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CHEMICAL ENGINEERING TRANSACTIONS
VOL. 65, 2018
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Eliseo Ranzi, Mario Costa
Copyright © 2018, AIDIC Servizi S.r.l.
ISBN 978-88-95608- 62-4; ISSN 2283-9216
Evaluation of the Production of Starch from Bitter Cassava
(Manihot utilissima) using Different Methodologies
Luis F. Lopez-Diagoa, Kevin Castilloa, M.V. Vidala, Jorgelina Pasqualinob, Pedro
Meza-Castellara, Henry A. Lambis-Mirandaa*
a
Process Engineering program. CIPTEC Research Group. Fundacion Universitaria Tecnológico Comfenalco.Cr44D N 30A,
91, Cartagena, Bolivar, Colombia
b
Environmental Engineering program. GIA Research Group. Fundacion Universitaria Tecnológico Comfenalco.Cr44D N
30A, 91, Cartagena, Bolivar, Colombia.
hlambis@tecnocomfenalco.edu.co
Cassava is a shrub belonging to euphorbiaceous family, widely cultivated in South America, Africa and the
Pacific because of its roots with starches of high nutritional value. There is a variety called Manihot utilissima
or bitter cassava, which contains high concentrations of cyanogenic elements that make it unusable and
poisonous raw material which avoided for human consumption, while the high concentrations of carbohydrates
place it as a potential source of starch mainly for industrial use. The wet extraction method was used to get
starch from bitter cassava (Manihot utilissima). A factorial (2x3) design was implemented for the experimental
set up, with 2 levels of time and mincer speed and 2 for the temperature of H2O, with 2 independent
replicates. The comparison with the dry extraction method was also studied, this method consists in drying the
bitter cassava over night at 50ºC. The yield of starch obtained in wet method ranged from 17.2 to 39.4 g of
starch (mean of 26.6 g) obtained from the original samples of 250 g of wet bitter cassava, yielding yields
ranging from 6.88 to 15.76% (average 10.64%) of the dry mass. The results were then analysed using PAST
software v3.16 for ANOVA statistical evaluation, trend and Pareto were used to determine optimal conditions
for the extraction of starch by wet and dry methods. Once the assumptions for analysis of variance (ANOVA)
were made, it was concluded that a higher yield of starch is obtained from lower speeds and time, whereas
the temperature of H2O is not significant for the process, giving as optimal value for wet method = 0.0678762
for 1/x of the yield of starch obtained, with a starch purity of 64.90% ± 1.21% ranging up to 85.37% ± 1.42%.
The extraction of bitter cassava starch has demonstrated its potential for the use of this variety of cassava
which generates an added value to the product.
1. Introduction
Cassava is a shrub belonging to euphorbiaceous family, widely cultivated in South America, Africa and the
Pacific because of its roots with starches of high nutritional value. It is considered a functional component of
food due to the health benefits it confers following its consumption (Ogbo and Okafor, 2015). There is a
cassava variety called Manihot utilissima or bitter cassava, which contains high concentrations of cyanogenic
elements that make it poisonous and thus unusable as raw material for human consumption. For this reason,
bitter cassava cultivars have been employed mainly as an emergency famine food (Tumwesigye et al., 2017).
However, its high carbohydrates concentrations place it as a potential source of starch mainly for industrial
use. Starch obtained from cassava and bitter cassava, has numerous applications in the paper, textile,
pharmaceutical (as excipient), adhesives, food (as thickener), water treatment (as coagulant), and polymer
industries (Hernandez-Carmona et al., 2017).
Starch and chemically modified starch based films have drawn considerable attention on food packaging
owing to their attractive combination of price, environmental friendliness, and abundance (Owi et al., 2017).
Natural biodegradable polymers can be obtained directly from starch rich agricultural product (like corn,
potato, wheat, cassava, barley, and rice) and wastes, using different processes such as: extraction and
plastification of agricultural materials rich in cellulose and starch; microbial production; chemical synthesis of
613
DOI: 10.3303/CET1865103
Please cite this article as: Lopez-Diago L., Castillo K., Vidal Mejia M.V., Pasqualino J., Meza-Catellar P., Lambis H., 2018, Evaluation of the
production of starch from bitter cassava (manihot utilissima) using different methodologies., Chemical Engineering Transactions, 65, 613-618
DOI: 10.3303/CET1865103
source monomers; and chemical synthesis of synthetic monomers (Hernandez-Carmona et al., 2017). Starch
from cassava has been used to obtain biopolymeric materials such as bio-derived films and food packaging
film (Tumwesigye et al., 2017), green nanocomposites (Owi et al., 2017), thermoplastic starch blends with
other biodegradable polymers (Fidelis et al., 2017), and starch/polyurethane dispersion blends for surface
sizing agents (Rusman et al., 2017), among other applications. In this paper, we evaluate the experimental
conditions (temperature, time and mincer speed) that increase the starch production from bitter cassava using
the wet extraction method.
2. Methodology
2.1 Sample collection and preparation
The bitter cassava or industrial cassava (Manihot utilissima) was collected in the rural area of San Jacinto
town, department of Bolívar (North Coast of Colombia). For this research approximately 12 kg of cassava
were collected and only the ones that did not present malformations or physical damages were used.
2.2 Crude starch extraction procedure
The starch extraction was carried out by two methodologies, called "dry" and "wet" with the objective of
comparing their performance (Hernández-Carmona et al., 2017). The wet process (Figure 1) includes the
following stages: root reception, washing, chopping and crushing, extraction, sedimentation, and drying:
• Roots reception: bitter cassava was collected and transported immediately after harvest in order to avoid
physiological and/or microbial deterioration.
• Washing: the dust and dirt were removed from the surface with water; the cassava was then dried on
adsorbent paper.
• Chopping and crushing: the husks and roots were removed, the pulps (about 250 g) were manually minced
at an average length of 3 cm, and then crushed with 250 ml water at different temperatures (25 and 40°C).
• Extraction: the excess fibre or bagasse was separated with a sieve from the liquid phase, which contains the
starch.
• Decanting: the liquid phase was left to rest, and the starch was separated by density differences with the
water in a decanter. Decanting time varied from 6 to 8 hours at room temperature (25ºC). The starch phase
was then vacuum filtrated with filter paper (Whatman®) to remove the excess water for about 20 min until a
semi-solid tablet was partially observed.
• Drying: the starch was dried in a conventional laboratory oven at 40°C for 8 hours.
Figure 1: Scheme of the bitter cassava starch extraction wet and dry procedures.
2.3 Experimental Design
The starch yield (w/w%) from bitter cassava was selected as the dependent variable while the crushing
velocity (crushing machine), crushing time and temperature of the experiment were selected as the
independent ones. A balanced 2
3 factorial design (Table 1) was used for experimental planning process, with
2 crushing levels (low and high speed), 2 crushing time levels (2 and 6 min), and 3 independent replications
taken at each of the 3×2 treatment combinations. The design size was N=2×4×3=24 (Montgomery, 2009).
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The results were later analysed with PAST v3.14 and STATGRAPHICS CENTURION XVI software (Hammer
et al., 2001; Reyes et al., 2013) for statistical evaluation. A multifactor-way ANOVA test was performed to
evaluate whether the crushing velocity, crushing time and the temperature affect the starch yield and if there
are interactions between them.
Table 1: 23 Factorial design.
Factor Experimental factor Levels Coded variable
A Crushing velocity Low / High - / +
B Crushing time 2 min / 6 min - / +
C Temperature 25ºC / 40ºC - / +
2.4 Product characterization
2.4.1 Determination of starch purity and amylose/amylopectin ratio
The Lane-Eynon volumetric method was used, based on the determination of the volume required to
completely reduce a known volume of alkaline copper reagent. Methylene blue indicator was used to
determine the final point (Storz and Steffens, 2004; Blanco et al., 2000).
2.4.2 Iodine test
The Lugol solution was prepared with 5 g of I2 and 10 g of KI diluted with 100 mL distilled water, giving a
brown solution with total iodine concentration of 150 mg/mL.
2.4.3 Colour determination
The cassava starch samples were compared with a standard starch forming rectangles (2.5-5.0 cm length and
1.6-3.5 cm height) with a spatula on a sheet of white paper, pressing the samples with a clean and fine paper
to equalize the upper surface, and comparing the colour (Grace, 1977).
2.4.4 Apparent density
The starch samples were added with a spatula into a 250 ml graduated cylinder previously dried and weighed,
until the total volume was freely completed, and then weighed again in order to calculate the density as the
relationship between the sample mass an volume.
2.2.5 Gelatinization temperature
A starch suspension (10 g/100 mL) was prepared in cold water and doubled boiled at 85ºC, measuring the
temperature until paste formation (Grace, 1977).
2.2.6 pH
A suspension was prepared with 20 g starch and 100 mL previously boiled distilled water. After 15 minutes the
mixture was filtered through a Whatman® filter paper and the pH was measured to the liquid phase with a
HANNA pH-meter.
2.2.7 Ashes
Ashes content was measured by incineration at 550°C during 3.5 hours.
3. Results
3.1 Starch yield from the wet extraction method
Considering the experimental design (Table 1) 24 starch samples were obtained. The crude starch yield is
presented in Table 2, ranging from 6.88% to 15.76%.
3.2 Statistical summary about data distribution
The descriptive statistical analysis and the variance analysis (multifactorial ANOVA) were implemented to the
production of bitter cassava starch with the statistical software package PAST v3.16 to evaluate if the
variables affect the starch yield and if there are interactions between them. Additional tests were carried out to
verify the assumptions of data independence, data normality and homoscedasticity (Hammer et al., 2001;
Montgomery, 2009).
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Table 2: Summary of performance in wet methodology
Crushing speed Crushing time (min) Temperature (ºC) Crude starch yield (w/w%)
Block 1 Block 2 Block 3 Mean
Low 2 25 9.84 13.88 15.12 12.95
Low 2 40 8.36 13.04 14.76 12.05
Low 6 25 10.4 11.76 10.8 10.99
Low 6 40 8.56 7.96 14.12 10.21
High 2 25 13.0 6.96 7.88 9.28
High 2 40 15.28 9.64 8.44 11.12
High 6 25 9.4 7.4 6.88 7.89
High 6 40 15.76 8.92 7.16 10.61
Figure 2: Statistical results summary.
A summary of the statistical results is presented in Figure 2, where it can be seen that the values of
standardized bias and standardized kurtosis are within the range -2 to +2, which indicates that the data have a
normal distribution. The value of Shapiro-Wilk must be within the acceptance zone (ZA) for the null hypothesis
(H0), which must be formed by all the values of the test statistic Wexp which are lower than the expected or
tabulated value W(1-α;n). ZA = Wexp < W(1-α; n). Since the value of Wexp=0.9026 is lower than the expected value
W(0.95; 24)=0.916, the null hypothesis is accepted, concluding that there is a 95% confidence that the starch
yield variable is not normally distributed. This could also be confirmed with the p(normal) value (p-
value=0.02448) which is lower than the level of significance (α=0.05), confirming that the distribution is not
normal. Therefore, it is not possible to use tests that consider standard deviations until it is stabilized, which is
done by transforming the dependent variable with either its neperian logarithm, base 10 logarithm, its inverse
or its square root (Table 3).
Table 3: Correlation coefficients of transformations in order to normalize the data
Coefficient Value
Normal 0.9613
Ln X 0.9725
Log10 X 0.9721
1/X 0.9753 √X 0.9675
Analysing the results obtained with the Shapiro-Wilk test, histogram and normal probability graph (Figure 2), it
is necessary to stabilize (transform) the data resulting from the starch yield, which was done by applying the
inverse of each of the data obtained in order to approximate it as much as possible to a normal distribution.
This decision was taken according to the results from Table 3, once transformed.
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Figure 3: Statistical summary of the data after (1/X) transformation.
In the analysis of variance (ANOVA) for the response variable (Figure 3), 1/X yield of the starch obtained,
each variable is analysed statistically and its effect on the model explaining the variation of each of them in the
response. As shown in Table 4 there are, in this case, two effects that have a p-value of less than 0.05. This
indicates that they are significantly different from zero with a confidence level of 95%, so they have a high
impact on starch yield, while the other variables and/or their combinations do not have a significant effect on
the response variable.
Table 4: Multifactor ANOVA test for starch yield from bitter cassava
Variable
Source
Square
Summation
Degrees of
freedom
Medium
Squares
F p-Values
A 0.010412 1 0.010412 45.36 0.0000
B 0.001473 1 0.001473 6.41 0.0222
C 0.000528 1 0.000528 2.30 0.1488
AB 0.000370 1 0.000370 1.61 0.2225
AC 0.000498 1 0.000498 2.17 0.1601
BC 0.000021 1 0.000021 0.09 0.7653
ABC 0.000076 1 0.000076 0.33 0.5721
Error 0.003673 16 0.000230 - -
TOTAL 0.017052 23 - - -
Since the p-value of these is less than α, it can be said that the differences between some of the means are
statistically significant, so the null hypothesis is rejected and it is concluded that not all the population means
are equal. The statistical value r2 of the model, thus adjusted, gives a value of 78.46% of the variability in
performance. This is adjusted in a great way since it is higher than the 69.03% which is the most suitable in
models with different number of independent variables.
Equation 1 shows the optimal model of the variable (1/X):
1 0,0137996 0,0638877 0,0135389 0,00129068 0,00582842 0,00108332 0,000175280,000118942
3.3 Product characterization
Table 5: Properties comparisons
Properties Bitter cassava Cassava starch
Density (g/mL) 0.572 1.560
pH 4.42 4.5 - 5.5
Gelatinization
temperature (ºC)
70 57.5 - 70
% Amount of ashes 0.13 -
The amount of amylose obtained varied between 66-69% while amylopectin was found between 30-33%. The
starch obtained from bitter cassava contains high values in the content of amylose which favours a greater
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solubility, higher viscosity, better clarity of the paste and greater tendency to the retrogradation of the gels.
The starch sample produced an intense blue colour corresponding to the starch according to the Iodine test. In
addition, by the qualitative colour determination, neutral coloration (white) also corresponds to starch. The only
significant difference, when compared to standard starch, is that the standard starch is much finer than the
starch of bitter cassava. The product characterizacion is shown in Table 5, the density and the pH results are
minimal lower in relation with cassava starch, the low density can be explained with the regular compaction of
the starch.
4. Conclusions
Starch extraction from bitter cassava (Manihot utilissima) was carried by wet extraction method. The optimal
conditions were evaluated using 23 factorial design. The starch yield was selected as the dependent variable
while the crushing speed, crushing time and the temperature were selected as the independent ones. The
starch yield was varied between 6.88% and 15.76%. ANOVA analysis results shown the variables with the
greatest influence on the starch yield are the crushing speed (low velocity) and crushing time (2 min). The
characterization tests show that, when compared to standard cassava starch, the starch obtained from bitter
cassava has similarity in colour, apparent density, gelatinization temperature and pH. The high content of
amylose present in the extracted starch can favour properties such as solubility, viscosity, paste and
retrogradation of the final biopolymer.
Acknowledgments
This work was funded by the Fundación Universitaria Tecnológico Comfenalco – Cartagena (Resolution #318
from October 28th 2014), as part of the project “Production and characterization of vegetable biopolymers
based on banana and plantain peel wastes”.
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