On the other hand, however, the well-chosen process parameters should minimize the content of higher alcohols that generate the undesirable fragrances like alcohol, sweet, or a solvent scent.
Trelea et al. [12] presented an intriguing possibility of reducing the fermentation time without changing the aroma profile of beer volatile components. A few groups of researchers successfully developed predictive experimental models that incorporated process parameters for modulating the biosynthesis of flavor-active compounds in the fermenting yeast cells [3,13,14,15], but none of these endeavors were performed utilizing a commercial fermentation plant.
Currently, statistical process control (SPC) techniques are increasingly being used in brewing. They allow controlled process maintenance with a very high repeatability of the process and the desired quality of the final beer. The most optimal fermentation process parameters developed by response surface methodology (RSM) guarantee the obtaining of a high stability of processes calculated by SPC. The production of beer on an industrial scale often employs constant values of temperature and regular pressure profiles. For many reasons, determination of optimal process parameters for a particular production plant is crucial. The key fermentation parameters of bottom-fermented lager beers brewed on an industrial scale can be successfully predicted, modulated, and controlled by applying the RSM methodology so that appropriate flavor and aroma compounds are synthesized at optimal concentrations. The purpose of the current study was to apply the RSM methodology by developing empirical models to modulate the values of the fermentation temperature, pitching rate, aeration levels, and different times of filling the cylindroconical fermentation tanks in the industrial brewery, to control and predict the concentrations of volatile esters and fusel alcohols in a lager beer. The variations in the key process parameters had been limited, however, to be acceptable and ready for the market, and lager beer was also produced under experimental regimes. Yet, another aim of the study was to optimize the flavor and aroma compound concentrations to levels that ensure the best sensory quality of the final beer. 2. Materials and Methods 2.1. Experimental Setup The process of beer fermentation and maturation was investigated in industrial cylindroconical fermentation tanks (CCT—cylindroconical fermentation tanks; gross volume 3850 hL with diameter 5.15 m and height 20 m) with different times (4.5–13.5 h) of filling CCTs. The experiments were carried out in a big commercial brewery in Poland. Each fermentation tank was filled with three brews (wort volume in every CCT—3090 hL). HGB worts (high gravity 15.5 °P) were prepared from the same batch of malt under identical technological conditions. A pilsener-type malt from two malt houses was used throughout the experiments. The process of infusion mashing-in took place within the standard scale of 60–76 °C. Sample collection started after filling the CCT and was continued during the following 18 days of the production cycle. Sampling from a tank was performed using a sampling device equipped with an installed small pump working in a closed loop system, which let us take samples of fermenting wort and of matured beer. Samples of beer were taken from CCTs at a point located above the conical part, 5 m from the bottom of the tank. Saccharomyces pastorianus brewers W34/70 yeast strain from Weihenstephan TUM was used for the fermentation. Total fermentation time lasted between 7 and 9 days depending on the selected fermentation parameters. The process of maturation was divided into two phases: Warm maturation and lagering. Yeast for experiments was cropped from CCTs during the 5th day of maturation (at temperature 13 °C). The warm maturation lasted 5 days at a temperature of 13 °C. After cropping, the yeast in the YST (yeast stored tank) was stored for a maximum of 4 days at temperatures of 1.3–1.8 °C with an overpressure of 0.05 bar. After cropping, the beer was cooled down to −0.7 °C (to phase of lagering). Yeast was pitched for first brews, using the fully automatic high-precision ABER system for rate control. Worts were aerated by compressed, sterile air during transfer to each CCT, with an identical intensity of 10 mg O2/L wort. The processes of fermentation and maturation were carried out in the same technological conditions. The yeast growth ranged from volume factor 2.60 to 4.0 in relation to the initial pitching rate, and the Free Amino Acids (FAN) consumption varied from 112 to 144 mg/L. 2.2. Analytical Procedures
Extract marking was performed using an automatic wort and beer analyzer (Beer Analyzer DMA 4500+ Anton Paar, Graz, Austria), at 20 °C, and the specific weight was measured using an oscillating densitometer. The Tabarié formula was the basis for ‘Alcolyzer’ beer calculations [16].
Qualitative and quantitative analysis of volatile components (the identification was done on the basis of retention time) was performed using gas chromatograph GC 8000 (Fisons Instruments, Ipswich, UK) fitted with a flame ionization detector GC-FID and detector GC-ECD for detection of diacetyl, 2,3-pentanedione. The column temperature was kept at 45 °C for 10 min, increased to 120 °C at 5 °C/min, and then held at that temperature for 8 min, eventually being lowered to 45 °C at 15 °C/min. The temperature of the injection zone was fixed at 140 °C. The carrier gas was helium at a pressure of 65 kPa, with a flow of 4–6 mL/min. Injection of samples (0.75 mL) was performed with an HS-800 autosampler. The sample annealing temperature was 40 °C for 40 min. The temperature of the autosampler syringe was 60 °C. Concentrations were calculated using a quantitative computer program based on the calculated peak area. Selected components of beer were determined using surfaces under the curves produced relative to internal standards. The internal standard method involved introducing an internal standard to a test sample and determining the relationship between the peak area ratio of the test substance and the internal standard and the mass ratio of the test substance and the internal standard. The standards had to be well separated from other peaks in the sample and have a similar concentration as the substance to be determined. The sample of beer, with a volume of 2.5 mL, was placed in a vial and conditioned at 40 °C for 40 min to equilibrate the liquid and gas phase (head space method). The capillary column DB-WAX (dimensions: 60 m long, 0.53 mm internal diameter, and 1 μm thick) packed with polar polyethylene glycol was used for the separation. A mixture of 3-panthenol and n-butanol was used as an internal standard for the determination of esters, amyl alcohols, and the sum of higher alcohols. The chromatograph was calibrated once a month. Before and after each series of measurements, a comparative analysis was carried out with a beer sample used as a control (reference) batch. 2.3. Sensory Analysis Sensory evaluation of bottling beer used a comparison test, with the test sample compared to the reference beer profile. The beer was tested in black glasses. Profile tests involved the evaluation of attributes of the beer, including fruity aroma esters, hops, bitterness, sulfur compounds, sweetness, acidity, fullness, balance, and flavor. The sensory analysis panel consisted of nine employees from the production, analysis, and technology departments whose standard job was to routinely assess the sensory quality of beer. The sample coding procedures used ensured objective evaluations. The sensory quality beer was evaluated using a gradation scale from 50 to 75 points where 70–75 points meant a very good or perfect example of beer; 65–69 points represented good, clean, and fresh beer; 60–64 points were allocated to neither good nor bad beer with low levels of undesirable flavors and aromas, 55–59 points - beer with one or more intense undesirable flavors and aromas, and 50–54 points represented a very bad - unfit for consumption, wrong product. 2.4. Statistical Analyses Processing factors were tested using the Experimental Design Module of the Statgraphics Centurion XVII ver. 17.1.12 (Professional Edition statistical software, Statpoint Technologies, Inc., Warrenton, Virginia).
2.4.1. Optimization of The Volatiles and Sensory Quality of Beer
The influences of process parameters on the volatile concentrations and on the sensory quality of beer were studied using a fully randomized Box–Behnken design with four factors at three levels each and two blocks, including 3 centerpoints per block, which yielded 54 experimental runs and 38 degrees of freedom. There were two blocks with repetitions (all experiments were performed twice) serving as a block. The central composite design could not be used in this work, because it would generate experimental values of process parameters, resulting in the production of abnormal beer, not acceptable on the market. Table 1 illustrates the coded and actual values of the input variables (fermentation process parameters). Experimental worts were fermented using various pitching rates (6–10 mln cells/mL), aeration levels (8–12 mg/mL), times (4.5–13.5 h) of filling CCTs (cylindroconical fermentation tanks; 3850 hL), and fermentation temperatures (8.5–11.5 °C). The manufactured beer was then subjected to the volatile concentration and sensory quality analyses (measured responses). The relationship between the measured exposures and fermentation process parameters was expressed using second-order polynomial equations:
y = β0 + β1x1 + β2x2 + β3x3 + β4x4 + β11x12 + β22x22 + β33x32 + β44x42 + β12x1x2 + β13x1x3 + β23x2x3 + β14x1x4 + β24x2x4 + β34x3x4 + γ
where y is a volatile concentration or sensory quality; x1 is the pitching rate (mln yeast cells/mL of wort); x2 is the fermentation temperature (°C); x3 is the aeration level (mg O2/L); x4 is the total time used for CTT filling (h); β0 is the intercept coefficient; β1–4 are the linear coefficients; β11, β22, β33, and β44 are the quadratic coefficients; and β12, β13, β23, β14, β24, and β34 are the interaction coefficients, whereas γ is the block effect.
The established models were subjected to ANOVA and Pareto chart (data not shown) analyses, and the non-significant (p > 0.05) components were removed from the models. To evaluate the statistical significance of the second-order polynomial model, the coefficient of determination (R2) and the probability of the lack-of-fit values were calculated.
2.4.2. Multiple Response Optimization Procedures
The module Multiple Response Analysis of the Statgraphics Centurion XVII ver. 17.1.12 (Professional Edition statistical software, Statpoint Technologies, Inc., Warrenton, Virginia) was used to establish the values of technological parameters that simultaneously optimized the content of a few measured responses. 3. Results and Discussion 3.1. Model Fitting
A significant influence of the process parameters with the coefficient of determination exceeding 0.70 on the ethyl acetate, isoamyl acetate, higher alcohols, amyl alcohols, and isobutanol concentrations was observed, but in the case of methanol, 1-propanol, ethyl formate, ethyl capronate, and ethyl propionate, lower values of R2 were calculated within the studied ranges of the pitching rate, fermentation temperature, aeration level, and times of CTT filling (Table 2). The volatiles with a determination coefficient lower than 0.60 were excluded from further optimization.
3.2. Polynomial Equations for the Measured Responses Ethyl acetate
Table 3 shows the analysis of variance for ethyl acetate content in matured beer after removing insignificant components from the model. The relationship between the only two significant factors and the predicted responses of ethyl acetate concentrations was modeled as follows:
y1 = −1.295 + 0.850 x1 + 1.293 x2
where y1 denotes ethyl acetate concentration.
As shown in Table 3, small p values (<0.05) were observed for the constant, a linear term x1, and linear term x2, indicating that only pitching rate and fermentation temperature were the crucial process parameters affecting amounts of ethyl acetate in beer. The subsequent application of the established model to predict the process parameters optimal for ethyl acetate biosynthesis using the Optimize Response module revealed that over the studied range of process parameters, to achieve maximized ethyl acetate concentrations, the values of process parameters should be set to x1 = 10, x2 = 11.5, x3 = 10, and x4 = 9. Under these conditions, 22.07 mg/L of ethyl acetate was predicted to be synthesized, a value that can be perceived as an acceptable level of this volatile in beer. Verstrepen with co-workers [17], who reviewed the literature on wort specific gravity and wort sugar profiles, reported the problem of overproduction of acetate esters in the HGB.
There seems to be a general consensus in the literature, however, that with growing fermentation temperature, the biosynthesis of acetate esters is enhanced [12,15,17]. In the work of Lee and Davis [18], a 10-fold increase in pitching rate caused a two-fold increase in the concentration of ethyl acetate in beer that seems to be in line with the results of our studies. The data presented by other researchers, however, did not confirm these observations. Verbelen et al. [19] did not show changes in the content of ethyl acetate with increasing amounts of inoculum from 10 to 120 mln cells/mL. Similar findings were provided by Erten [20] who reported that, as a result of increasing yeast pitching rate from 1 × 107 to 1 × 108 cells/mL, the concentration of that volatile remained within the range of 13–14 mg/L and was not statistically significant. These discrepancies might mainly be attributed to differences in working volumes of the experimental fermentors used and, consequently, to differences in hydrostatic pressure that affected the metabolism of yeast.
Isoamyl acetate
Similarly to ethyl acetate, the analysis of variance for isoamyl acetate concentrations in beer revealed that yeast pitching rate and fermentation temperature significantly affected the biosynthesis of this ester (Table 4).
Linear in nature, a simple equation that links amounts of isoamyl acetate with process parameters was the following:
y2 = −0.857 + 0.0869 x1 + 0.192 x2
where y2 denotes isoamyl acetate concentration in mg/L.
Maximizing the predicted isoamyl acetate concentrations by means of the Optimize Response module allowed us to achieve 2.22 mg of that volatile per liter of beer. The values of process parameters set at x1 = 10, x2 = 11.5, x3 = 10, and x4 = 9 were calculated as optimal for the highest ethyl acetate concentrations. Nakatani with co-workers [21] reported double the rate of isoamyl acetate synthesis, which resulted from rising fermentation temperature from 10 to 15 °C. Lima et al. [15] and Brown and Hammond [13] observed similar tendencies. In yet another study, Saerens et al. [22] demonstrated that increasing the fermentation temperature by 3 °C resulted in a 50% increase in the concentration of isoamyl acetate. Our studies also suggest that the initial rate of yeast addition to wort was positively related to concentrations of this volatile in beer. Verbelen with co-workers [19] provided evidence of the significance of such a correlation within the range of yeast concentrations from 10 to 40 mln cells/mL.
Amyl alcohols and the sum of higher alcohols
The analysis of variance for amyl alcohol concentrations in beer is given in Table 5, whereas similar analysis for the sum of higher alcohols is shown in Table 6.
The striking similarity of significant components in models predicting concentrations of amyl alcohols and the sum of higher alcohols suggested the important role of the volatile amyl alcohols in these beer flavor components. With the exception of aeration rate, all other process parameters of beer fermentation significantly modulated the concentrations of volatile alcohols in beer. As shown in Table 5 and Table 6, among process parameters, p values < 0.05 were observed for linear terms of x1, x2, and x4, as well as for interaction term x3 x4.
The polynomial functions for amyl alcohols:
y3 = 9.498 + 1.478x1 + 2.012x2 + 2.270x3 + 3.854x4 − 0.244x3x4
and for the sum of higher alcohols:
y4 = 18.116 + 2.274x1 + 1.640x2 + 3.395x3 + 5.305x4 − 0.354x3x4
allowed us to recognize the time of CTT filling as the most significant factor. The established models were subsequently applied to predict the optimal process parameters for minimizing higher alcohol concentrations in beer. The value of each of the process parameters when kept at the lowest levels guaranteed values as low as 62.30 mg/L of amyl alcohols, and 85.6 mg/L of higher alcohols in beer.
There have been multiple experiments undertaken to assess the impact of process parameters on the higher alcohol concentration in fermenting wort. Jones with co-workers [23] showed that the content of fusel alcohols like isobutanol, isoamyl alcohol, and 1-propanol was only slightly higher when additional amounts of oxygen were applied 12 h post-inoculation. Erten et al. [20] reported that a higher pitching rate led to an increase in the concentration of isobutanol but also to a decrease in the content of active amyl alcohols, like 2-methyl-1-butanol. Jones with co-workers [23], however, reported that the concentrations of 1-propanol increased with higher yeast pitching rates. The experiments conducted by Lima et al. [15] also confirmed that increased pitching rate from 15 to 22 mln cells/mL caused a rise in the concentration of 1-propanol. There seems to be a general agreement in the literature that biosynthesis of fusel alcohols is positively related to the temperature of fermentation [3,13,24,25]. All these reports seem to be in good agreement with the observations of this study.
Sensory quality The proper and unchanging sensory quality of beer is one of the most important problems in brewing, particularly in the HGB method. The flavor stability and repeatability of good sensory properties of beer were maintained throughout the current study (65.7 to 66.7 points).
Table 7 lists significant components of the model that relates the sensory quality of beer to the values of process parameters applied during fermentation. A few two-factor interaction terms were found insignificant in the original model, and each of the process parameters had a significant quadratic term. Fermentation temperature appeared to be the key factor influencing the sensory characteristic of lager beer. Both a linear and quadratic component of the temperature, as well as a significant interaction of the temperature with aeration level, were among the main determinants of the sensory quality. There was also a significant negative effect of the pitching rate and a positive effect of its quadratic component. The complete polynomial equation that related the sensory quality of beer to the values of process parameters was the following:
y5 = 61.255 − 0.691 x1 + 1.693 x2 − 0.148 x3 + 0.0370 x4 + 0.0828 x12 − 0.0542 x1 x2 − 0.0167x1 x4 − 0.115 x22 + 0.121 x2 x4 − 0.057 x32 + 0.0083 x42
where y6 denotes the sensory quality of beer (in points).
It appears that due to the significant interaction term (x2x3), the level of wort aeration determined the character of changes in the sensory quality of beer, which resulted from different fermentation temperatures. At high aeration level, the raising temperature enhanced the sensory quality of beer almost linearly, but at a low aeration rate, there was a plateau and then a decline in beer quality. When Equation (5) was applied for maximizing sensory quality, a lager with excellent sensory quality (67 points) was predicted when the pitching rate was set at the low level and all the other parameters were maintained at their respective high values. In one-factor experiments, a direct, positive relationship between fermentation temperature and the sensory quality of beer was reported by Brown and Hammond [13]. The direct connection of a correct wort oxygenation and the sensory quality of beer that was observed in this study had also been reported earlier [19].
3.3. Multiple Response Optimization Procedures Effects of process parameters on the multiple measured exposures were assessed by the Multiple Response Optimization procedure of the Statgraphics software.
At first, combined maximization of volatile estes (ethyl acetate and isoamyl acetate) concentrations was undertaken. The next optimization that involved all volatile components comprised the maximization of ester concentrations and simultaneous minimization of volatile alcohols, i.e., isobutanol and the sum of higher alcohols. The final optimization (“optimize all”) also included the maximization of beer sensory quality. A comparison of results from the single response optimizations described earlier to those originating from the multiple response optimization procedure, as well as the predicted values for each of the measured beer volatile and the sensory quality of beer, is presented in the Table 8.
As evidenced in Table 8, the fermentation temperature set at the high level (11.5 °C; +1) optimized the concentrations of volatile esters, the sum of volatile substances, and the sensory quality of beer and was also required for the overall optimization. The low pitching rate (−1) guaranteed the highest sensory quality, but the high pitching rate (+1) was necessary for both optimal volatile concentrations, and for the general optimization. The time of CTT filling was calculated to be set at 13.5 h (+1) for optimal volatile ester concentration and for good sensory quality of beer; however, for the overall optimization, which, in addition to sensory quality, comprised both volatile esters and volatile alcohols, the short time of CTT filling (4.5 h; −1) was simulated. With the exception of fermentation temperature, the levels of process parameters that were simulated to guarantee optimal concentrations of volatile compounds in beer differed from those that were calculated as optimal for the sensory quality of beer. This finding clearly suggests that volatiles other than esters and fusel alcohols play a predominant role in determining the sensory quality of beer. In our previous study [26], the process parameters that optimized acetaldehyde and DMS concentrations were the same as those that maximized the sensory quality of beer. Volatile compounds in fermented beverages have been found to strongly influence the sensory characteristics of the product, and, therefore, their identification and optimization are of utmost importance for understanding the relationships among different process parameters and chemical composition, as well as for maintaining and for further enhancements in the product quality [27].
There have been attempts to optimize process parameters in high-gravity brewing fermentations performed in a laboratory or in a pilot plant by varying values of the most important process parameters in statistically designed experiments [23]. None of these endeavors, however, have aimed to provide a direct relationship between results of such optimization and the sensory quality of beer produced on an industrial scale.
4. Conclusions The multiple response optimization procedure of the Statgraphics software allowed us to find levels of the process parameters that optimized concentrations of esters, higher alcohols, and the sensory quality of a lager beer. The values of process parameters that maximized the concentrations of volatile esters differed from those that minimized the concentrations of higher alcohols. The simultaneous optimization of volatile compounds and the sensory quality of beer yielded overall values of process parameters: Pitching rate—10 mln cells per mL; fermentation temperature—11.5 °C; aeration level—8.8 mgO2/L, and time of filling CCTs—4.5 h. These levels may be perceived as a result of a compromise between optimal volatile esters and higher alcohol concentrations. It is suggested that volatiles other than esters and fusel alcohols must have played a predominant role in determining the sensory quality of beer. We suggest that the RSM modeling can be successfully used for prediction and control of important process parameters of fermentation performed in an industrial plant to have desirable taste and aroma of bottom-fermented lager beers.
| Independent Variables | Units | Symbol | Coded Levels | ||
|---|---|---|---|---|---|
| −1 | 0 | +1 | |||
| Pitching rate | Mln cells/mL | x1 | 6 | 8 | 10 |
| Fermentation temperature | °C | x2 | 8.5 | 10 | 11.5 |
| Aeration level | mg/L | x3 | 8 | 10 | 12 |
| Total time of CCT filling | h | x4 | 4.5 | 9 | 13.5 |
| Dependent Parameter | Analysis of Variance | |||||||
|---|---|---|---|---|---|---|---|---|
| R2 | Lack-Of-Fit | x1 | x2 | x3 | x4 | Significant Components of the Model | ||
| Probability | ||||||||
| Higher alcohols | 0.91 | 0.932 | 0.022 | 0.0193 | Ns | 0.0003 | 0.0473 | x3x4 |
| Amyl alcohols | 0.91 | 0.808 | 0.027 | 0.025 | Ns | 0.0001 | 0.0473 | x3x4 |
| Methanol | 0.53 | 0.578 | 0.0307 | ns | Ns | 0.0400 | 0.0327 | blocks |
| Isobutanol | 0.68 | 0.358 | 0.0039 | 0.0461 | Ns | 0.035 | 0.0061 | x12 |
| 1-propanol | 0.30 | 0.000 | 0.0001 | 0.027 | Ns | 0.0001 | 0.0001 | x12 |
| Ethyl acetate | 0.89 | 0.967 | 0.0032 | 0.0019 | Ns | ns | - | - |
| Isoamyl acetate | 0.69 | 0.953 | 0.0298 | 0.0054 | Ns | ns | - | - |
| Ethyl formiate | 0.62 | 0.813 | 0.0356 | ns | Ns | ns | 0.0488 | x1x4 |
| Ethyl capronate | 0.56 | 0.0967 | ns | ns | Ns | 0.0247 | - | - |
| Ethyl propionate | 0.39 | 0.450 | 0.0179 | 0.0131 | Ns | ns | - | - |
| Sensory analysis | 0.71 | 0.0951 | 0.0213 | 0.0012 | 0.0089 | 0.0272 | 0.0021 | x12 |
| 0.0474 | x1x2 | |||||||
| 0.0299 | x1x4 | |||||||
| 0.0040 | x22 | |||||||
| 0.0021 | x2x3 | |||||||
| 0.0102 | x32 | |||||||
| 0.0384 | x42 | |||||||
| Source | Sum of Squares | Df | Mean Square | F-Ratio | p-Value |
|---|---|---|---|---|---|
| x1 | 69.2920 | 1 | 69.2920 | 54.14 | 0.0000 |
| x2 | 90.2682 | 1 | 90.2682 | 70.53 | 0.0000 |
| blocks | 0.0308 | 1 | 0.0308 | 0.02 | 0.8776 |
| Lack-of-fit | 24.1341 | 14 | 1.7239 | 1.35 | 0.2295 |
| Pure error | 46.0771 | 36 | 1.2799 | ||
| Total (correlation) | 229.8020 | 53 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | p-Value |
|---|---|---|---|---|---|
| x1 | 0.7245 | 1 | 0.7245 | 22.50 | 0.0000 |
| x2 | 1.9982 | 1 | 1.9982 | 62.05 | 0.0000 |
| Blocks | 0.0011 | 1 | 0.0011 | 0.03 | 0.8566 |
| Lack-of-fit | 0.5608 | 14 | 0.0400 | 1.24 | 0.2885 |
| Pure error | 1.1592 | 36 | 0.0322 | ||
| Total (correlation) | 4.4437 | 53 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | p-Value |
|---|---|---|---|---|---|
| x1 | 209.5690 | 1 | 209.5690 | 43.40 | 0.0027 |
| x2 | 218.6480 | 1 | 218.6480 | 45.28 | 0.0025 |
| x3 | 0.4817 | 1 | 0.4817 | 0.10 | 0.7679 |
| x4 | 968.5020 | 1 | 68.5020 | 200.56 | 0.0001 |
| x3 x4 | 38.6760 | 1 | 38.6760 | 8.01 | 0.0473 |
| Blocks | 7.7521 | 1 | 7.7521 | 1.61 | 0.2739 |
| Lack-of-fit | 129.9700 | 43 | 3.0226 | 0.63 | 0.8077 |
| Pure error | 19.3162 | 4 | 4.8291 | ||
| Total (correlation) | 1592.9100 | 53 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | p-Value |
|---|---|---|---|---|---|
| x1 | 496.4050 | 1 | 496.4050 | 49.00 | 0.0022 |
| x2 | 145.2880 | 1 | 145.2880 | 14.34 | 0.0193 |
| x3 | 2420 | 1 | 4.2420 | 0.42 | 0.5528 |
| x4 | 1515.1100 | 1 | 1515.1100 | 149.55 | 0.0003 |
| x3 x4 | 81.1538 | 1 | 81.1538 | 8.01 | 0.0473 |
| Blocks | 30.1953 | 1 | 30.1953 | 2.98 | 0.1594 |
| Lack-of-fit | 250.8160 | 43 | 5.8329 | 0.58 | 0.8406 |
| Pure error | 40.5250 | 4 | 10.1312 | ||
| Total (correlation) | 2563.7400 | 53 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | p-Value |
|---|---|---|---|---|---|
| x1 | 0.3267 | 1 | 0.3267 | 17.40 | 0.0145 |
| x2 | 1.4259 | 1 | 1.4259 | 74.40 | 0.0010 |
| x3 | 0.6176 | 1 | 0.6176 | 32.22 | 0.0048 |
| x4 | 0.2017 | 1 | 0.2017 | 10.52 | 0.0316 |
| x12 | 1.1704 | 1 | 1.1704 | 61.07 | 0.0014 |
| x1 x2 | 0.2113 | 1 | 0.2113 | 11.02 | 0.0294 |
| x1 x4 | 0.1800 | 1 | 0.1800 | 9.39 | 0.0375 |
| x22 | 0.7177 | 1 | 0.7177 | 37.44 | 0.0036 |
| x2 x3 | 1.0513 | 1 | 1.0513 | 54.85 | 0.0018 |
| x32 | 0.5551 | 1 | 0.5551 | 28.96 | 0.0058 |
| x42 | 0.3038 | 1 | 0.3038 | 15.85 | 0.0164 |
| Blocks | 0.0007 | 1 | 0.0007 | 0.03 | 0.8648 |
| Lack-of-fit | 3.2974 | 37 | 0.0891 | 4.65 | 0.0716 |
| Pure error | 0.09 | 4 | 0.0225 | ||
| Total (correlation) | 11.457 | 53 |
| Technological Parameters | Levels | Optimum/Goal | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| EtAcet | IsAc | Esters | Isobutanol | HA | Alcohols | Sensory | Volatiles | All | |||
| −1 | +1 | Maximize | Minimize | Maximize | Optimize | Optimize | |||||
| Pitching rate (mln cells/mL) | 6.0 | 10.0 | 10.0 | 10.0 | 10.0 | 6.0 | 6.1 | 6.0 | 6.0 | 10.0 | 10.0 |
| Temperature of fermentation (°C) | 8.5 | 11.5 | 11.5 | 11.5 | 11.5 | 11.3 | 8.8 | 9.6 | 11.5 | 11.5 | 11.5 |
| Wort aeration level (mg/L) | 8.0 | 12.0 | 10.0 | 9.6 | 10.0 | 8.0 | 8.0 | 8.0 | 11.1 | 8.1 | 8.8 |
| Total filling time CCTs (h) | 4.5 | 13.5 | 9.0 | 13.5 | 13.5 | 4.5 | 4.5 | 4.6 | 13.5 | 4.7 | 4.5 |
| Volatiles/Sensory | Predicted Values | ||||||||||
| Ethyl acetate (EtAcet; mg/L) | 22.1 | 22.1 | 22.1 | 22.0 | |||||||
| Isoamyl acetate (IsAc; mg/L) | 2.34 | 2.34 | 2.1 | 2.09 | |||||||
| Isobutanol (mg/L) | 11.8 | 12.6 | 12.6 | 12.9 | |||||||
| Higher alcohols (HA; mg/L) | 83.5 | 85.2 | 97.4 | 97.9 | |||||||
| Sensory quality (pts) | 67 | 66.4 | |||||||||
Author Contributions
Conceptualization, K.Ż.; K.K. and T.T.; methodology, K.K. and K.Ż.; software, K.Ż.; formal analysis, K.Ż.; investigation, K.Ż.; resources, K.K.; data curation, K.K.; writing-original draft preparation, K.Ż., K.K. and T.T.; writing-review and editing, T.T.; visualization, K.Ż.; supervision, K.K.; project administration, K.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
1. He, Y.; Dong, J.; Yin, H.; Zhao, Y.; Chen, R.; Wan, X.; Chen, P.; Hou, X.; Liu, J.; Chen, L. Wort composition and its impact on the flavour-active higher alcohol and ester formation of beer-A review. J. Inst. Brew. 2014, 120, 157-163.
2. Titica, M.; Landaud, S.; Trelea, I.; Latrille, E.; Corrieu, G.; Cheruy, A. Modeling of the kinetics of higher alcohols and ester production on CO2 emission with a view to control of beer flavor by temperature and top pressure. J. Am. Soc. Brew. Chem. 2000, 58, 167-174.
3. Pires, E.; Teixeira, J.; Branyik, T.; Vicente, A. Yeast: The soul of beer's aroma-a review of flavour-active esters and higher alcohols produced by the brewing yeast. Appl. Microbiol. Biotechnol. 2014, 98, 1937-1949.
4. Renger, S.; Van Hateren, S.; Luyben, K. The formation of esters and higher alcohols during brewery fermentation; the effect of carbon dioxide pressure. J. Inst. Brew. 1992, 98, 509-513.
5. Stewart, G. High gravity brewing and distilling-Past experiences and future prospects. J. Am. Soc. Brew. Chem. 2010, 68, 1-9.
6. Ferreira, I.; Guido, L. Impact of wort amino acids on beer flavour: A review. Fermentation 2019, 4, 23.
7. Vanderhaegen, B.; Necen, H.; Verachtert, H.; Derdelinckx, G. The chemistry of beer aging-a critical review. Food Chem. 2007, 95, 357-381.
8. Holt, S.; Miks, M.; Carvalho, B.; Moreno, M.; Thevelein, J. The molecular biology of fruity and floral aromas in beer and other alcoholic beverages. FEMS Microbiol. Rev. 2019, 43, 193-222.
9. Yilmaztekin, M.; Cabaroglu, T.; Erten, H. Effects of fermentation temperature and aeration on production of natural isoamyl acetate by Williopsis saturnus var. saturnus. BioMed Res. Int. 2013, 2013, 1-6.
10. Humia, B.; Santos, K.; Barbosa, A.; Sawata, M.; Mendoca, M.; Padilha, F. Beer molecules and its sensory and biological properties: A review. Molecules 2019, 24, 1568.
11. Moonjai, N.; Verstrepen, K.; Delvaux, F.; Derdelinckx, G.; Verachtert, H. The effects of linoleic acid supplementation of cropped yeast on its subsequent fermentation performance and acetate ester synthesis. J. Inst. Brew. 2002, 108, 227-235.
12. Trelea, I.; Titica, M.; Corrieu, G. Dynamic optimisation of the aroma production in brewing fermentation. J. Process Control 2004, 14, 1-16.
13. Brown, A.; Hammond, J. Flavour control in small-scale beer fermentations. Inst. Chem. E 2003, 81, 40-49.
14. Dragone, G.; Silva, D.; Almeida e Silva, J. Factors influencing ethanol production rates at high-gravity brewing. Lebensm. Wiss. Technol. 2004, 37, 797-802.
15. Lima, L.; Brandão, T.; Lima, N.; Teixeira, J. Comparing the impact of environmental factors during very high gravity brewing fermentation. J. Inst. Brew. 2011, 3, 359-367.
16. Miedaner, H. Brautechnische Analysenmethoden, Band II, Methodensammlung der Mitteleuropaischen Brautechnischen Analysenkommision, MEBAK 4th ed.; Selbstverlag der MEBAK: Munich, Germany, 2002.
17. Verstrepen, K.; Derdelinckx, G.; Dufour, J.; Winderickx, J.; Thevelein, J.; Pretorius, I.; Delvaux, F. Flavour-active esters: Adding fruitness to beer. J. Biosci. Bioeng. 2003, 2, 110-118.
18. Lee, M.; Davis, D. Fermentation intensification-Part III: The effect of increasing yeast biomass concentration on fermentation performance. BRI Q. 2000, 3, 1-4.
19. Verbelen, P.; Dekoninck, T.; Saerens, S.; Van Mulders, S.; Thevelein, M.; Delvaux, F. Impact of pitching rate on yeast fermentation performance and beer flavour. Appl. Microbiol. Cell Physiol. 2009, 82, 155-167.
20. Erten, H.; Tanguler, H.; Cakiroz, H. The effect of pitching rate on fermentation and flavour compounds in high gravity brewing. J. Inst. Brew. 2007, 113, 75-79.
21. Nakatani, K.; Fukui, N.; Nagami, K.; Nishigaki, M. Kinetic analysis of ester formation during beer fermentation. J. Am. Soc. Brew. Chem. 1991, 4, 152-157.
22. Saerens, S.; Verbelen, P.; Vanbeneden, N. Monitoring the influence of high-gravity brewing and fermentation temperature on flavour formation by analysis of gene expression levels in brewing yeast. Appl. Microbiol. Biotechnol. 2008, 80, 1039-1051.
23. Jones, H.L.; Margaritis, A.; Stewart, R. The combined effect of oxygen supply strategy, inoculum Size and temperature profile on Very-High-Gravity beer fermentations by Saccharomyces cerevisiae. J. Inst. Brew. 2007, 113, 168-184.
24. Landaud, S.; Latrille, E.; Corrieu, G. Top pressure and temperature control the fusel alcohol/ester ratio through yeast growth in beer fermentation. J. Inst. Brew. 2001, 2, 107-117.
25. Olaniran, A.O.; Hiralal, L.; Mokoena, M.P.; Pillay, B. Flavour-active volatile compounds in beer: Production, regulation and control. J. Inst. Brew. 2017, 123, 13-23.
26. Kucharczyk, K.; Żyła, K.; Tuszyński, T. Optimization of wort aeration, wort-filling time, pitching rate, and temperature levels for high-gravity brewing fermentation in an industrial brewery on volatile carbonyls, sulphur compounds and quality of a lager beer. Czech J. Food Sci. 2020. in consideration.
27. Coelho, E.; Lemos, M.; Genisheva, Z.; Domingues, L.; Vilanova, M.; Oliveira, J.M. Validation of a LLME/GC-MS methodology for quantification of volatile compounds in fermented beverages. Molecules 2020, 25, 621.
Krzysztof Kucharczyk1,*, Krzysztof Żyła2 and Tadeusz Tuszyński1
1Krakow School of Health Promotion, 31-158 Krakow, Poland
2Department of Food Biotechnology, Food Technology, University of Agriculture, 30-149 Krakow, Poland
*Author to whom correspondence should be addressed.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Contents of selected volatile esters and fusel alcohols and their relation to the sensory quality of a bottom-fermented lager beer fermented under high-gravity conditions (15.5 °P) were analyzed using response surface methodology (RSM, Box–Behnken design). The influence of various pitching rates (6–10 mln cells/mL), aeration levels (8–12 mgO2/mL), times (4.5–13.5 h) of filling CCTs (cylindroconical fermentation tanks; 3850 hL), and fermentation temperatures (8.5–11.5 °C) on the contents of selected esters, as well as on concentrations of amyl alcohols and on the sum of higher alcohols in beer, was determined in a commercial brewery fermentation plant. Beers produced throughout the experiments met or exceeded all criteria established for a commercial, marketed beer. Statistical analyses of the results revealed that within the studied ranges of process parameters, models with diversified significance described the concentrations of volatiles in beer. The multiple response optimization procedure analyses showed that the values of process parameters that minimized higher alcohols in beer (97.9 mg/L) and maximized its ethyl acetate (22.0 mg/L) and isoamyl acetate (2.09 mg/L) contents, as well as maximized the sensory quality of beer, (66.4 pts) were the following: Pitching rate 10 mln cells per mL; fermentation temperature 11.5 °C; aeration level 8.8 mg/L; and CCT filling time 4.5 h.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Żyła, Krzysztof; Tuszyński, Tadeusz