Optimization of Processing Conditions for Wine Production from
Acerola (Malpighia glabra L.)
S.S. Almeida, N. Narain and R.R. Souzaa J.C.C. Santana
Department of Chemical Engineering
Federal University of Sergipe
University Campus “José Aloísio de Campos”
Av. Marechal Rondon, S/N, Rosa Elze
49100-000, São Cristóvão, Sergipe
School of Chemical Engineering
State University of Campinas
University Campus “Zeferino Vaz”
Av. Albert Einstein, 500
6066 Barão Geraldo
13083-970 Campinas, São Paulo
Keywords: sensorial analysis, RSM, color, flavor, aroma, optimization
In the present work, an attempt has been made to standardize the processing
conditions for the manufacture of wine from acerola (Malpighia glabra L.) by RSM
optimization. A central point design was used to evaluate the effect of soluble solids
(°Brix) and the concentration of fruit pulp on sensorial quality attributes (color,
flavor and aroma) of wine which were measured on hedonic scale. Saccharomyces
cerevisiae yeast was used for fermentation. Acerola wines were found to be suave,
sweet and 11°GL of alcohol concentration. Flavor and color of wines were
characteristic of acerola fresh fruit. Sensorial analysis revealed that these were
different among wines and optimization showed that wines produced with high
°Brix and low fruit mass were the best products. This work supports the usage of
acerola for obtaining high quality wines which possess pleasing aroma and shiny red
The acerola (Malpighia glabra L.) fruit, as with other minor non-conventional
fruit plants, leaves doubt on its origin. It was introduced in Brazil about 50 years back, in
the state of São Paulo, brought from Puerto Rico (Dinizi et al., 2003). The fruit is known
for its very high ascorbic acid (vitamin C) content. About 100 g of juice possesses 50 to
100 times more of this vitamin than that of an equal quantity of lemon or orange juice
(Gomes et al., 2002). Other vitamins of relevant importance for health and human food
purposes such as A, B1 and B2 also favor the consumption of this fruit. The daily
consumption of 2 to 4 acerolas is sufficient to meet the normal necessities of the human
being. The acerola is also important from social and economic aspects as it offers to the
poor population as an easy and accessible source of vitamins and mineral salts at low
cost. The wine commercialization undergoes long and traditional trajectories until it
arrives at the table for consumption. However, the product undergoes stabilization
treatments and packaging that transforms it into a quality product although at many times,
turns it to be quite original and personalized. Thus, the wines should get constant
improvements in its characteristics, and these must be perfectly stabilized and submitted
to severe rules which assure product protection against frauds, whereby guarantee the
consumer (Delanoe et al., 1989).
Although the wines better appreciated are made from grapes yet other fruits could
be utilized as raw material for the manufacture of wines. These fruits could be orange,
pineapple, strawberry, acerola, cashew apple and other exotic fruits such as cupuacu
(Freitas et al., 2001; Garrutti, 2001). Generally, the wines made from these fruits result in
flavor and aroma characteristics of the original fruit utilized and if due care is taken,
could last for long time storage.
IS on Trop. and Subtrop. Fruits
Eds.: M. Souza and R. Drew
Acta Hort. 864, ISHS 2010
With this objective in mind, this work was undertaken to obtain a wine of good
and acceptable quality prepared from the usage of acerola fruit, which may consequently
aggregate further values to this fruit culture.
MATERIALS AND METHODS
Preparation of Must
Acerola fruit at stage of maturity were selected, cleaned with chlorine (2 ppm of
active Cl2) water and triturated in mixer, thus obtaining the pulp which was stored in a
refrigerator. For the preparation of must, the pulp quantity of acerola fruits and total
soluble solids (°Brix) content were varied according to the experimental planning design
of 22, presented in Table 2. The inorganic nutrients were added in the concentrations of 1
g/L of NH4H2PO4 and 0.1 g/L of MgSO4. The pH of the medium was later corrected in
the range of 4 to 5 with Na2CO3. Fractions of total volume of these were separated in
different flasks, from the principal vat as being to approximately 4 L, 500 ml and 10 ml,
which were denominated as vessels. These were pasteurized by heating in an autoclave
and cooling rapidly in running water having the sole objective of sterilization of the
medium (Delanoe et al., 1989; Garruti, 2001; Lima et al., 2001).
Inoculation of the Yeast
Saccharomyces cerevisiae yeasts were inoculated in the lowest volume of vessel at
a concentration of 70 to 80 g/L, where it remained between 20–24 h for adaptation of the
medium. It was later transferred to the next vessel and maintained for 48 h, after which it
was transferred to the principal vat, in which it remained for the final days of its
fermentation (Delanoe et al., 1989; Garruti, 2001; Lima et al., 2001).
The procedure was followed based on jar tests. Ten to fifteen ml volume of wine
sample was distributed in 5 (or more) tubes, and in these, a volume varying from 0.1 to
2.0 ml of bentonit clay at 1% solution was added. The mixture was homogenized and left
over for 24 h. It was observed that the flocculent material decanted in the tubes and the
one which presented the minimum turbudity in the supernatent was considered to be the
best (wine volume/clay mass volume) for wine clarification. After the setting of
flocculent material in the principal vat, the liquid continued to decant. Later filtration for
complete separation of the two phases (liquid and solid) was achieved, resulting in a clear
wine (Delanoe et al., 1989; Garruti, 2001; Lima et al., 2001).
The wines were packed in amber-colored bottles of 1.0 L capacity which were
sealed with cork. The closed wine bottles were pasteurized by heating in an autoclave at
115°C and 1.5 kg/cm2 for 15 min, cooled later in running water and stored in refrigerator
at 5°C for a period of 6 months for posterior evaluation of its quality (Gava, 1986).
The characteristics determined were: Total acidity by titrating with NaOH solution
0.1 M and volatile acidity according to the method of Casenave-Ferré, reducing sugars by
Fehling method, percent alcohol by distillation and later measurement of density with
alcoholmeter, density measurement by weighing the mass in analytical balance of a
determined volume, dry matter by drying at 100–105°C and pH by potentiometer method
(Ascar, 1985; Delanoe et al., 1989; Garruti, 2001).
A panel of 50 untrained members evaluated the sensorial characteristics such as
flavor, color and aroma of wines. The comparisons were made among the various wines
prepared according to the experimental planning (Table 2). The experimental research on
quantitative basis was undertaken wherein a standard form for sensorial analysis was used
and random sampling was applied for each of the above attributes using a hedonic scale.
The results obtained vide the filled-in forms were tabulated and their mean response are
presented in last 3 columns of the Table 2. Based on frequency of responses, the sensorial
data were compared by T Student test of significance and plotted in Figure 1 (Teixeira et
In order to better evaluate the effect of total solids (°Brix) and fruit pulp mass
(%M) on the wines acceptability in relation to flavor, color and aroma, an experimental
planning of the 22 (square design) was done so as to reduce the number of experiments to
be realized and a better evaluation of results obtained by application of response surface
methodology. Table 2 presents the data on planning of experiments with the normal and
codified variables and their responses to flavor, color and aroma attributes. The tested
models were plane and hiperplane form. The matrix calculations for obtaining the
estimates for the parameters were done by minimum square method. By application of
software Matlab® (version 6.0) and evaluation for adjustment of data for the model was
done by analysis of variance (ANOVA), as described by Barros Neto et al. (2001).
Variables codification: The factors °Brix of must (x1) and acerola fruit mass (x2,
%M), were codified for their values in the form that these were normalized at -1, 0 and 1,
to facilitate the regression calculations, according to the following equations:
x1 = (°Brixi – 24)/2 (1)
x2 = (% Mi – ¼)/(1/12) (2)
RESULTS AND DISCUSSION
The wines obtained possessed clean appearance having the color and aroma
characteristics pertaining acerola fruit, light and sweet flavor, showing that these
characteristics of the fruit were retained to a great extent. Table 1 presents the data
obtained after the analysis of acerola wine samples.
From the data it could be observed that total acidity was within the range
established as Brazilian standard (lower than 130 meq/L) and practically all fermented
samples did not characterize for any undesirable acidity which could be volatile,
indicating presence of acetic acid or its derivatives. Such substances denature wine,
modifying the aroma (pungent) and flavor of the same (bitter).
The reducing sugars content in wines varied from 5–20 g/L, which indicates
relative stability that a small quantity of sugar could reduce or inhibit any perturbation
which may occur in the physico-chemical properties of wines due to microbial action.
The dry matter content also was lower and hence it presented a clear appearance
and low density due to the presence of non-volatile acids, superior alcohols,
carbohydrates, inorganic minerals, tannins, etc.
The wine pH was in the range of 3.1 to 3.9 which is very much desired and it
results in avoiding microbial contaminations or alterations in color, flavor and in oxi-
reduction potential (Cassone, 1995; Delanoe et al., 1989; Garruti, 2001).
The detailed observation for the data in Table 2 shows that majority of wines
presented satisfactory results in their sensorial analysis, being close to 6. Figure 1 presents
in the visual form the presentation of mean values of analysis sensorial of wines. This
shows that in color practically there was no difference between the diluted or more
concentrated wines leading to conclude that the weight of fruit mass did not alter the color
significatively. It was also observed that there was a little diference between the samples
in relation to wine aroma and to a little higher extent to flavor. However, the T Student
test did not present any significant difference between the wines in both these sensorial
attributes since the calculated T varied from 0.02 to 0.36 which is much lower than the T
tabled (2.86), according to Barros Neto et al. (2001) and Teixeira et al. (1987). The value
of T calculated must be at least four times lower than that of T tabled so that there may
not be any significant differences between the samples.
The results obtained from the analysis of variance are presented in Table 3. The
statistical parameters, multiple correlation and F test were utilized to evaluate the
adjustment to models. As is known and from the data presented in table that however
close the unit will be to the value of R2, more adjusted will be the data for the model. The
test F1Calc/F1Tab evaluated the statistical significance of the models, while the test
F2tab/F2calc evaluates the adjustment of data to the model and that it requires their values
should be greater than 4. Thus, it could be stated that the empirical models are statistically
significative and are adjusted. The equations of the models which were better adjusted are
presented as follows:
Color = 6.097 + 0.3618 °Brix - 0.0417 Mass (3)
Aroma = 5.8093 + 0.4930 °Brix - 0.0970 Mass (4)
Flavor = 5.9029 + 1.3218 °Brix - 0.2953 Mass (5)
Observing the Eq. 3, 4 and 5, it is perceived that the dependence for both
responses was linear with the factors and that the influence of °Brix on the sensorial
quality was much more than that of the pulp mass. Figures 2, 3 and 4 demonstrate the
response surfaces generated to optimize the production process of acerola wines under the
conditions studied in this work. A general analysis of these figures shows that with the
increase in initial °Brix of must, the wine obtained characterized better acceptance in all
sensorial attributes studied. It is also perceived that practically there is no inclination in
the curve in relation to the pulp mass, which indicates that its influence decreases the
quality of final product, which was also perceived on comparison of model for the
The wines obtained in this work had color, aroma and flavor characteristics of
acerola and it was classified as suave. Its alcoholic gradation was approximately 11 °GL
and had all other physico-chemical characteristics within the norms specified by Brazilian
The sensorial analysis demonstrated that there was no significant difference
between the various wines manufactured and their mean acceptance was about 6 point. In
hedonic scale. The models which adjusted most to the sensorial data were linear for the
studied factors and the optimization showed that the wines which were produced with the
must of higher °Brix and lower quantity of pulp mass were more acceptable by panel
members. This work demonstrated that it is possible to obtain good and commercially
acceptable, which may serve as another form of aggregating value to the acerola culture.
One of the authors (S.S.A.) thanks to Conselho Nacional de Desenvolvimento
Cientifico e Tecnologico (CNPq), Brazil for awarding a fellowship. The authors also thank
the financial support received from the FundoVerde-Amarelo (convention FINEP-
Ascar, J.M. 1985. Alimentos: Aspectos Bromatológicos e Legais. Análises Percentuais.
V.1. 1st edition. UNISINOS, São Leopoldo, RS, Brazil.
Barros Neto, B., Scarminio, I.S. and Bruns, R.E. 2001. Como Fazer Experimentos:
Pesquisa e Desenvolvimento na Ciência e na Indústria. Vol. 1. 1st edition, Coleção
Livros - Textos, EDUNICAMP, Campinas – SP, Brazil.
Cassone, L. 1995. Conheça o Mundo. do vinho e do queijo. Gaia, São Paulo, Brazil.
Delanoe, D., Maillard, C. and Maisondieu, D. 1989. O vinho da analálise à elaboração.
Col. EUROAGRO. Europa-América Ltda, Porto-Portugal.
Dinizi, E., De Figueiredo, R.M.F. and Queiroz, J.M.Q. 2003. Water activity and electric
conductivity of concentrated of “acerola” pulps. Brazilian J. of Agricultural Product,
Ferrão, J.E.M. 1999. Fruticultura tropical: espécies com frutos comestíveis. Lisboa:
Instituto de Investigação Científica Tropical 1:75–84.
Freitas, R.F., Schwan, R.F., Dias, D.R. and Oliveira, R.L. 2001. Elaboração e
caracterização de vinho de cupuaçu (Theobroma grandiflorum - Will ex.
Spreng:Schum). XXI Congresso Brasileiro de Microbiologia, Foz do Iguaçu-PR,
Anais do XXI Congresso Brasileiro de Microbiologia. Microbiologia dos Alimentos:
Garrutti, D.S. 2001. Comp. de Volát. e qual. de aroma do vinho de caju. Campinas: FEA -
UNICAMP, p.220. (DSc Thesis).
Gava, A.J. 1986. Princípios de tecnologia de alimentos. 7th, Nobel, São Paulo, Brazil.
Gomes, P.M.A, De Figueiredo, R.M.F., Queiroz, A.J.M. 2002. Characterization and
moisture isotherms of adsorption of power acerola pulp. Brazilian J. of Agricultural
Products 4:2, 157–165.
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Processos. Fermentativos. Vol.3. 1ª ed. Ed. Blucher Ltda, São Paulo, Brazil.
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Série Didática. Editora UFSC, Florianópolis, Brazil.
Table 1. Physico-chemical analysis of acerola wine.
Reducing sugars (g/L) 6.670 0.780
Total acidity (meq/ L) 5.798 0.780
Volatile acidity (meq/ L) 0.139 0.121
Density 0.985 0.008
pH 3.0 0.5
Total solids (%) 4.123 0.126
Alcohol content at 20°C (°GL) 11.0 0.5
Table 2. Planning matrix for experimental design for acerola wine.
Factors Coded variables Responses
Assay °Brix % Mass x1 x
2 Color Aroma Flavor
1 22 1/6 -1 -1 5.74 5.428 4.86
2 26 1/6 1 -1 6.46 6.34 7.261
3 22 1/3 -1 1 5.653 5.16 4.027
4 26 1/3 1 1 6.38 6.22 6.913
5 24 1/4 0 0 6.2 5.907 6.324
6 24 1/4 0 0 6.189 5.95 6.176
7 24 1/4 0 0 6.02 5.66 5.759
Table 3. Variance analysis for obtaining the optimum empirical model.
Analysis Color Aroma Flavor Table
F1 Test 34.210 34.175 36.037 6.94
F2 Test 0.524 0.206 1.372 19.00
Multiple correlation R2 0.9454 0.9447 0.9475 1.00
Fig. 1. Sensorial attributes for acerola wine.
Fig. 2. Response surface for wine color optimization.
Fig. 3. Response surface for wine aroma optimization.
Fig. 4. Response surface for wine flavor optimization.