Food Research International 41 (2008) 569–578
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Bread from composite cassava–wheat flour. II: Effect of cassava genotype and nitrogen fertilizer on bread quality T.A. Shittu a,*, A. Dixon b, S.O. Awonorin a, L.O. Sanni a, B. Maziya-Dixon b a b
Department of Food Science and Technology, University of Agriculture, Abeokuta, Nigeria International Institute for Tropical Agriculture, Ibadan, Nigeria
a r t i c l e
i n f o
Article history: Received 30 June 2007 Accepted 3 March 2008
Keywords: Cassava Wheat Composite bread Physical properties Image analysis
a b s t r a c t There is an increasing interest in the use of cassava roots for food and industrial purposes especially in the baking industry in Nigeria. Development of some cassava mosaic disease (CMD) resistant clones and application of inorganic fertilizers are principal strategies targeted in the country to boost and sustain cassava root production and utilization. A study was conducted to determine the effect of cassava genotype and field application of nitrogen fertilizer on some physical properties of bread from composite cassava–wheat (CCW) flour. Five CMD cassava clones were planted in a randomized complete block design with two level of fertilizer treatments (0 and 160 kg nitrogen/ha) with two replications while harvesting was done 12 months after planting. Composite flour was produced at a ratio of 10/90 (cassava/wheat flour, w/w). The oven spring, specific volume, crumb texture (softness) and crumb moisture of loaves ranged from 0.57 to 0.63 cm, 4.37 to 6.85 cm3/g, 18.4 to 29.4 mm and 31.40% to 34.70%, respectively. The crust’s tristimulus color parameters L*, a*, b* and brownness index also ranged from 54 to 67, 9 to 15, 22 to 29, and 57 to 83, respectively. These values differed significantly from each other at p < 0.01. Out of all these loaf properties, crumb texture was the most affected by the main and interactive effects of cassava genotype and fertilizer application (p < 0.001) while loaf weight was only affected by their interactive effects (p < 0.05). Digital image analysis of the bread crumb showed that the total number of cells, number of small cells and total cell area of the bread crumb ranged from about 22 to 27 cm3, 20 to 25 cm3 and 12% to 29%, respectively. The distribution of large cells and total cell area occupied in the crumb were principally determined by the genotypic difference (p < 0.05) in the cassava roots. The main effect of fertilizer application significantly affected the distribution of small cells, total number of cell and the cell area (p < 0.05). However, the interactive effects of genotype and fertilizer application was more significant (p < 0.01) on the crumb cell characteristics. The study indicated that optimal quality of CCW bread loaf could be attained by appropriate selection of cassava genotype and fertilizer application. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Cassava flour (CF) is one of the major products from cassava roots traded in the world food market. The future of Nigeria as one of leading CF exporting countries is particularly bright as its export capacity had risen from about 230 Mt in 1988 to about 10,975 Mt in 2003 (FAO, 2004). Similarly, CF has also continued to find wider applications in the food, feed and chemical industries (Balagopalan, 2002). One of the most popular food uses of CF worldwide is in the manufacture of baked product. The use of CF as partial substitute to wheat flour for baking purposes has currently received the support of the Federal Government of Nigeria, which mandated the flour mills to include a minimum of 10% high
* Corresponding author. Tel.: +234 803 538 8060. E-mail address:
[email protected] (T.A. Shittu). 0963-9969/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodres.2008.03.008
quality cassava flour (HQCF) into wheat flour for making composite flour meant for baking purposes. As a preemptive approach to prevent the spread of cassava mosaic disease (CMD), which has ravaged several parts of Africa, plant breeders at IITA, Ibadan, Nigeria, have now developed some cassava clones resistant to CMD. Previous works have indicated that varietal influence significantly affects physical, chemical and functional characteristics of CF, which could subsequently affect their food applications (Aryee, Oduro, Ellis, & Afuakwa, 2006; Defloor, De Geest, Schellekens, Martens, & Delcour, 1994; Defloor, Leijskens, Bokanga, & Delcour, 1995; Eggleston, Omoaka, & Arowoshegbe, 1993; Olorunda, Aworh, & Numfor, 1981). Similarly, in a very recent study by Shittu, Sanni, Awonorin, Maziya-Dixon, and Dixon (2007) of about 43 clones of these resistant cassava clones, it was concluded that their flour making characteristics of their roots differed significantly while their subsequent food applications could also vary.
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publication). The results indicated significant differences in the flour properties as a result of genotypic difference and fertilizer treatment. However, the impact of such modification of flour properties on prospective food applications is yet to be assessed. This study therefore examined how genotypic differences in cassava roots, field application of nitrogen fertilizer and their interaction could affect breadmaking potential of cassava flour in composite with wheat flour.
Generally, literature reports of studies conducted to relate cassava flour properties to their food uses are presently not common. An example of such study was conducted by Eggleston et al. (1993) who observed that the CF’s diastatic activity and maximum paste viscosity were found to influence the specific volume of gluten free bread loaf made from soy-cassava flour. Also, Hudson and Ogunsua (1976) concluded that degree of starch damage in CF did not have any significant effect on the quality of composite cassava–wheat (CCW) bread. Cassava growers are encouraged to use inorganic fertilizer since studies have shown that it leads to increased dry matter content of root (Cadavid, El-Sharkawy, Acosta, & Sanchez, 1998). This implies an increased starch or amylose content since these are the major dry matter constituents of the root. However, no study has considered the effect of nitrogen treatment on the chemical composition and quality of food products from such roots. An average application dosage of fertilizer by cassava growers in Nigeria is about 150 kg/ha (Phillips, Taylor, Sanni, & Akoroda, 2004). As part of the efforts to screen the newly bred cassava clones for their productivity under various agro-ecological zones of the country, field trials were also planned to examine the crops response to fertilizer application at the average dosage applied by farmers. Consequently, a study was conducted in our laboratory to determine the effect of nitrogen fertilizer on the properties of flour as a primary product made from selected CMD resistant cassava clones (Shittu, Dixon, Sanni, Maziya-Dixon, & Awonorin, submitted for
2. Materials and methods The materials used in this study include wheat flour (Honeywell Flour Mills, Lagos), cassava flour from five selected cassava varieties (M98/0040, 82/00058, 92B/0061, 99/6012 and 98/0002) from IITA, Ibadan. The cassava were grown with (160 kg/ha NPK fertilizer) or without fertilizer. The level of fertilizer used is an average dosage applied by farmers in the country (Phillip et al., 2004). Other materials include Simas margarine (PT Intiboga Sejahtera, Jakarta, Indonesia), salt and sugar (Dangote Nigeria Plc., Lagos), Fermipan Baking yeast (DSM bakery ingredient, Dordrecht-Holland), Edlen Dough Conditioner (EDC) (Edlen International Inc., Nigeria). The physical, chemical and functional properties of the flours from the selected clones as presented in Shittu et al. (submitted for publication) are shown in Tables 1–4. The bread dough was prepared using the ingredients in the proportion shown in Table 5. The mixing was done manually for
Table 1 Physical properties of cassava flour used in the study L*
Bulk density (g/cm3)
Variety
a*
b*
F
UF
F
UF
F
UF
F
UF
82/00058 98/0002 92b/0006 M98/0040 99/6012
0.34 0.32 0.39 0.32 0.28
0.34 0.25 0.36 0.48 0.41
86.02 88.81 87.25 86.90 87.48
86.07 86.66 87.59 86.51 88.13
1.23 0.81 1.20 1.06 1.13
1.37 1.34 0.50 1.78 0.66
15.60 12.26 14.96 13.65 13.54
14.99 13.90 14.65 15.29 13.89
LSD (p < 0.05)
0.025
0.086
0.127
0.182
F: fertilized; UF: unfertilized. Mean values in bold fonts (F–UF) are significantly different from each other.
Table 2 Chemical composition of cassava flour used in the study Cassava genotype
82/00058 98/0002 92b/0061 M98/0040 99/6012 LSD (p < 0.05)
Protein (%)
Ash (%)
Sugar (%)
Starch (%)
Amylose (%)
CNP (mg/kg)
pH
F
UF
F
UF
F
UF
F
UF
F
UF
F
UF
F
UF
F
UF
17.3 13.8 12.9 20.9 9.6
14.8 4.3 10.4 19.3 12.0
13.5 15.6 20.4 23.9 26.3
19.5 19.5 14.3 15.6 19.9
2.65 2.97 2.94 2.71 1.86
2.45 2.25 2.43 1.85 2.47
82.26 88.96 75.67 84.66 85.23
88.46 90.15 80.84 76.52 86.70
19.74 18.73 23.50 23.47 20.81
18.82 18.42 23.38 23.81 23.29
17.8 5.9 14.1 2.7 0.0
2.8 2.7 19.1 2.7 3.7
6.03 5.94 5.36 5.23 5.28
5.99 5.98 5.34 5.67 5.90
0.34 0.32 0.25 0.36 0.36
0.38 0.32 0.36 0.47 0.41
4.2
1.5
0.36
5.17
0.12
3.2
0.02
TTA (%)
0.09
F: fertilized; UF: unfertilized. Mean values in bold fonts (F–UF) are significantly different from each other. CNP: cyanogenic potential; TTA: total titratable acidity.
Table 3 Functional properties of cassava flour used in the study Genotype
82/00058 98/0002 92b/0061 M98/0040 99/6012 LSD (p < 0.05)
Diastatic activity (mg maltose)
WAC (%)
Swelling power
Starch damage (%)
Least gelation concentration (%)
F
UF
F
UF
F
UF
F
UF
F
UF
F
UF
148.00 62.50 101.00 125.00 22.50
198.00 75.50 125.00 176.00 93.00
201.67 195.11 172.68 170.50 161.97
21.71 16.55 18.97 17.83 17.75
273.62 257.08 219.64 263.42 249.37
249.54 203.24 235.93 244.31 242.94
10.26 14.78 12.53 10.63 10.81
13.46 15.13 12.80 12.05 14.09
1.42 1.42 1.09 2.62 1.66
0.89 1.37 1.37 2.87 2.64
12.0 9.0 13.0 12.0 13.0
12.0 10.0 10.0 12.0 10.0
13.00
7.79
OAC (%)
6.43
0.90
0.53
3.0
F: fertilized; UF: unfertilized. Mean values in bold fonts (F–UF) are significantly different from each other. OAC: oil absorption capacity; WAC: water absorption capacity.
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76.83 77.15 79.15 81.72 77.68 79.30 78.03 79.15 79.22 78.38
0.44
UF
Pasting temperature (°C)
F
Table 5 Dough recipe and formulation used Recipe
Amount (%)
Wheat flour Cassava flour Water Sugar Salt Yeast Shortening EDC
90 10 60–62 10 1 1.5 5 0.3
4.00 4.27 3.87 4.47 4.33 0.07
4.13 4.24 4.04 4.63 4.57
15 min prior to kneading, which was also done manually. The dough was divided into 100 g divisions. Proofing of the dough was done in the pan at ambient conditions (29 ± 2 °C, 79% RH) for 2 h. Baking was done with an electric oven (Gallenkamp, UK) at 170 °C for 25 min. The weights of the loaves were determined with the aid of a weighing balance with accuracy of 3 d.p. (Mettler Toledo, Switzerland). Oven spring was determined by recording the height of the fermented dough and height of the baked bread samples. Oven spring was determined as the difference in the dough height and baked bread height. The loaf volume was determined after baking process using volume displacement method in which millet seed was used instead of rapeseed. The specific volume was calculated as
3.01 5.55 5.78
148.84 182.00 147.38 121.08 107.54 64.08 154.17 52.25 123.21 149.38 14.42 169.92 77.58 142.96 170.29
LSD (p < 0.05)
27.07
275.04 322.63 216.42 227.21 306.46 163.25 351.92 224.96 264.00 277.83 82/00058 98/0002 92b/00061 M98/0040 99/6012
F: fertilized; UF: unfertilized. Mean values in bold fonts (F–UF) are significantly different from each other.
6.52
10.29 62.54 34.54 48.09 57.25
Specific volume ðcm3 =gÞ ¼
210.96 168.46 164.17 104.30 157.09
24.71 232.46 112.13 189.54 227.55
80.58 225.92 73.67 166.67 208.83
16.80 71.75 21.42 43.46 59.71
UF F UF
Setback viscosity (RVU)
F UF
Final viscosity (RVU)
F UF
Breakdown viscosity (RVU)
F UF
Trough (RVU)
F UF
Peak viscosity (RVU) Genotype
Table 4 Pasting characteristics of flour as affected by cassava genotype and fertilizer application
F
Peak time (min)
All other percentages are based on composite flour weight.
loaf volume loaf weight
ð1Þ
Textural analysis was determined with the aid of a cone penetrometer to measure bread crumb softness. Six centimeters thick bread slice was placed on the base of the penetrometer (Central Model, Central Ignition Co., London) and the tip of the cone was adjusted to touch the central core surface of the bread crumb. The cone was then released to penetrate into the crumb under gravitational force. The readings were done in duplicate. Penetration was measured at 3 s (range 0–400 units, equivalent to 0–40 mm penetration). Higher penetration units indicate increased crumb softness. The tristimulus color parameters L* (lightness), a* (redness to bluishness), a* (yellowness to greenness) of the baked loaves crumbs were determined using a digital colorimeter (Color Tec PCM, Accuracy Micro Sensor Inc., USA). The instrument has spot diameter view of 15 mm. To determine the color of bread crust, the top crust was divided into three regions while the tristimulus color parameters L*, a*, b* were determined at each point in duplicate. The brownness index (BI) was calculated according to Maskan (2001): BI ¼
x¼
100 ½x 0:31 0:17
ða þ 1:75 LÞ ð5:645 L þ a 3:012 bÞ
ð2Þ
ð3Þ
For crumb moisture determination, 1 g of bread was obtained from five different portion of a slice and weighed into a previously weighed Petri dish. The Petri dish and the samples were transferred into the oven set at 105 °C to dry to constant weight for 2 h. At the end of the 2 h, the Petri dish and sample were removed from the oven and transferred to desiccators and cooled and weighed. Digital image analysis was performed on the crumb grain by capturing images of the sliced breads using a flatbed Mercury Scanner 1200U (SCAMXX, Mercury, China; http://www.kobian. com). The images were scanned full scale at 300 dots per inch
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Table 6 Effect of cassava variety and N-fertilizer on some physical properties (n = 5) of the composite bread Variety
Fertilizer application
Oven spring (cm)
Volume (cm3)
Weight (g)
Specific volume (cm3/g)
Softness index (mm)
Moisture content (%)
M98/0040
F UF
0.27bc 0.17b
888.71b 893.03b
153.67b 130.67a
5.78b 6.85c
28.07ef 29.33f
34.63d 34.43d
99/6012
F UF
0.13b 0.63c
844.03b 970.88b
150.67b 155.18b
5.61b 6.26bc
18.43a 27.10de
31.42a 32.50b
92B/0061
F UF
0.27bc 0.07b
924.84b 933.17b
150.00b 152.00b
6.19bc 6.15bc
28.30ef 26.50d
33.11c 34.53d
82/00058
F UF
0.60a 0.17b
667.72a 856.10b
150.67b 134.33a
4.43a 6.37bc
23.80c 28.53ef
34.70d 32.50b
98/0002
F UF
0.23bc 0.20b
666.44a 640.97a
150.67b 146.67b
4.43a 4.37a
21.30b 28.70ef
34.64d 34.70d
(V) (F)
* *
*** *
NS NS
*** **
*** ***
*** NS
**
NS
*
*
***
***
Main effect Variety Fertilizer Interaction VF
Mean values followed by the same alphabet within a column are not significantly different (p > 0.05). F: fertilized; UF: unfertilized. Significant effect at p 6 0.05. **Significant effect at p 6 0.01. ***Significant effect at p 6 0.001. NS: not significant.
*
Table 7 Correlations between the CCW bread properties Variable
OVSP
VOL
WT
SPV
Oven spring (OVSP) Loaf volume (VOL) Loaf weight (WT) Specific volume (SPV) Softness index (SOFT) Moisture content (MOI) L a b Brownness index (BI) Number of small cells (NSC) Number of large cells (NLC) Total number of cells (TNC) % cell area (AREA)
1.000 0.490** 0.078 0.409** 0.207 0.301
SOFT
1.000 0.065 0.904** 0.272 0.392*
1.000 0.363 0.318 0.015
1.000 0.378* 0.359
1.000 0.300
0.050 0.155 0.549** 0.374* 0.006
0.272 0.327 0.408* 0.519** 0.228
0.498** 0.587** 0.235 0.416* 0.183
0.039 0.048 0.471** 0.298 0.294
0.195 0.278 0.374* 0.033 0.321
0.077
0.048
0.061
0.031
0.018
0.212
0.185
0.183
0.097
0.301
MOI
L
a
b
BI
NSC
NLC
TNC
0.104 0.144 0.286 0.289 0.278
1.000 0.836** 0.179 0.847** 0.046
1.000 0.249 0.762** 0.002
1.000 0.333 0.444*
1.000 0.272
1.000
0.069
0.017
0.499**
0.409*
0.238
0.338
0.175
1.000
0.278
0.298
0.270
0.033
0.061
0.464
0.210
0.989
0.322
1.000
0.034
0.025
0.038
0.621**
0.452*
0.247
0.253
0.764**
0.361*
AREA
1.000
0.429*
1.000
**
Correlation is significant at the 0.01 level (two-tailed). Correlation is significant at the 0.05 level (two-tailed).
*
and analyzed in grey scale. A 200 200 pixel square field of view (FOV) was evaluated for each image. This FOV captured the majority of the crumb area of each slice. Seven digital images were processed and analyzed for each bread sample, giving a total of 70 images. Image analysis was performed using the ImageJ 1.32j software (National Institute of Health, USA). All samples used throughout the analyses were replicated five times except otherwise stated. 3. Statistical analyses One-way analysis of variance and generalized linear model analyses of data were carried out using SPSS 10.0 statistical package (SPSS Inc., USA). 4. Results and discussion 4.1. Size related bread properties Table 6 shows the effect of cassava genotype and fertilizer application on some physical properties of CCW bread. The values
of oven spring, loaf volume, loaf weight and specific volume, which ranged from 0.60 to 0.63 cm, 640 to 971 cm3, 130 to 156 g and 4.43 to 6.85 g/cm3, respectively, were significantly different (p < 0.05) from each other. These values fall within the range of values reported in previous studies (Dwyer & O’Halloran, 1999). From the generalized linear model (GLM) analysis it was found that cassava genotype and fertilizer application significantly (p < 0.05) affected the oven rise and loaf volume. Composite breads from fertilized clones 99/6012, 82/00058 and M98/0040 had significantly smaller specific volume (p < 0.01) than the unfertilized. Among the unfertilized genotype, M98/0040 gave the highest specific volume while 98/0002 gave the least. Oven spring, which takes place in the early period of baking, is a measure of dough strength/stability that is basically dependent on certain factors such as thermal regime (heating rate and duration), type of flour and ingredients used in dough formulation (Gandikota & MacRitchie, 2005; Sumnu, Datta, Sahin, Ozge Keskin, & Rakesh, 2006). It correlated positively with the loaf specific volume (r = 0.409, p < 0.01, Table 7). No significant correlation was however, found between the oven spring and any of the cassava flour properties. From the works of Soulaka and Morrison (1985) and Luyten and van Vliet (1995), it was indicated that variation in
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the size distribution and morphology of starch granules in the flours could have significant effect on the formation and physical properties of the crumb cell wall. In addition, polymeric changes such as starch gelatinization and protein denaturation which take place in the oven affect dough viscosity and further determines the amount of stress exerted by gas on the cell wall (Blanshard, 1986). Furthermore, excessive stress on the gas cell could lead to tensile failure and opening up of the cell wall faces (Bloksma, 1990; Fan, Mitchell, & Blanshard, 1999), thereby leading to gas cell coalescence (dough collapse). In this experiment, starch granular distribution was not studied. Also, other bread formula and baking condition used were not different except cassava flour, which is the only source of variation. It is pertinent to note that variation in loaf weight was neither affected by cassava genotype nor fertilization; it was their interaction that significantly affected loaf weight (Table 6). Hence, fertilized bread samples from M98/0040 and 82/0002, however, had slightly higher loaf weights. Physically, weight loss during baking is mainly due to water evaporation at the outermost layer (Therdthai, Zhou, & Adamczak, 2002). The water evaporated consists of the free or temporarily bound water molecules and not from the
Table 8 Effect of cassava variety and fertilizer application on the top crust color of CCW bread Variety
Treatment
L*
a*
b*
Brownness index
M98/0040
F UF
54.2ab 54.9ab
13.2b 12.8b
25.4ab 25.1ab
79.58c 76.82bc
99/6012
F UF
57.9ab 53.2a
12.6b 14.1b
28.8b 24.6ab
82.84d 80.23d
92B/0061
F UF
61.9bc 58.6ab
9.9a 10.4a
25.4ab 25.7ab
63.54ab 69.52abcd
82/00058
F UF
66.9c 54.2ab
9.2a 14.1b
25.5ab 24.5ab
57.22a 77.89bd
98/0002
F UF
57.4ab 57.7ab
13.3b 9.8a
22.5a 25.5ab
65.88ab 69.52abcd
NS *
** NS
NS NS
** NS
NS
***
NS
Main effect Variety (V) Fertilizer (F) Interaction VF *
**
NS ***
Significant effect at p 6 0.05. Significant effect at p 6 0.01. Significant effect at p 6 0.001. NS: not significant. F: fertilized; UF: unfertilized. Values in parentheses are standard deviations from the mean (n = 3).
Table 9 Significant correlates of cassava flour properties with the composite bread properties
Trough Setback viscosity Final viscosity Breakdown viscosity Pasting time SD LGC Protein Starch Ash WAC TTA
Volume
Weight
Specific volume
L
a
b
NSC
NLC
CA
0.733* – 0.797* 0.755* 0.656* – – – – – – –
– – – – – – 0.745* – – – – –
0.783** 0.682* 0.777** 0.734* 0.804** – – – – – – –
– – – – – 0.664* 0.642* 0.637* – – – –
– – – – – 0.776** 0.762* – – – – –
– – – – – – – – 0.637* – – –
– – – – – – – – – 0.750* – –
– – – – – – – – – 0.778** – –
– – – – – – – – – – 0.789** 0.676*
NSC: number of small cells; NLC: number of large cells; CA: crumb cell area; SD: starch damage; LGC: least gelation concentration; WAC: water absorption capacity; TTA: total titratable acidity. Significant effect at p 6 0.05. **Significant effect at p 6 0.01.
*
Table 10 Stepwise regression models between cassava flour and bread properties Dependent variable
Independent variable
Coefficient
R2 change
Significance F change
Volume
Constant Breakdown viscosity Starch Total titratable acidity
1451.121 5.873 14.951 1100.541
– 56.9 24.3 10.9
– 0.012 0.02 0.028
Weight
Constant Least gelation concentration Diastatic activity
185.43 0.419 0.068
– 55.5 19.8
– 0.013 0.049
Specific volume
Constant Peak time
6.818 2.928
– 64.6
– 0.005
L*
Constant Starch damage
38.532 1.504
– 44.1
– 0.036
a*
Constant Starch damage Least gelation concentration
11.741 0.551 0.066
– 55.3 73.3
– 0.008 0.039
Number of small cells
Constant Ash
615.471 60.617
– 60.5
– 0.008
Number of large cells
Constant Ash
583.988 61.283
– 55.5
– 0.008
% cell area
Constant Water absorption capacity
22.271 0.002
– 57.5
– 0.007
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mass of the flour in the gel. Stepwise regression analysis further indicates that bread volume could be reliably predicted from the breakdown viscosity, starch content and total titratable acidity of the flour to about 91% accuracy (Table 10). The most important property to bread volume is the breakdown viscosity as it accounted for about 57% of variation in bread volume. LGC and diastatic activity accounted for about 56% and 20% of variation in loaf weight, respectively. 4.2. Crumb moisture and texture The values of crumb moisture and softness index are also shown in Table 6. GLM analysis indicates that the most significantly affected bread property is the crumb texture. The crumb softness index ranged from about 18 to 30 mm (penetrometer unit). About 80% of the unfertilized cassava gave significantly softer bread crumb as compared to the fertilized samples. The crumb moisture which ranged from about 31% to 35% was only influenced by genotypic differences and not by fertilizer application. The moisture content was strongly affected by the genotypic differences in the cassava roots. This might possibly be due to the differing gelatinization properties of flour from the cassava roots (Eggleston et al., 1993). Piazza and Masi (1995) reported a significant negative correlation between moisture content and modulus of elasticity for different types of bread. Thus, higher moisture is expected to give rise to softer crumb (higher crumb penetration value). Although, softness index of the composite bread increased with moisture content, however, the correlation is insignificant (Table 7). On the other hand, a significant negative correlation (r = 0.392, p < 0.05, n = 5) was observed between the crumb moisture and loaf volume. It should be mentioned that increased loaf volume implies that the crust surface area available for moisture evolution would likely increase. Therefore, loaf volume should correlate negatively with moisture content. However, no significant linear correlation of flour properties on crumb moisture and texture was observed. 4.3. Crust color characteristics
Fig. 1. Gray images of bread crumb obtained from composite cassava–wheat flour. Differences observed are due to varietal differences and fertilizer application to cassava on the field (F: fertilized; UF: unfertilized).
gelatinized starch granules. The extent of gas cell coalescence is expected to determine the amount of volatiles that will escape from the baking loaf; therefore, loaf weight should correlate negatively with oven rise. However, the loaf weight did not significantly correlate with oven rise or any of the size related properties. Flour with higher paste viscosities had higher loaf volumes (Table 9). This is in agreement with the observation of Defloor et al. (1994, 1995). Also, least gelation concentration (LGC) of the flour correlated negatively with loaf weight (r = 0.745, p < 0.05). By interpretation, cassava flour that gels at lower flour/water ratio will give rise to heavier bread loaf since flours with higher LGC would have lesser amount of water per unit
Determination of bread crust’s color is important to determine its acceptability. Principally, the crust color exhibited in a bread loaf depends on a number of factors including dough formulation (i.e. type of flour, type and quantity of ingredient used), baking temperature and time etc (Gallagher, Gormley, & Arendt, 2003; Gallagher, Kunkel, Gormley, & Arendt, 2003; Shittu, Raji, & Sanni, 2007). In this study, bread crust’s lightness (L*), redness to greenness (a*) and blueness to yellowness (b*) ranged from 53 to 59, 9 to 15 and 22 to 29, respectively (Table 8). The brownness index (BI) also ranged from 57 to 83. One way ANOVA showed that the bread samples differed significantly in terms of the crust’s lightness (L*, p < 0.05), redness to greenness (a*, p < 0.001) and brownness (BI, p < 0.01). However, their blueness to yellowness (b*) did not differ significantly. When the singular effects of the independent factors are considered, fertilizer application had a significant effect on the crust’s lightness, whereas genotypic difference affected the brownness (BI) of the crust (p < 0.01). Specifically, genotype 99/6012 gave the most brownish crust. About 60% of the fertilized cassava gave bread with lighter crust (lower BI). Linear correlation analysis (Table 9) also indicated that as protein content of cassava flour increased, the lightness (L*) reduced due to nonenzymatic browning which gave the crumbs darker color. Furthermore, our results showed that higher degree of damaged starch in cassava flour samples gave bread with darker crust color. Starch damage in cassava flour accounted for about 44% of the variation in crust lightness (Table 10).
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Fig. 2. Showing the typical cell structure (left) and the corresponding pore size distribution (right) of composite bread crumb using flour from genotype 82/00058.
Fig. 3. Showing the typical cell structure (left) and the corresponding pore size distribution (right) of composite bread crumb using flour from genotype 92b/0061.
4.4. Image analysis of bread crumb structure Knowledge of the structure and properties of bread crumb is necessary to optimize its quality and consequently its acceptability. Digital image analysis (DIA) technique has been widely used to analyze the bread crumb structure (Barcenas & Rosell, 2005; Brosnan & Sun, 2004; Datta, Sahin, Sumnu, & Keskin, 2007; Gallagher, Gormley et al., 2003; Gallagher, Kunkel et al., 2003). Though mechanical behavior of bread crumb is complex, DIA techniques have also been used to generate qualitative relationship between bread crumb structure and its mechanical behavior. This complex-
ity may even become more prominent when composite flour system, as found in this study, is used. From the findings reported in our recent paper on the physical properties of composite cassava–wheat (CCW) bread (Shittu, Raji et al., 2007) the complexity of physical properties CCW bread as affected by varying baking temperature and time was evident. However, the crumb grain structure of the resultant loaves was not investigated. The additional two factors (cassava genotype and fertilizer application) which have been considered in this work could make determination of crumb structure more practically important as it has been rarely reported for composite bread in literature.
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1500
1200
82-00058F CV= 25.21%
900 600 300
Frequency
Frequency
1500
0
1200
82-00058U CV= 19.36%
900 600 300 0
0
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0
30
60
Gray value
Gray value
1500
1200
92b-0061 F CV= 21.03%
900 600 300
Frequency
Frequency
1500
0
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92b-0061U CV= 25.11%
900 600 300 0
0
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90 120 150 180 210 240 270
0
30
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Gray value
900
Frequency
Frequency
1200
98-0002 F CV= 25.17
1200 600 300
98-0002U CV= 17.93%
900 600 300 0
0 0
30
60
0
90 120 150 180 210 240 270
30
60
90 120 150 180 210 240 270
Gray value
Gray value
1500
99-6012 F CV= 37.63%
900 600 300
Frequency
1200
Frequency
90 120 150 180 210 240 270
Gray value
1500
0
99-6012U CV= 26.97%
1200 900 600 300 0
0
30
60
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0
30
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Gray value
90 120 150 180 210 240 270
Gray value
1500
1200
1200
M98-0040 F CV= 21.31%
900 600 300 0
Frequency
Frequency
90 120 150 180 210 240 270
M98-0040U CV= 21.18%
900 600 300 0
0
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60
90 120 150 180 210 240 270
Gray value
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Gray value
Fig. 4. Distribution of gray levels in bread crumbs and respective coefficient of variation (CV) as an index of crumb cell uniformity of bread samples (n = 5).
Fig. 1 shows the gray images of CCW bread crumb from the five cassava clones. These figures indicate the typical differences in the crumb structure formed based on genetic difference and application of fertilizer. Obviously, the differences are faintly perceivable when viewed with human eyes. In order to have a more vivid idea of the crumb structure, the cropped RGB images were processed to enhance its feature in gray scale and by applying contrast to differentiate the pores and solid portions of the crumb. The outlines obtained are exemplified in Figs. 2 and 3 for genotype 82/00058 and
92/0061, respectively. One notable feature of the images is that the shape of the crumb cells is highly irregular. This is generally true of all the composite bread obtained in this study. This might be due to the heterogeneous polymeric composition, non-uniform dough strength and consequently non-uniform carbon dioxide pressure distribution in the dough. This anisotropy could also introduce some sort of complexity into the mechanical behavior of bread (Gibson & Ashby, 1997). Also, unfertilized bread samples seem to have more open crumb structure than the fertilized samples in
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30 26
Number of cells /cm3
3
No of small cells /cm
34
Fertilized Unfertilized
22 18 14
Fertilized
30
Unfertilized
26 22 18 14 10
10 82-00058
92b-0061
98-0002
99-6012
82-00058
M98-0040
Cassava clone
92b-0061
98-0002
99-6012
M98-0040
Cassava clone Fertilized
Unfertilized
% Cell area
26 22 18 14 10 6 82-00058
92b-0061
98-0002
99-6012
M98-0040
Cassava clone Fig. 5. Effect of cassava genotype and fertilizer on the crumb cell characteristics of composite cassava–wheat bread determined by digital image analysis of bread crumbs (n = 7).
most of the cassava clones. This might be due to the differences in the gelatinization characteristics of the flour as affected by the fertilizer application. Eggleston et al. (1993) had similarly observed that starch gelatinization characteristics critically affected the crumb structure of wheatless bread from various cassava clones. In determining the pore size distribution with DIA, particles whose sizes are greater than 1 pixel have been considered. The reason is that particles having sizes of approximately 1 pixel are considered to be ambiguously small to be considered as crumb cells at such a digital resolution of 300 dpi we have used in the image acquisition step (Datta et al., 2007). The median diameter of the crumb cells from the CCW bread based on the cell diameter ranged from 1300 to 1850 lm. The gray level (GL) distribution was equally analyzed to determine the uniformity of the crumb structure across a loaf (Fig. 4). From each loaf five slices were obtained from area located 1.5 cm away from the edges of the loaf. The averages of frequency of gray color intensity for each crumb area (200 200 squared pixels) were obtained while the coefficient of variation (CV) of GL intensity was taken as a measure of uniformity of crumb structure. This approximation was assumed since there is a relationship between cellular structures of bread crumb and the intensity of light reflected during image acquisition. The region with finer structure reflect more light (lower gray level intensity) while regions with coarser texture reflect less light. The higher the CV value the less uniform the crumb structure. It could be seen that fertilized bread had less uniform crumb structure than the unfertilized samples for all other cassava genotype except in 92b/0061 where fertilized bread crumb was found to have lower CV value (i.e. more uniform crumb structure). The actual crumb cell distribution in these samples is depicted in Fig. 5. The values plotted are the mean and the standard deviation of cell population per cm2. The total number of cells and the number of small cells per cm3 ranged from 21 to 27 and 20 to 25, respectively. The crumb cell distribution of the composite bread obtained is comparable with those reported
for wheaten and wheatless bread. Fertilizer application had a positive significant effect on the total number of cells formed in bread made from 92b/0061 and 98/0002 and the opposite effect was observed in 82/00058, 99/6012 and M98/0040. Thus, only the interactive effect of variety and fertilizer was found to have significantly affected the total number of cells and the number of small cells (p < 0.01). The number of large cell and total cell area were not significantly different based on the varietal and fertilizer effects. Fundamentally, major factors dictating the bread crumb microstructure include dough composition such as gluten quality (Ragaee & Abdel-Aal, 2006; Tohver, Kann, That, Mihhalevski, & Hakman, 2005), starch pasting characteristics (Ragaee & AbdelAal, 2006); dough formulation (Brennan, Blake, Ellis, & Schofield, 1996; Gallagher, Gormley et al., 2003); alpha amylase activity of flour (Tohver et al., 2005), mixing and proofing process (Baardseth, Kvaal, Lea, Ellekjaer, & Faergestad, 2000), and heating mode (Datta et al., 2007). The difference in the crumb cell microstructure of the CCW bread may be attributed to only two of these listed factors, namely, starch pasting and alpha amylase (diastatic) activity of flour as the same wheat flour, formulation and heating mode was used throughout the experiment. The diastatic activity (DA) and pasting profile of cassava flour were significantly influenced by cassava genotype and fertilizer use (Table 3). Too high DA leads to unstable, soft or watery dough unsuitable for baking while low DA gives stable dough but dense bake crumb (Tohver et al., 2005). Truly, DA of cassava flour had negative but insignificant negative correlations with loaf specific volume and crumb softness. The ash content also had significant influence on crumb cell distribution. It had positive correlations with the number of small cells (r = 0.75, p < 0.05) and large cells (r = 0.778, p < 0.01). In fact, it accounted for about 60.5% and 55.5% variation in these parameters of the composite breads, respectively (Table 10). The reason for this observation is presently not clear. Water absorption capacity of flour, however, accounted for about 57.5% of the variation in the % cell area on a bread slice.
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4.5. Overall effect of treatments on CCW bread’s properties It is important to have a general view of the effect of independent variables on the measured properties. Statistical significance of each effect as measured from the F-values (variance ratio) was used to determine how influential each factor is on the bread properties. The effect of genotype on the measured bread properties follows this order: moisture content > crumb texture > volume > specific volume > oven spring > weight, whereas in term of fertilizer application it follows this order: crumb structure > specific volume > loaf weight > oven spring > volume > moisture content. When we considered the contribution of each bread property to generate the overall loaf score by ranking, the flour unfertilized root of 92b/0061 gave the overall best performance for breadmaking purposes. 5. Conclusions In spite of the small amount of cassava flour (10%) included into wheat flour (90%), the breadmaking characteristics of the composite flours from different cassava genotype grown with or without fertilizer application differed significantly. The greatest effect of cassava genotype was realized on the crumb moisture while fertilizer application had the greatest effect on the bread crumb texture. The unfertilized bread samples seemed to have more open crumb structure than the fertilized samples in most of the cassava clones. Flour from unfertilized roots from genotype 92b/0061 showed the best performance in making composite cassava wheat bread. This study has been able to establish that careful selection of cassava root variety and application of fertilizer are important factors that should be considered in optimizing composite cassava–wheat bread quality. References Aryee, F. N. A., Oduro, I., Ellis, W. O., & Afuakwa, J. J. (2006). Physicochemical flour samples from the roots of 31 varieties of cassava. Food Control, 2, 916–922. Baardseth, P., Kvaal, K., Lea, P., Ellekjaer, M. R., & Faergestad, E. M. (2000). The effects of bread making process and wheat quality on French baguettes. Journal of Cereal Science, 32, 73–87. Balagopalan, C. (2002). Cassava utilization in food feed and industry. In R. J. Hillocks, J. M. Thresh, & A. C. Bellotti (Eds.), Cassava: Biology, production and utilization (pp. 301–318). CAB International. Barcenas, M. E., & Rosell, C. M. (2005). Effect of HPMC addition on the microstructure, quality and aging of wheat bread. Food hydrocolloids, 19, 1037–1043. Blanshard, J. M. V. (1986). The significance of the structure and function of the starch granule in baked products. In J. M. V. Blanshard, P. J. Frazier, & T. Galliard (Eds.), Chemistry and physics of baking (pp. 1–13). London: Royal Society of Chemistry. Bloksma, A. H. (1990). Dough structure, dough rheology and baking quality. Cereal Foods World, 35, 237–244. Brennan, C. S., Blake, D. E., Ellis, P. R., & Schofield, J. D. (1996). Effect of guar galactomannan on wheat bread microstructure and in vivo digestibility of starch in bread. Journal of Cereal Science, 24, 151–160. Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision – A review. Journal of Food Engineering, 61, 3–16. Cadavid, L. F., El-Sharkawy, M. A., Acosta, A., & Sanchez, T. (1998). Long-term effects of mulch, fertilization and tillage on cassava grown in sandy soils in northern Colombia. Field Crops Research, 57, 45–56.
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