This research work was done using palm wine as a source of fermentable sugar. Equal sample
of the palm wine was fermented aerobically by baker’s yeast under standard atmospheric condition for
consecutive series of 1 to 15 days. Testing and Distillation of liquor was done on each day to determine the
amount of fatty acid, PH, sugar, specific gravity and vitamin C, with time during fermentation on one hand and
the equilibrium mole fraction relationship for ethanol and water on the other hand. Models were developed to
predict the reproduction of the experimental values into future, and, on validationgave R
2 which ranges from
0.9901 to 0.9980. On optimization it was revealed that in 1.49 days, 0.1067 percentage fatty acid was produced.
With 1.44 percentage mole fraction of ethanol, 3.398 refractive index of palm wine was obtained. It also showed
that a minimum of 0.07305 refractive index of distillate per mole fraction of more volatile component was made
in just 0.499 percentage mole fraction and a minimum of 0.1956 mole fraction of gaseous ethanol per mole
fraction of more volatile component was obtained. The results and models can be applied in the distillation
work of this kind for prediction and reproduction of experimental values.
This research work was done using palm wine as a source of fermentable sugar. Equal sample
of the palm wine was fermented aerobically by baker’s yeast under standard atmospheric condition for
consecutive series of 1 to 15 days. Testing and Distillation of liquor was done on each day to determine the
amount of fatty acid, PH, sugar, specific gravity and vitamin C, with time during fermentation on one hand and
the equilibrium mole fraction relationship for ethanol and water on the other hand. Models were developed to
predict the reproduction of the experimental values into future, and, on validationgave R
2 which ranges from
0.9901 to 0.9980. On optimization it was revealed that in 1.49 days, 0.1067 percentage fatty acid was produced.
With 1.44 percentage mole fraction of ethanol, 3.398 refractive index of palm wine was obtained. It also showed
that a minimum of 0.07305 refractive index of distillate per mole fraction of more volatile component was made
in just 0.499 percentage mole fraction and a minimum of 0.1956 mole fraction of gaseous ethanol per mole
fraction of more volatile component was obtained. The results and models can be applied in the distillation
work of this kind for prediction and reproduction of experimental values.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
Determination of Propionates and Propionic Acid in Bread Samples Using High P...theijes
In the present study, a HPLC method for determination of the preservative propionates in 7 groups of industrial bread samples is described. The separation the propionates were performed on the C18- column and Na2SO4 (8.0 mM) + H2SO4 (1.0 mM): acetonitrile (90:10, v/v %) as mobile phase. The detector wavelength was set at 210 nm. Separation of the propionates was achieved in less than 8 min. The samples first were milled and then extracted with 0.1 mol L-1 NaOH solution under ultrasonic irradiation. After centrifuge, supernatant clear solution was filtered using 0.45 µm Nylon syringe filter and 25 µL of solution was injected to HPLC loop. Analytical characteristics of the method such as limit of detection (LOD= 5 mg L-1 ), recovery percentage (>91%) and reproducibility (RSD=3.5 %) were evaluated. A plot of peak area (y) versus concentration of propionate (x, mg Kg-1 ) was linear over 10-1000 mg Kg-1 . The calibration graph can be described by the equation y = 301.64x + 9963 (r2= 0.991). The levels of propionates in bread samples ranged from 3683-4752 mg Kg-1. The results stated that High performance liquid chromatography is a simple and rapid method for the determination of propionates in bakery products.
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In the present work, we demonstrate the ability of AAnalyst™ 800 atomic absorption spectrophotometer in analyzing a variety of fish samples. Sample preparation has been done in two different ways i.e. by AOAC Method 999.10, which is the official method for the sample preparation of fish samples with microwave digestion and AOAC Method 999.11, which is the preparation of fish samples with conventional dry ashing using a muffle furnace.
Learn more about our solutions: http://bit.ly/1f7ZNRC
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparative Ethanol Productivities of Two Different Recombinant Fermenting St...IJERA Editor
Production of biofuel such as ethanol from lignocellulosic biomass is a beneficial way to meet sustainability and energy security in the future. The main challenge in bioethanol conversion is the high cost of processing, in which enzymatic hydrolysis and fermentation are the major steps. Among the strategies to lower processing costs are utilizing both glucose and xylose sugars present in biomass for conversion. An approach featuring enzymatic hydrolysis and fermentation steps, identified as separate hydrolysis and fermentation (SHF) was used in this work. Proposed solution is to use “pre-processing” technologies, including the thermal screw press (TSP) and cellulose-organic-solvent based lignocellulose fractionation (COSLIF) pretreatments. Such treatments were conducted on a widely available feedstock such as source separated organic waste (SSO) to liberate all sugars to be used in the fermentation process. Enzymatic hydrolysis was featured with addition of commercial available enzyme, Accellerase 1500, to mediate enzymatic hydrolysis process. On average, the sugar yield from the TSP and COSLIF pretreatments followed by enzymatic hydrolysis was remarkable at 90%. In this work, evaluation of the SSO hydrolysate obtained from COSLIF and enzymatic hydrolysis pretreaments on ethanol yields was compared by fermentation results with two different recombinant strains: Zymomonas mobilis 8b and Saccharomyces cerevisiae DA2416. At 48 hours of fermentation, ethanol yield was equivalent to 0.48g of ethanol produced per gram of SSO biomass by Z.mobilis 8b and 0.50g of ethanol produced per gram of SSO biomass by S. cerevisiae DA2416. This study provides important insights for investigation of the source-separated organic (SSO) waste on ethanol production by different strains and becomes a useful tool to facilitate future process optimization for pilot scale facilities.
Establishing Optimal Dehydration Process Parameters for Papaya By EmployingA ...IJERA Editor
This study employs a Firefly Algorithm (FA) to determine the optimal osmotic dehydration parameters for papaya. The functional form of the osmotic dehydration model is established via a standard response surface technique. The format of the resulting optimization model to be solved is a non-linear goal programming problem. While various alternate solution approaches are possible, an FA-driven procedure is employed. For optimization purposes, it has been demonstrated that the FA is more computationally efficient than other such commonly-used metaheuristics as genetic algorithms, simulated annealing, and enhanced particle swarm optimization. Hence, the FA approach is a very computationally efficient procedure. It can be shown that the resulting solution determined for the osmotic process parameters is superior to those from all previous approaches.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
Removal of phosphate ion from water using chemically modified biomass of suga...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
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1. 145
American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
ISSN (Print) 2313-4410, ISSN (Online) 2313-4402
http://asrjetsjournal.org/
Dynamic Evolution of Bitterness Units in Beer Worts:
Modeling and Concerns
Ivi do Nascimentoa
, Lucas Caladob
, Ninoska Bojorgec
, Vinícius Silvad
,
Fernando Peixotoe*
a,c
Departamento de Engenharia Química e de Petróleo. Universidade Federal Fluminense. Rua Passo da
Pátria, 156 – 24210-240 - Niterói – RJ – Brazil
b
Escola de Química, Universidade Federal do Rio de Janeiro, Cidade Universitária, CEP: 21949-900, Rio de
Janeiro, RJ, Brazil
d
Departamento Engenharia de Telecomunicações. Universidade Federal Fluminense. Rua Passo da Pátria, 156
– 24210-240 - Niterói – RJ – Brazil
e
Seção de Engenharia Química do Instituto Militar de Engenharia. Praça Gen. Tibúrcio, 80 - Urca, Rio de
Janeiro - RJ, 22290-270 – Brazil
a
Email: ivicnascimento@gmail.com, b
Email: Lucascalado1991@hotmail.com, c
Email: nbojorge@id.uff.br,
d
Email: viniciusnhs@id.uff.br, e
Email: fpeixoto@ime.eb.br
Abstract
Beer bitterness is reported in International Bitterness Units (IBU), which is the concentration (in ppm) of iso-
alpha-acids in the product. Such acids result from the isomerization of alpha-acids originally found in hops
carried out by boiling the beer wort. This concentration can be measured by HPLC, which led, in the past, to
some empirical predictions for the effects of boil time and wort density on the dynamic evolution of IBU in
barley worts. Since HPLC is an expensive and time onerous procedure, the organizations devoted to the
standardization of procedures in the brewing industry established protocols that indirectly evaluate the IBU by
spectrophotometry. This work investigated the dynamic evolution of IBU values as a function of the wort
density when evaluated by this standard procedure, which involves the extraction with iso-octane and the UV
absorbance measurement in a spectrophotometer.
------------------------------------------------------------------------
* Corresponding author.
2. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
146
Three barley worts were prepared with dry malt extract (DME) having specific gravities (SG) of 1.030, 1.040
and 1.050. These worts and a fourth sample constituted of distilled water (SG = 1.000) had their pH buffered in
5.2 and were boiled under atmospheric pressure. During the boiling step, Czech Saaz hop pellets were added
according to a time schedule (1, 3, 5, 10, 25, 40 and 60 minutes). All reactions were conducted in small (500 ml)
aluminum reactors with triplicates for each isomerization time. Finally, the IBU samples were measured and
used to fit non-linear empirical models for the IBU evolution in the wort, based on a maximum likelihood
statistical criterion and deterministic optimization methods. Results indicated that IBU values, evaluated by the
standard procedure, rise to their final value within a few minutes instead of one hour, which is traditionally
assumed.
Keywords: iso-alpha acids; spectrophotometer; IBU; mathematical model; beer; bitterness.
1. Introduction
Hops are natural preservatives of beer, contributing with flavor, aroma and bitterness, the last one being
produced by the isomerization of alpha-acids inherent to this cannabinaceous flower. Such features are due to
the presence of special chemical compounds in the lupulin glands of the hop flower, known as cones [1]. Some
of these substances are the α-acids, which are directly related to the bitterness of the final product [2, 3]. There
are three types of α-acids relevant for the characteristics of beer, name, humulone, cohumulone and adhumulone
[4]. Although other constituents of beer contribute to the final bitterness, it is recognized that the main substance
responsible for bitterness is the iso-α-acids derived from hops [5, 6, 7, 8]. Each isomer has two stereoisomers:
cis-isohumulone and trans-isohumulone; cis-isocohumulone and trans-isocohumulone; and cis-adhumulone and
trans-adhumulone. The main reason that iso-α-acids are more important for beer production, when it comes to
bitterness and chemical and bacterial stability is that they are soluble in water and carry their properties to the
final product non-isomerized counterparts. For the production of iso-α-acids, the α-acids must undergo an
isomerization reaction at high temperatures [9]. The knowledge about such isomerization reaction is crucial to
the quality control of the beverage. The observed efficiency of this reaction under different conditions does not
exceed the range of 50-60% of conversion. In addition to the efficiency of the isomerization reaction, a term
widely used in the brewing industry to refer to the bitterness of the beer is Utilization. The term is defined as the
ratio between the amount of iso-α-acid found in the finished beer and the amount of α-acids initially added to
the wort. Due to the loss of iso-α-acids in the post-boil, the utilization in the final product is even lower than the
efficiency, falling in a range of 30-40% [3, 10]. The low yield of the isomerization reaction is affected by many
factors, such as the non-solubility of alpha-acids in water [11], the pH of the wort [12, 13], mass transfer effects
related to the technology used for hops pelletization [14, 15], the wort density, the boiling temperature [2, 9], the
boiling time [16, 17], and beer storage [18, 19, 8]. Some studies have shown satisfactory results assuming a
first-order kinetics model with Arrhenius behavior for the isomerization reaction [9]. The concentration of iso-α-
acids in ppm is reported as International Bitterness Units (IBU), and several methods for its determination can
be found in the literature, such as High-Performance Liquid Chromatography - HPLC [9, 20], ion-exchange
chromatography [21], liquid chromatography with detection of ultraviolet absorbance or with mass spectroscopy
[22, 23], electronic tongues [24] and even a low-cost method based on fluorescence [25]. However, the most
used methodology for determining the bitterness beer in the industry is recommended by the American Society
3. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
147
of Brewing Chemists (ASBC) and the European Brewing Community (EBC). It involves extracting the iso-α-
acids with iso-octane, measuring the extract absorbance at 275 nm, using a spectrophotometer, and multiplying
the result by 50, which falls within the IBU scale [26, 14, 27] and practices as an approximation of perceived
bitterness of the product. Although this routine is readily available to the brewing industry, it may have to be
performed for every batch to ensure the quality of the final product, which naturally involves costs and time to
perform the analysis. If one contemplates the development of a new product, several bitterness analyses may be
necessary. In this scenario, reliable correlations for estimating IBU in beers are an interesting alternative.
Although such correlations may not entirely replace an experimental IBU characterization, it can complement
these, especially when designing new products. In this scenario, the present work is devoted to analyzing the
IBU of beer wort during a typical boil stage, focusing on the variation with boil time and wort density and
consequently presenting a correlation for predicting wort bitterness values. The standard method, proposed by
ASBC and EBC was chosen, and the results (as well as the fitted model) showed that even the IBU final values
are achieved much faster than what is assumed in the brewing industry.
2. Introduction
2.1. Isomerization
Each wort was prepared with dechlorinated water and enough commercial dry malt extract (DME) to the
predefined specific gravity (SG) values of 1.030, 1.040 and 1.050, common in the beer industry. Heat was
applied in bulk to ensure maximum solubilization, and the mixture was cooled to room temperature so that the
spare malt would settle and separate from the wort. The supernatant was siphoned to another recipient and
received a commercial buffer used in beer industry (pH Stablizer) to lock the pH in 5.2 (the usual value). The
specific gravity was then determined from measurements made on a Brix refractometer, and the pH was
measured with a digital pHmeter. Each wort was then distributed into custom-made 500 ml aluminum mini
reactors (a total of 21) equipped with special loose-fit lids to guarantee that the inner pressure was maintained at
atmospheric levels and that evaporation losses were minimized. The aluminum reactors were all placed in a
heating bath simultaneously, and the internal temperature of each reactor was measured manually. As the
temperature of all reactors reached 100ºC, time counting started. On predefined times (1, 3, 5, 10, 25, 40 and 60
minutes), three 500 mg hop loads were placed in sets of three reactors simultaneously to obtain a triplicate
sample for each reaction time. Before being added to the respective reactors, the entire hop load was
homogenized and crushed in a porcelain mortar. The hop varietal used was the Czech Saaz, from a 2018 harvest,
with an α-acid content of 2.9 % (cohumulone 23-26 %). At the end of the experimental run, all reactors were
simultaneously cooled by immersion in an ice bath, once α-acid isomerization is reported to be irrelevant under
85 °C [2]. Samples of 20 ml were drawn from each reactor, transferred to 50 ml amber glass flasks to avoid
degradation [19, 17, 28] and stored at 5 °C for 15 days to precipitate oxidated forms of α-acid from the solution
as these could interfere in the measurements. Luckily, such molecules are way less soluble than the iso-α-acids,
especially at low temperatures. The complete procedure was also conducted using pure water (with no DME) for
an additional set of experimental points, corresponding to SG = 1.000.
2.2. Extraction and absorbance measurements
4. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
148
The extraction and UV absorbance measurement procedures were based on the recommendations by ASBC and
EBC and are detailed by Calado and co-workers [25]. All reagents (iso-octane, 1-octanol and hydrochloric acid)
were purchased from Sigma-Aldrich (Sigma-Aldrich Brasil Ltda, São Paulo, Brazil) as P. A. standards, the
mechanical shaker was a Wrist Action Shaker Model 75 (Burrell Scientific, Pittisburgh, U.S.A.) and the
spectrophotometer was a Bel Spectro S-2000 (Bel Photonics, Milan, Italy). Iso-octane quality was assured by
checking whether its absorbance was less than 0.010 at 275 nm [26]. We want to highlight three important
aspects of the experiment: the need for an in-depth cleansing of the cuvettes between readings, once some iso-α-
acids are easily adsorbed on the cuvette walls [29]; the fact that polymer recipients must be avoided in all steps,
once they can interfere in the readings [28]; and the fact that some increase in temperature is expected under UV
light, increasing the IBU, which can be avoided by expedite readings [29]. The interference of polypropylene is
particularly acute, and its effect was analyzed in a previous work [28], in which an anomalous increase of the
IBU was observed in samples that were prepared in tubes made with this polymer in opposition to what was
observed in glass tubes. This indicates that, even with small contact times, organic solvents can chemically
attack polypropylene tubes and interfere in the final bitterness measurements.
2.3. Modeling and parameter estimation
The experimental data, obtained by varying the density (i.e., specific gravity) and the isomerization (boiling)
time as described in section 2.1, was used to fit a non-linear predictive model, described by
𝐼𝐵𝑈 = 𝑈 (1 − 𝑒𝑥𝑝(𝛽 𝑡))
𝛼 𝑚
𝑉
(1)
where α stands for the percentage of α-acids in hops, informed by the supplier or measured according to specific
procedures [30], m is the mass of hops, V is the wort volume, and t is the boil time. The parameter U is an
effective utilization factor, which can depend on a variety of factors. It represents the actual fraction of α-acids
that will contribute to bitterness in the finished beer. For obtaining the commonly used IBU values, the mass of
hops should be entered in milligrams and the wort volume in liters. Equation (1) represents a simple form for
calculating the IBU using a simple isomerization model where there is the production of iso-α-acids from α-
acids, however, with no degradation of iso-α-acids, which is suitable for the boil times and pressures used in this
study. It must be said that, even though this form is commonly used in the brewing industry, no systematic (and
scientific) study was conducted and reported in the prevailing literature.
Model parameters were fitted using a Maximum Likelihood criterion, as (carefully) described by Kappel and co-
workers [31]. In the present work, the optimization was conducted using a direct Nelder-Mead algorithm [32]
and its coherent unbiased estimator gives the model “fundamental” variance:
𝑆𝑦
2
=
1
𝑛−𝑞
∑ ∑ (𝑦𝑖𝑗 − 𝑦𝑖
^)
2
𝑗
𝑖 (2)
where 𝑦𝑖𝑗 is the experimental (measured) IBU value for the j-th replica of the i-th coordinate (given by the
ordered pair {SG, boiling time}), 𝑦𝑖
^ is the fitted model prediction for this coordinate, n stands for the total
number of experimental points and q is the number of model parameters. The parameter variances and
5. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
149
covariances matrix is given by:
𝐶𝑂𝑉 (𝑝) = 𝑆𝑦
2
(𝐽𝑡
. 𝐽)
−1
(3)
where 𝐽 is the Jacobian matrix of the fitted model.
In order to characterize the bitterness variation with wort density, the utilization factor is written in terms of the
wort density, using four different forms:
𝑈 = 𝑈0 𝑒𝑥𝑝[𝑎 (1 − 𝑆𝐺) ] (4)
𝑈 = 𝑈0 [1 + 𝑎 (1 − 𝑆𝐺)3
] (5)
𝑈 = 𝑈0 𝑐 [1 +
2
𝜋
𝑡𝑎𝑛−1(𝑎(1 − 𝑆𝐺) + 𝑏)] (6)
Where 𝑐 = [1 +
2
𝜋
𝑡𝑎𝑛−1(𝑏)]
−1
and 𝑈0 is the value of U for SG = 1. The parameters a and b are determined
using the same curve fitting procedure used for Equation (1).
3. Results and Discussion
Table 1 summarizes the resulting IBU values obtained with the experimental procedure described in Section 2.2,
for worts with different SG values for different boil times (t). As can be seen, the IBU values generally increase
with boil time and decrease for higher SG values.
Fitting the experimentally obtained IBU data to the form described in Equation (1) leads to the parameter values
described in Table 2. These results, along with the experimental data (points), are plotted in Figure 1. As one
can observe from these results, the utilization factor U has a prominent variation, dropping around 50 %, from
the pure water samples to the 1.050 SG samples.
6. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
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Table 1: IBU of samples with different specific gravity (SG) values for different boil times.
SG t (min) Triplicate 1 Triplicate 2 Triplicate 3
1.000 1 12.90 14.15 15.50
1.000 3 13.45 15.60 13.85
1.000 5 19.40 15.90 16.70
1.000 10 14.45 14.65 15.35
1.000 40 15.35 15.60 16.40
1.000 60 18.15 17.60 15.65
1.030 1 10.65 11.20 12.75
1.030 5 12.65 14.20 12.60
1.030 10 14.45 15.00 14.80
1.030 25 15.00 14.00 14.10
1.030 40 13.25 15.25 14.35
1.030 60 14.50 14.80 14.70
1.040 1 10.20 10.15 10.2
1.040 3 9.75 11.15 10.00
1.040 5 12.00 10.80 11.40
1.040 10 13.40 12.90 13.00
1.040 25 13.00 14.25 13.05
1.040 40 12.55 13.15 14.10
1.040 60 11.60 13.00 11.40
1.050 1 5.95 5.75 6.30
1.050 3 6.10 5.95 6.00
1.050 5 6.15 6.05 6.40
1.050 10 6.70 6.45 6.95
1.050 25 7.00 6.70 6.25
1.050 40 6.75 7.60 7.95
1.050 60 6.30 7.35 6.20
Table 2: Calculated model parameters for Equation (1).
SG U Error (U) 𝛽 Error (𝛽) Variance
1.000 0.547870 2.55% 2.19938 25.2% 2.43696
1.030 0.491217 1.56% 1.65609 11.7% 0.735158
1.040 0.423962 2.37% 1.66684 19.3% 1.48075
1.050 0.227798 1.98% 2.36308 23.3% 0.306252
When looking at the time decay parameter (β), the order of magnitude of the calculated β-values indicates that
the IBU reaches its final (i.e., maximum) values a few minutes after the beginning of the boil. This is different
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from the traditionally expected behavior for bitterness in the finished beer, where smaller time decay parameters
are seen. The maximum IBU values are generally obtained around an hour of boil time. These differences may
be attributed to the fact that characterization followed the procedure established by ASBC and EBC for product
quality control, which is different than the employed, for instance, by Malowicki and Shellhammer [10].
Figure 1: Evolution of IBU with boil time: experimental data and fitted models
These authors measured the isomerized alpha acid content by HPLC, which is a much more expensive and
complex procedure, almost prohibitive for most breweries. This may indicate that the alpha acids that have not
been fully isomerized (which would be absent in the finished beer) may contribute to the bitterness readings
when adopting the standard IBU characterization procedure. Regardless of the time decay results, the IBU
reduction for increasing wort densities is by the expected behavior. In this sense, the subsequent results are
intended to quantify the IBU variation with wort density. Table 3 presents the calculated values for parameters a
and b for the models for describing the IBU variation with wort density. In contrast, Figure 2 presents the actual
curves for these models along with the experimental data. The points in this figure correspond to the U-values
obtained in the previous analysis (Table 2), normalized with the value for SG = 1.000.
Table 3: Calculated model parameters for Equations (4) to (6).
Model a Error (a) b Error (b) Variance
Eq.(4) 9.79727 41.37% - - 0.035636
Eq.(5) 4415.29 7.49% - - 0.00223793
Eq.(6) 120.54 4.23% 5.68122 4.31% 0.0000658787
As seen from these results, the exponential model given by Equation (4), gives decreasing U-values with an
asymptote with U = 0 for large SG values; however, it does not capture the U-values obtained from the previous
analysis with precision. Equation (5), on the other hand, gives a better fit to the U points; nevertheless, it yields
negative values for SG > 1.060, which is physically unsound. Finally, Equation (6), gives a fit that accurately
8. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2021) Volume 80, No 1, pp 145-155
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represents the U points but has an asymptote going to zero for larger SG values. Finally, a curve fit of the entire
data set in the form obtained by the substitution of Equation (6) in Equation (1) is performed. The results are
presented in Table 4 and Figure 3. The estimated variance for this fit is 1.19387.
Figure 2: Variation of the utility factor U with wort density for different models
Table 4: Calculated model parameters for the model given by Equations (1) and (6).
Parameter a Error
a 116.905 9.76%
b 5.50862 10.32%
𝛽 1.87721 9.23%
𝑈0 0.547697 1.54%
Figure 3: Variation of IBU with wort density for the model given by Equations (6) and (1), along with
experimental points.
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The low estimated variance indicates that the model presents an adequate adherence to experimental data.
4. Conclusions
The apparatus developed was adequate to conduct the isomerization of the hops alfa-acids controlled, which
allowed studying its kinetics. As expected, the IBU bitterness value increased during boiling and decreased
when the sugar concentration increased. Nevertheless, the IBU increase with boil time was faster than the
usually expected behavior, which indicates that the traditional approach employed can be subject to the
interference of other UV absorbing substances in the early stages of the boiling. This can be a particular concern
when using late hopping techniques, whirlpool hop additions and/or dry hopping when the hop is added during
fermentation and/or maturation. Another limitation of the model is related to the fact that IBU readings were
performed in unfermented worts, which misses the loss of bitterness substances during fermentation that can
lower IBU values. Besides, the model doesn’t take into account the boiling temperature and pH, which interfere
in the IBU evolution and can be subject to further studies. Regardless of the behavior, the IBU values stabilized
for larger boil times, leading to different quantities for different wort densities. These values were fitted to
different models, and the best fit was in the form of an inverse tangent function. This empirical model can
estimate the effect of wort density in the IBU, which is of assistance for process simulation and product design.
Acknowledgments
The authors would like to acknowledge the financial support provided by the Brazilian funding agencies CNPq,
CAPES (Finance Code 001) and FAPERJ and Programa de Desenvolvimento de Projetos Aplicados (PMN-
FEC).
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