Effect of processing factors on Shea (Vitellaria paradoxa) butter extraction

LWT - Food Science and Technology 66 (2016) 172e178

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Effect of processing factors on Shea (Vitellaria paradoxa) butter extraction G.A. Yonas a, *, E.A. Shimelis b, A.F. Sisay c a

School of Chemical and Food Engineering, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia School of Chemical and Bio-Engineering, Addis Ababa University, P.O. Box 385, Addis Ababa, Ethiopia c Ethiopian Environment and Forest Research Institute, P.O. Box 2322, Addis Ababa, Ethiopia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 June 2015 Received in revised form 10 October 2015 Accepted 13 October 2015 Available online 19 October 2015

The objective of this work was to evaluate the effect of processing factors on butter yield and mineral concentration of Shea butter (Vitellaria paradoxa) subspecies nilotica extracted using screw expeller. The significant effect of conditioning duration (CD), kernel moisture content (MC) and die temperature (DT) were investigated by a 33 full factorial design combined with response surface methodology. Butter yield, fines particles (foot), Ca and Zn concentration were selected as response variables. The model enabled to identify the optimum operating settings (MC ¼ 6.5 g/100 g w.b and DT ¼ 65.5  C) for maximize butter yield, under which it predicted 42.05 g/100 g d.w. While considering other response, in allover optimization, CD of 30 min, MC of 9 g/100 g w.b and DT of 65  C were obtained. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Butter yield Conditioning duration Die temperature Moisture content Optimization Chemical Compounds Studied in this Article: Hexane (PubChem CID: 8058) Hydrochloric acid (PubChem CID: 313) Nitric acid (PubChem CID: 944) Calcium standard for AAS (PubChem CID: 24963) Copper standard for AAS (PubChem CID: 23978) Lead standard for AAS (PubChem CID: 24924) Zinc standard for AAS (PubChem CID: 23994)

1. Introduction The Shea tree (Vitellaria paradoxa C. F. Gaertn, Sapotaceae family), is a major tree species of African agroforestry systems. The native range of this long-lived (over 200 years) savannah tree species is a large belt approximately 5000 km long by 500 km wide in areas receiving 600e1400 mm of rainfall. V. paradoxa is divided into two subspecies: subspecies paradoxa extends from Senegal

Abbreviation: CD, conditioning duration; MC, Moisture content; DT, die temperature of screw expeller. * Corresponding author. E-mail address: [email protected] (G.A. Yonas). http://dx.doi.org/10.1016/j.lwt.2015.10.036 0023-6438/© 2015 Elsevier Ltd. All rights reserved.

eastwards to the Central African Republic whilst the other named nilotica occurs in Uganda, Northeast Zaire, Southern Sudan and Ethiopia (Davrieux et al., 2010; Hall, Aebischer, Tomlinson, OseiAmaning, & Hindle, 1996). In Ethiopia the trees found in Gambella region at an altitude 600 m a.s.l, in areas receiving an annual rainfall of about 900e1400 mm (Deribe, 2005). The tree is generally found in semi-arid to arid areas north of the humid forest zone and blossom from February to March; fruits from May to August and mature from June to July. The fruit annual production is from 15 to 30 kg/tree (Honfo, Akissoe, Linnemann, Soumanou, & Van Boekel, 2013). Shea butter extracted using traditional boiling, semi-mechanized, mechanical and solvent extraction. Traditional method is labor-intensive, inefficient (20%) and inconsistence. Fully mechanized system like screw oil expeller

G.A. Yonas et al. / LWT - Food Science and Technology 66 (2016) 172e178

achieves extraction rates of 42e50% (USAID, 2004; FAO and CFC, 2004). Traditionally Shea butter used as food accompaniment, skin protectant, moisturizer and therapeutic; internationally it used in chocolate, cosmetics and pharmaceutical industries (Ferris, Collinson, Wanda, Jagwe, & Wright, 2001; USAID, 2004). The variability in butter yield mainly depends on the origin, pretreatments, processing factors and methods of extraction (Honfo et al., 2013). Davrieux et al., (2010) noticed that the average fat content of East African Shea kernels (52.92 g/100 g) was significantly higher (P < 0.05) than West African (48.03 g/100 g). Yield variability ranged from 43.9 to 58.4 g/100 g within Uganda also reported by Gwali et al., (2013). Pretreatment like soaking of nuts at room temperature for two weeks achieved higher (55 g/ 100 g) butter yield (Nde Bup, Kapseu, Matos, Mabiala, & Mouloungui, 2011). Heating (70e160  C) of kernels for 25 min results higher butter yield (Adeeko & Ajibola, 1990). Analysis of the butter extracted from 4 mm thick slices dried at 45  C showed highest yield (Kapseu et al., 2007). Processing factors such as particle size, heating temperature, screw speed, nozzle size and their interaction on the butter yield of mechanically expressed oil were reported (Adeeko & Ajibola, 1990; Nagre, Ellis, & Oduro, 2012; Olaniyan & Oje, 2011). There is no report regarding the fines particles (foot) content of Shea butter. However, Martínez, Mattea, and Maestri (2008) and Evangelista, Isbell, and Cermak (2012) determine the effect of processing factors on foot content of walnut and palm kernel oil respectively. The presence of some minerals in Shea butter has been reported by various authors. Some minerals were assessed by using atomic absorption spectroscopy and neutron activation analysis. Ca value varies from 0.2 to 34.1 mg/100 g d.w, Zn from 1.9 to 3.4 and Cu from 0 to 1.5 (Honfo et al., 2013; Megnanou, Niamke, & Diopoh, 2007). These heavy metal ions actively catalyze lipid oxidation. Their presence even in trace amounts has long been recognized as potentially detrimental to the shelf life of fats and oils. They can activate molecular oxygen by producing superoxides and lead to the formation of hydroxyl free radicals (Szefer & Nriagu, 2007). Variations in Shea butter have often been attributed to origin, extraction methods and processing factors. In view of, variability this research work is aimed to evaluate the effect of processing factors on butter yield and mineral concentration of Shea butter from Phugnido district using screw expeller. A model equation that would predict and determine the optimum conditions for butter yield, foot and mineral concentration was developed. Which is very important with regards to butter manufacturing, moreover the overall optimization of the process is also addressed for the first time.

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30 min with saturated steam. The batches were put in drying cabinet at 50  C and 15% relative humidity to reach uniform moisture content of 3 g/100 g. Considering the initial moisture content and the mass of Shea kernel in each jar, the required mass of distilled water was added to reach the target moisture content of 3, 9 and 15 g/1100 g (w.b) using Eq. (1) as research performed by Zewdu and Solomon (2007).

    Q ¼ W i M f  M i = 100  M f

(1)

where: Q is the mass of water to be added in kg; Wi is the initial mass of the sample in kg; Mi and Mf are the initial and final moisture content of the sample in g/100 g (w.b) respectively. The required amount of distilled water were added to each glass jars, then the samples were kept in refrigerator at 5  C (±1) for 5 days for the moisture to distribute uniformly throughout the sample. The jars were shaken at regular interval to facilitate internal moisture stabilization. 2.2. Extraction of Shea butter The pretreated kernels were pressed in a single step using screw oil expeller (Model: CA 59 G, 2011, Germany). The expeller was set using 4 mm restriction die, 20 rpm screw speed. The screw press was first run for 15 min without input material while heating via an electrical heating ring attached around the press barrel, to the desired temperature. The running temperature, both the butter and outgoing material (press cake) were constantly checked with a digital thermometer inserted into the restriction die. Between each run the press chamber components were dismantled, washed and dried. Each run was warmed in air tight jar at its perspective die temperature for 30 min just before pressing. The resulted butter was collected and filtered using low speed centrifuge (Model: L-530 Tabletop, 2012, China) at 5030 g for 30 min. The filtered butter was labeled and frozen at 16  C till analyses performed. 2.3. Methods of analysis According to Ixtaina et al., (2011) the initial butter content in the kernel and the residual butter content in the press cake was extracted with hexane, gravimetrically determined and expressed in dry basis. The butter yield from each run was calculated considering the initial butter content in the kernel and the residual butter content in the press cake using Eq. (2) (Martinez et al., 2012). Butter yield was expressed as a ratio between the respective run butter yields to initial butter content using Eq. (3).

Extracted Butter ðg=100gÞ ¼ ðButter in Kernel eButter in Press cakeÞ

(2)

2. Materials and methods 2.1. Sample collection and pretreatments Shea nuts, which are not damaged and spoiled, were collected from selected mother tree in Phugnido district, Gambella, Ethiopia. After, the pulp was removed by both scraping and boiling the nuts parboiled and dried using sunlight for a week. The dried nuts then cracked and the resulting kernels were manually sorted, washed and then dried using drying cabinet (Model: AS 100, 2003, Italy) at 50  C and 15% relative humidity. The cleaned kernels were manually grinded (3e5 mm), then conditioned using autoclave (Model: KORIMAT KA 160, 2006, Germany) at 95  C and 2 MPa for 0, 15 and

Butter Yield ðg=100gÞ ¼ ðExtracted Butter=Butter in KernelÞ  100% (3) As performed by Martinez et al., (2012), the fines particles were separated by centrifugation at 5030 g for 30 min, washed with hexane, filtered using Qualitative filter paper (Whatman grade 1, 2012, USA), dried for 30 min at 105  C, and then gravimetrically determined and expressed as weight percentage of the butter content. In mineral analysis, 0.25 g butter sample was subjected to acid digestion with 8 mL of nitric acid (65%), using high

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performance Microwave digester (Model: ETHOS One-CAT288EN003, 2008, Italy), then diluted to 100 mL using deionized water and analyzed for Ca, Cu, Pb and Zn using atomic absorption spectrophotometer (Model: ZEEnit 700 P, 2012, Germany) equipped with a flame and graphite furnace with the Zeeman background corrector. 2.4. Experimental design and statistical data analysis Processing factors that are conditioning duration, moisture content and die temperature were varied into three levels (Table 1) to obtain second-order and robust optimization model as described by Lazic (2004). According to Montgomery (2005) a combination of response surface methodology (RSM) with full factorial design (33) was developed to evaluate contribution of each factors, their interaction and for optimization. The butter yield, foot and Ca and Zn concentration were chosen as responses (dependent variables). Response surface methodology with miscellaneous design and 5 center points were used to evaluate 32 experimental runs (means of triplicate). The adequacy of the model was determined by evaluating the lack of fit, coefficient of determination (R2) and the Fisher test value (F-value) obtained from the analysis of variance that was generated by “Design-Expert®” Version 7.0.0 (Stat-Ease, Inc., Minneapolis, MN, USA) software. Statistical significance of the model and variables were determined at 5% probability level (P < 0.05). Each model was expressed in terms of coded factors and regardless of statistical insignificant terms. In spite of insignificance, factors that exhibit interaction was not eliminated from the models in order to support hierarchy.

cell wall structures. In contrast, higher MC above the optimal value (6.5) was the cause for the discrepancy of butter droplets not to form continuous phase to flow out and resulted insufficient friction during pressing (Martinez et al., 2012; Nagre et al., 2012). The optimum value is lower than 12e15 g/100 g recommended by Fintrac (1999) for use of simple manual presses, which operates at less expelling pressure than screw expeller. Butter yield show increment as DT raise; however interaction with other factors improved only up to 65  C. The high butter viscosity at low temperature, which makes difficult to seeps through the cake structure, might be the cause for low yield. On the other hand, high temperature evaporates the moisture within kernels, which makes insufficient to displace the butter from the cell wall structure (Nagre et al., 2012). The finding was much similar with the report by Tacoronte (2010) who suggested between 60 and 62  C using hydraulic jack press, and to Obeng, Adjaloo, and Donkor (2010) who found 60  C using low pressure manual screw press. However, it was lower than 82.24  C reported by Olaniyan and Oje (2011) using an instrumented piston-cylinder rig. 3.2. Response surface modeling and optimization of foot (fines particles) The highest foot of 32.32% was obtained at 0 min CD, 3 g/100 g MC and 30  C DT, while the lowest (3.63) was obtained at 15 min CD, 3 g/100 g MC and 70  C DT. The regression is statistically significant (p < 0.05) with a satisfactory determination coefficient of R2 ¼ 0.9989.

Foot ¼ 12:47  0:23CD  4:90MC  6:59DT þ 0:53CD  MC þ 6:90MC  DT þ 1:00MC 2  0:83DT 2

3. Results and discussion

(5) 3.1. Response surface modeling and optimization of butter yield As presented in Table 2, the highest extracted butter (41.63 g/ 100 g d.w) and butter yield (82.20 g/100 g d.w); and lowest extracted butter (18.50) and butter yield (36.53) were obtained at 0 min CD, 3 g/100 g MC, DT 70  C and at 0 min CD, 15 g/100 g MC, 30  C DT respectively. The ANOVA results highlight the regression is statistically significant (p < 0.05) with a satisfactory determination coefficient of R2 ¼ 0.9923. That is the coefficient of determination was able to explain 99.23% of the data variability.

Butter yield ¼ 76:45 þ 0:68CD  14:99MC þ 4:15DT þ 2:29CD  MC  2:00MC  DT þ 1:26CD2  17:60MC 2  2:82DT 2 (4) Eq. (4) shows the linear coefficient of MC is about 4 times of DT, this implies MC is more effective in regulating butter yield than DT. Moreover, the negative value in both quadratic terms leads to maximum extraction efficient (83.25%) around 6.5 g/100 g MC and 65  C DT (Fig. 1). The lower butter yield in less moisture kernel was probably due to the low moisture to displace the butter from the

Table 1 Experimental design of the study. No.

1 2 3

Processing factors

Conditioning duration (min) Moisture content (g/100 g w.b) Die temperature ( C)

Levels 1

2

3

0 3 30

15 9 50

30 15 70

As indicated in Eq. (5) all factors had negative coefficients that imply indirect proportion; and the higher magnitude in DT makes it much effective in decreasing foot content. High interaction between MC and DT indicates reduction of foot at less moisture with high temperature and at high moisture with less temperature (Fig. 2). In the former case the press cake less disintegrated by water evaporation and the high temperature coagulates the protein that did compact the press cake FAO, (2001). While at less die temperature more moisture needed to soften the press cake to reduce the fine particles diverted to the barrel openings. Similar to Evangelista et al., (2012) conditioning duration was insignificant. The optimization result gives a minimum (3.4 g/100 g) foot values around 30 min CD, 3 g/100 g MC and 70  C DT. At lowest foot percentage, the press cake became compact and lesser amount diverted to the barrel openings. Whereas at highest, the press cake become soft, fragile and greater amount of sediments diverted to the barrel openings. The finding was similar to Walnut seed oil extraction report by Martinez et al., (2008). Generally, as the press cake flexibility increases foot that will goes into the butter shows reduction. 3.3. Response surface modeling and optimization of minerals As presented in Table 2, The highest concentration of both Ca (21.99 mg/100 g d.w) and Zn (0.647) in the butter occurred at 30 min CD, 9 g/100 g MC and 30  C DT; the lowest were found around 30 min CD, 15 g/100 g MC and 70  C DT for both Ca (8.02) and Zn (0.607 mg/100 g d.w). The values in this finding were similar to other reports that stated from 0.2 to 34.1 and 1.9 to 3.4 (mg/ 100 g d.w) for both Ca and Zn respectively (Honfo et al., 2013). Relatively concentration of Zn is lesser than Ca; this caused

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175

Table 2 Butter yield, extracted butter, foot and minerals compositions of Shea butter at different processing factors. Run Independent variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Conditioning duration (min)

Moisture content (w.b)

Die temperature ( C)

0 0 0 15 15 15 30 30 30 0 0 0 15 15 15 15 15 15 15 15 30 30 30 0 0 0 15 15 15 30 30 30

3 3 3 3 3 3 3 3 3 9 9 9 9 9 9 9 9 9 9 9 9 9 9 15 15 15 15 15 15 15 15 15

30 50 70 30 50 70 30 50 70 30 50 70 30 50 50 50 50 50 50 70 30 50 70 30 50 70 30 50 70 30 50 70

Butter yield (g/ 100 g d.w)

65.14 76.91 82.20 65.08 74.24 76.90 65.34 73.76 75.68 70.73 78.39 76.57 70.89 77.41 77.41 75.42 76.56 74.28 77.41 76.49 71.41 77.86 80.15 36.53 40.04 44.30 37.82 45.92 42.01 44.43 46.71 47.69

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

Extracted butter (g/ 100 g d.w)

4.07 4.70 2.64 2.15 2.07 4.30 3.02 3.97 2.61 2.06 3.40 4.17 2.01 1.99 1.30 1.14 1.90 1.22 0.26 0.25 2.46 0.36 1.20 0.24 0.18 0.05 0.14 0.07 3.25 0.55 2.62 1.62

32.99 38.95 41.63 32.96 37.60 38.94 33.09 37.35 38.32 35.82 39.70 38.78 35.90 39.20 39.20 38.19 38.77 37.62 39.20 38.73 36.16 39.43 40.59 18.50 20.28 22.43 19.15 23.25 21.27 22.50 23.65 24.15

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

5.11 5.39 3.17 3.20 2.85 4.99 4.07 4.76 3.34 2.95 4.04 4.92 2.89 2.67 1.62 2.03 1.75 2.00 1.67 1.34 3.33 1.02 1.80 1.73 2.45 1.73 1.73 1.56 5.76 2.22 4.24 3.20

Foot (g/ 100 g)

32.23 18.80 4.76 30.67 18.75 3.63 30.47 17.54 3.54 18.47 12.92 5.63 17.78 12.78 12.54 12.60 12.40 12.53 12.16 4.86 17.70 12.34 4.87 7.23 7.76 7.87 7.67 8.69 7.88 7.87 8.76 8.41

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

Minerals (mg/100 g d.w) Ca 4.52 3.84 1.04 1.10 1.30 1.32 1.52 2.42 3.03 5.62 1.30 1.25 2.20 1.02 2.50 2.50 1.50 2.20 1.25 1.57 2.22 2.50 1.20 1.92 1.57 3.17 3.57 1.35 2.06 2.08 0.87 0.88

12.29 13.24 14.58 19.77 16.77 14.78 21.46 16.90 10.35 13.75 14.75 15.76 19.75 16.66 16.66 15.76 17.56 16.77 16.66 14.56 21.99 16.75 9.75 12.67 14.46 14.46 18.46 15.69 13.88 20.58 14.14 8.02

Zn ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.42 0.46 0.11 0.85 0.26 0.80 0.55 0.23 0.32 0.50 1.03 0.05 1.04 0.90 0.90 1.05 1.03 1.03 0.90 0.59 0.18 0.29 0.34 0.60 0.93 1.46 1.38 1.16 0.25 0.97 0.53 0.65

0.646 0.612 0.652 0.653 0.618 0.654 0.662 0.628 0.659 0.656 0.610 0.636 0.655 0.615 0.612 0.612 0.613 0.612 0.612 0.636 0.647 0.614 0.637 0.664 0.615 0.625 0.655 0.606 0.616 0.646 0.596 0.607

Cu Pb ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.03 0.01 0.02 0.01 0.05 0.01 0.01 0.02 0.01 0.01 0.01 0.03 0.03 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.05 0.03 0.02 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.01

ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND

ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND

Values are means ± SD (n ¼ 3) and ND denotes “Not Detected”.

higher sensitivity in the model used for Zn. Moreover their chemical properties vary, hence the ANOVA results show little different between two minerals. As expressed in Eqs. (6) and (7) both models are statistically significant (p < 0.05) with a determination coefficient of R2 ¼ 0.9805 and 0.9894 respectively.

A: CD (min) = 15.00

32

24.75

Foot

A: CD (min) = 15.00

82

10.25

3

70.75

Butter yield

17.5

59.5

70.00

15.00 60.00

48.25

12.00 50.00

C: DT (°C) 37

70.00

15.00 60.00

9.00 40.00

6.00 30.00

3.00

B: MC (g/100g)

Fig. 2. Response surface plot showing the interaction effect of die temperaturemoisture content on foot.

12.00 50.00

C: DT (°C)

9.00 40.00

6.00 30.00

3.00

B: MC (g/100g)

Ca ¼ 16:98 þ 0:78CD  0:43MC  2:48DT  0:62CD  MC  3:5CD  DT  1:76  CD2  0:67MC 2 (6)

Fig. 1. Response surface plot showing the interaction effect of die temperaturemoisture content on butter yield.

G.A. Yonas et al. / LWT - Food Science and Technology 66 (2016) 172e178 A: CD (min) = 15.00

    Zn ¼ 0:61  1:19  103 CD  8:75  103 MC      9:01  103 DT  7:83  103 CD  MC    0:01  103 MC  DT þ ð0:03ÞDT 2

0.656

Eq. (6) indicates DT is more determinant factor compared to others. The negative value of the quadratic term in both CD and MC leads to a maximum Ca concentration at 19.5 min CD and 8.0% respectively (Fig. 3). The response surface for minimum Ca concentration (8.3) shows the optimum 30 min CD, 15 g/100 g MC and 70  C DT. In Eq. (7) DT and MC have the highest influence on Zn concentration. As well, the negative value in the quadratic term of DT leads to a maximum Zn concentration around 52  C DT. Hence, the response surface for minimum Zn concentration (0.59) obtained at 30 min CD, 15 g/100 g MC and around 55  C DT (Figs. 4 and 5). Proportional increase of Ca concentration with CD, up to the peak, might be due to the effect of high temperature and pressure used for conditioning. As explained by FAO (2001) conditioning results rupture of cell walls and coagulation of protein in the kernel. This might be the cause for the release of Ca available within the kernel and to be mixed with the butter. However, longer CD showed decline of Ca concentration, this is possibly due to the high solubility of Ca in water to be washed-out by the steam used for conditioning (Damodaran, Parkin, & Fennema, 2008). Regardless of factors interaction, it can be concluded that MC is indirectly proportional to minerals concentration. This is possibly due to high water available in the kernel to solubilize the minerals than letting to precipitate into the butter (Damodaran et al., 2008). DT raises exhibit reduction of minerals concentration (Fig. 3). This might be due to the change in chemical dissociation of molecules that resulted the release of minerals in ions form that makes them more soluble in the water available than being expelled with the butter extracted. This finding is similar to the statement by Singh, Gamlath, and Wakeling (2007) that, the possibility of phytate hydrolysis at high extrusion temperature, also the modification of fiber components reduces their chelating activity, which favors the availability of minerals to be solubilized easily. However, for Zn

Zn concentration

(7)

0.64225

0.6285

0.61475

0.601

70.00

15.00 60.00

12.00 50.00

9.00 40.00

C: DT (°C)

6.00 30.00

3.00

B: MC (g/100g)

Fig. 4. Response surface plot showing the interaction effect die temperature-moisture content on Zn concentration.

C: DT (°C) = 50.00

0.628

Zn concentration

176

0.61975

0.6115

0.60325

0.595

15.00

30.00 12.00

22.50 9.00

B: MC (g/100g) = 9.00

B: MC (g/100g)

7.50 3.00

0.00

A: CD (min)

Fig. 5. Response surface plot showing the interaction effect of moisture contentconditioning duration on Zn concentration.

22

Ca concentration

15.00 6.00

18.75

15.5

12.25

9

70.00

30.00 60.00

22.50 50.00

C: DT (°C)

15.00 40.00

7.50 30.00

0.00

A: CD (min)

Fig. 3. Response surface plot showing the interaction effect of die temperatureconditioning duration on Ca concentration.

beyond 55.77  C the concentration showed increment (Fig. 4), this might be a result of high Zn2þ reactivity to form insoluble compound that would be precipitated into the butter (Damodaran et al., 2008). In addition, the water evaporation from the kernel at high die temperature would causes saturation of Zn2þ where a revers mechanism might occur. Higher DT (70  C) resulted reduction of Ca concentration than lower DT (30  C). This reduction might be explained by the fact that, high temperature favors the dissociation effect of molecules in the kernel and the modification of fiber components that reduced their chelating effect, this favors the availability of minerals to mobilize easily and solubilize in the water than to the butter extracted. However, lower temperature is not enough to alter the structure of molecules and fibers, the minerals which are not in hydrolyzed form easily precipitate in the butter. Further, longer CD will favor the release of minerals by rupturing cell walls and coagulating the

G.A. Yonas et al. / LWT - Food Science and Technology 66 (2016) 172e178

protein in the kernel (Singh et al., 2007; FAO, 2001). Higher moist kernels (15 g/100 g) resulted relatively lower Zn concentration in the butter than lower (3) moist kernel. This might be the presence of higher moisture to diffuse Zn (water-soluble) to the press cake (Damodaran et al., 2008). Moreover, high factor interaction between conditioning duration and moisture content indicates that cell structure disintegration by conditioning was not sufficient to minimize the Zn concentration, unless enough moisture is available to solubilize the minerals than letting to precipitate in the butter (FAO, 2001). The slight increase of Zn concentration as MC increases at low DT (30  C); might be due to low temperature that is not enough to hydrolyze molecules that bind Zn and to make it available to be solubilize in water and expelled out with the press cake. While at high (70  C) DT the rise in MC resulted reduction of Zn concentration, this might be due to the hydrolysis of molecules at high die temperature that yield Zn2þ to be solubilize in water and expelled out with the press cake (Damodaran et al., 2008; Singh et al., 2007). 3.3.1. Copper and lead As presented in Table 2 copper and lead were not detected. Since they are toxic and accelerate the oxidation reaction their absence preferred. Nevertheless, the presence of Cu in Shea butter was reported by Honfo et al., (2013) where it varies from 0 to 1.5 mg/ 100 g d.w. 3.4. Overall optimal conditions As discussed in previous sections optimal conditions for each response were discovered separately. Yet, it can be observed that the optimal values of the factors do not match for all responses. Hence, to find an overall optimal condition, a response surface optimization covering all the four responses (butter yield, foot, Ca and Zn concentration) was applied. From manufacturing perspective of Shea butter, butter yield was given higher weight than foot content and minerals concentration. Since butter with reasonable foot content and minerals concentration can be further filtered and used for soap making and other products, it is more appropriate to prioritize butter yield than other response variables in overall optimization. Optimization criteria were as follows: maximize butter yield and minimize others, all responses given one weight while butter yield given two. Optimal 30 min CD, around 9 g/100 g MC and 65  C DT were discovered. 4. Conclusion In this study the developed model equations can be used to predict butter yield and other response variables, as influenced by conditioning duration, moisture content and die temperature. Different model equations were used to express each response; and all three factors were not necessarily significant in all cases. The highest butter yield (82.20 g/100 g d.w) obtained at 6.5 g/100 g moisture content and 65.5  C die temperature, while conditioning duration was insignificant. In both foot and minerals concentration die temperature had more influence relative to other factors. Less foot content (3.4 g/100 g) in Shea butter was achieved at 30 min CD, 3 g/100 g MC and 70  C DT. The response surface for minimum Ca (8.3 mg/100 g d.w) and Zn (0.59 mg/100 g d.w) concentration shows the optimum values of 30 min CD, 15 g/100 g MC and 70  C DT and 30 min CD, 15 g/100 g MC and 55  C DT respectively. Whereas Cu and Pb not detected in the extracted Shea butter. In overall all optimization, favoring butter yield 30 min conditioning duration, 9 g/100 g moisture content and 65  C die temperature were obtained. Hence, Shea butter produced around these

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processing conditions will produce highest butter yield with lowest foot and mineral concentration of Ca and Zn. Acknowledgments The authors wish to thank Addis Ababa University and Ethiopian Environment and Forest Research Institute for funding this research. In a special way, we would like to thank Mr. Omar Sherif from Forestry Research Center, Phugnido Shea Butter Cooperative, Gambella Agriculture Research Institute and School of Chemical and Food Engineering at Bahir Dar University, Ethiopia. References Adeeko, K. A., & Ajibola, O. O. (1990). Processing factors affecting yield and quality of mechanically expressed groundnut oil. Journal of Agricultural Engineering Research, 45, 31e43. Damodaran, S., Parkin, K. L., & Fennema, O. R. (Eds.). (2008). Fennema's food chemistry (4th ed.). Boca Raton, FL: CRC Press Taylor & Francis Group. Davrieux, F., Allal, F., Piombo, G., Kelly, B., Okulo, J. B., Thiam, M., et al. (2010). 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