Influence of natural additives on groundnut oil yield and cake quality

International Journal of Science and Advanced Technology (ISSN 2221-8386) http://www.ijsat.com Volume 1 No 8 October 2011 Influence of natural addit...
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International Journal of Science and Advanced Technology (ISSN 2221-8386) http://www.ijsat.com

Volume 1 No 8 October 2011

Influence of natural additives on groundnut oil yield and cake quality Rahman Akinoso Department of Food Technology University of Ibadan Ibadan, Nigeria. [email protected]

Abstract—This study was carried out to determine the effect of additive on groundnut oil yield and protein content of its cake. Response surface experimental design was employed. The variables were additives ratio (0:100, 60:40, 50:50, 40:60, 100:0) of Pepper: tomato and incubation time (1, 1.5, 2.5, 4, 6 hours). While the responses were oil yield and crude protein. The recorded mean values for oil yield and crude protein were 36.8±8.96 and 59.4±7.00 respectively. Analysis of results showed significance of treatment on both oil yield and the cake protein content at p < 0.05. Coefficients of determination (R2) of the developed models were 0.7767 and 0.9682 for oil yield and crude protein respectively. The best desirability (0.96) was achieved at 100 percent tomato additive incubated for 3 hours. This combination produced oil that was equally rated in appearance with commercial virgin groundnut oil; oil yield was 48.2 and 69.39% cake protein content. Keywords- Groundnut oil; groundnut cake; pepper; tomato; natural enzymes; incubation time I.

INTRODUCTION

Groundnut (Arachis hypogea) is a crop grown primarily for the use of its kernels, oil, meal, leaves and vegetative residue. The kernels are consumed raw, boiled or roasted, and can be blended into paste and salted to make peanut butter. Oil and meal are derived from the kernel through extraction and separation. The oil is used for domestic cooking and shortening in pastries and bread. Industrial applications include production of margarine and vegetable ghee, pharmaceutical and cosmetic products, lubricant and emulsion for insecticides, and fuel for diesel engines. The meal or press cake is consumed as snack, or used in enriching low-protein tuber flours [1]. Oils can be recovered from oil-bearing materials by mechanical expression, solvent extraction, use of supercritical fluids and aqueous extraction method [2]. Use of commercial food grade enzymes is an advanced biochemical method of boosting oil recovery. Enzymes such as cellulase, pectinase, carbohydrase and proteases were reported to enhance the oil extractability of seeds [3]. Previous works on use of enzymes for boosting oil yield include sunflower kernels [3], jatropha seeds [4], soy bean [5] and rapeseed [6]. The technology is challenged with the long process time necessary for enzymes to liberate oil bodies and the choice of enzymes, which are not

Idaresit Uyai Ekaete Department of Food Engineering University of Uyo Uyo, Nigeria [email protected]

commercially available [4]. Fruits and vegetables have natural enzymes [7]. Investigation into the potential of commercial cultivated vegetables as a source of enzymes may encourage the adoption of the technology. Hence, the objective of this work is to determine the effect of natural enzymes in pepper (Capsicum annum) and tomato fruit (Lycopersicon esculentum) on groundnut oil yield and meal quality, and established the suitable operating parameters of the combined additives. II.

MATERIALS AND METHODS

A. Experimental design The experimental design was based on the rotatable design of Response Surface Methodology for a two-variable case as described [8]. Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for the modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. It differs from the procedure that involves the isolation of test variables and changing one variable at a time [8]. The RSM is important in designing, formulating, developing, and analyzing new scientific studying and products. It is also efficient in the improvement of existing studies and products. The most common applications of RSM are in industrial, biological and clinical science, social science, Food science, and physical and engineering sciences. RSM tests several variables at a time, uses special experimental designs to cut costs, and measures several effects by objective tests. The most important difference is that a computer takes the experimental results and calculates models, using Taylor second order equations, which define relationships between variables and responses [9]. The relationships are quantitative, cover the entire experimental range tested, and include interactions if present. The models can then be used to calculate any combinations of variables and their effects within the test range. The two independent variables were the Additives ratio (A) and Incubation Time (B). The combinations of the additives were 100:0, 60:40, 50:50, 40:60 and 0:100 corresponding to 5 g of pepper/0 g of tomato, 3 g of pepper/2 g of tomato, 2.5 g of pepper/2.5 g of tomato, 2 g of pepper/3 g of tomato, and 0 g of pepper/5 g of tomato. The range of incubation time was from 1 to 6hours. The pH of the mixture was unmodified and the temperature of incubation was kept

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International Journal of Science and Advanced Technology (ISSN 2221-8386) http://www.ijsat.com constant at 40 oC ± 2 oC for all the treatments. Total numbers of treatments were 13 (Table 1). All the experimental procedures were replicated three times; mean values were recorded as obtained data. The process was optimized by maximising both oil yield and protein content while appearance ranged. B. Determination of some physicochemical properties of raw materials The physicochemical properties of pepper, tomato and groundnut used for the study were determined to establish their quality characteristics. These were determined by appropriate standard methods using recommended methods of analysis and sampling Codex stan 234-1999 as guide [10]. Methodology adopted were AOAC method for apparent density for specific gravity; ASABE S410.1 method for moisture content [12]. AOAC 981.12 method for pH; AOAC 920.39C method for fat; AOAC 942.15 method for titratable acidity; AOAC 950.49 method for ash; AOAC 962.10 method for fibre; AOAC 954.01 for crude protein determination [12], and carbohydrate by calculation as reported [13]. Unit mass was measured by electronic weigh balance of 0.01g accuracy (JD-3G Series, Shenyang Longteng Electronic Co. Ltd, Liaoning, China). The additives colour was determined with the aid of spectrophotometer (UVIKON XL, North Star Scientific, Leeds, UK) at 420nm absorbance. C. Preparation of samples Cleaned groundnut, pepper and tomato fruits were separately milled. Pepper-tomato ratio was prepared as additive. This mixture was added to 20 g of groundnut and incubated using incubator at 40oC ± 2oC (L10753 Gallenkamp, UK) at varied time duration (Table 1). D. Determination of oil content The oil content in the treated samples was determined by official method AOAC 920.39C for crude fat determination [11]. The samples were fitted in the thimble, which was placed in the extractor and fitted up with the reflux condenser and a 250ml dry soxhlet flask of known weight. The soxhlet flask was filled to ¾ of its volume with petroleum spirit (boiling point 40 oC – 60 oC). The entire set up was placed on a heating mantle and the temperature was set at 50 oC. As the solvent began to boil, with a cotton wool fitted in the top of the condenser as a check to prevent over-boiling, water was released from a tap through hoses into the condenser. The spirit vapour was condensed at the condenser and the spirit was left to siphon several times for 8 hours. When the ether was short of siphoning, the heating mantle was turned off and the thimble extractor and flask was allowed to cool and detached. The remaining solvent in the thimble was poured into the spirit stock bottle, the samples were removed, and the empty thimble, flask and condenser was re-fixed onto the heating mantle and the heating procedure was repeated two

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times to recover pure spirit until the flask was dry. The flask which now contains the oil was detached and dried to a constant weight in the oven. The percentage of the oil was calculated using the formula stated below (Equation 1).

Where W0 is the initial weight of the dry soxhlet flask; W1 is the final weight of oven dried flask + oil

E. Determination of protein content Protein content of the samples was determined by official method AOAC 954.01 [11]. The dried groundnut cake was milled using the laboratory mortar and pestle and 2g was weighed into a Kjeldahl flask. Added to the flask was 5g of anhydrous sodium sulphate, 1g of copper sulphate and 1 tablet of Kjeldahl catalyst selenium. 25cc concentrated sulphuric acid and 5 glass beads were also introduced into the mixture. The mixture was heated in a fume cupboard, gently and gradually the heat was increased with occasional shaking till solution assumed a green colour. After cooling, black particles showing at the mouth and neck of the flask was washed down with distilled water, reheated gently at first and then burner turned full and heated until green colour disappeared, then allowed to cool. The digest was transferred with several washings into a 250cc volumetric flask and made up to the mark with distilled water. Distillation was carried out using a Markham distillation apparatus. The method of distillation employed involves passing steam through the Markham distillation apparatus for about 15 minutes. A 100 cc conical flask containing 5cc boric acid indicator is placed under the condenser such that the condenser tip is beneath the liquid. 5cc of the digest was pipette into the body of the apparatus via the small funnel aperture and washed down with distilled water followed by 5 cc of 60% NaOH solution. Steam was allowed through for about 5-7 minutes to collect enough ammonium sulphate. The receiving flask was removed and the tip of the condenser washed down into the flask. The flame was removed and the resultant developing vacuum will remove the condensed water. The solution in the receiving flask was titrated using 0.01N hydrochloric acid. A blank was also run through along with the sample. Protein content was calculated using equations 2 and 3.

(2) Crude protein = % Nitrogen x 6.25

(3)

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International Journal of Science and Advanced Technology (ISSN 2221-8386) http://www.ijsat.com

F. Sensory evaluation The sensory attributes of the oil samples were analysed using Multiple Comparison Test as described [14]. Market sample of crude groundnut oil was used as control (R). The samples were rated as ‘more attractive than R’ (5 points), ‘equally attractive as R’ (3 points), and ‘less attractive than R’ (1 point). A panel of 75 judges which cut across sex, age and status were randomly selected. Obtained scores were statistically analysed. G. Data analysis Mean values of three replicates recorded as obtained data were analysed using Analysis of variance (ANOVA) and regression. Test of significance was fixed at 95 % confidence level. Second-order polynomial equations were developed to express the relationship using proprietary software (DesignExpert, Statese Inc., Minneapolis, USA). III.

RESULTS AND DISCUSSION

content reported to be present in pepper and tomato could not have accounted for 12.5% difference between the control and recorded maximum value [19]. From Fig. 1 it can be seen that effect of additives is more pronounced. Therefore, obtained results might have been strongly influenced by enzymes in pepper and tomato. Pepper and tomato fruit contain cell wall degrading enzymes which include polygalactonase, cellulase, and pectinmethylesterase [23]. Statistical analysis of the data also revealed significant influence of treatment on oil yield at 5% level of significance. Similar observations were reported for sunflower [3], for palm oil [24], for jatropha seed [4], for soy bean [5], and for rapeseed [6].

Oil Yield 1.00

45 40

0.50

35

B : In c u b T im e

Where X is total volume of digested sample (ml); Va is volume of acid required to titrate sample (ml); Vb is volume of acid required to titrate the blank (ml); Nacid is normality of acid 0.01N; W is weight of sample in grams ;Y is volume pipette during distillation.

Volume 1 No 8 October 2011

5

0.00

30

-0.50

40

A. Preliminary data Cultivars of pepper, tomato and groundnut for the experiment are DT97/469, Roma-VF and Boro light respectively. These plant cultivars are widely cultivated in South-Western states of Nigeria, a rain forest zone. Their physicochemical properties are presented as Table 2. Fruits are graded by their physical characteristics (size, weight, colour and shape), internal attributes (soluble solids content, moisture content) and eating quality (proximate composition) [15]. Based on these quality parameters pepper and tomato used for the study can be classified as matured-ripe fruits [16, 17]. The general specification project for pickled products established by the processed fruit and vegetable committee of the Codex Alimentarius Commission FAO/OMS, notes a maximum pH specification of 4.6 for pickled products [18]. The pH of the fruits and groundnut agreed with reported natural values of the crops [19, 20]. The information is required because enzymatic activities are pH dependent. Effect of processing factors on oil yield The recorded minimum and maximum oil yield were 24.9 and 57.2% respectively. Mean value was 36.8 ± 8.9%. Obtained maximum oil yield was higher than 50% reported [21], and 45.4% mentioned as maximum fat content of groundnut kernel from different locations [22]. Although fat contents of groundnut is known to vary with varieties, but this maximum oil recovered was also higher than the untreated used as control (44.7%) in this study. Likewise, 0.2% fat

-1.00 -1.00

-0.50

0.00

0.50

1.00

A: Additive Ratio

Figure 1. Contour plot of treatment effect on oil yield

A quadratic response surface model (Equation 4) was used to express relationship between oil yields (Y1), additives ratio (A) and Incubation time (B). The coefficient of determination (R2) of the model is 0.77, which indicates that the model can only explain 77.67% variability in the process. It also means that 22.33% is due to other factors not explained by the model. The model satisfied lack of fit test, a suggestion that application of the mathematical expression in predicting oil yield as affected by this treatment will give acceptable results at 5 % level of significance.

B.

C. Effects of processing factors on the cake crude protein Maximum crude protein content of 70.05% was obtained from the cake at 50-50% ratio of additive incubated for 6 hours while the minimum of 50.99 was recorded at 50-50% ratio of additive incubated for 3 hours. Dependence of crude protein content on incubation duration was also reported [25].

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International Journal of Science and Advanced Technology (ISSN 2221-8386) http://www.ijsat.com

Volume 1 No 8 October 2011

Table 1. The design matrix and obtained results₣ No.

Coded A pepper:tomato

Actual A pepper:tomato

Coded B Incubation time (hr)

1 -1 60:40 -1 2 -1 60:40 1 3 1 40:60 -1 4 1 40:60 1 5 1.414 0:100 0 6 -1.414 100:0 0 7 0 50:50 1.414 8 0 50:50 -1.414 9 0 50:50 0 10 0 50:50 0 11 0 50:50 0 12 0 50:50 0 13 0 50:50 0 C Where C is without any treatment (control) ₣ All the values are mean of three replicates.

Actual B Incubation time (hr) 1.5 4.5 1.5 4.5 3 3 6 1 3 3 3 3 3 -

Oil yield (%)

Crude (%)

protein

24.9 ± 1.4 33.9 ± 3.8 40.6 ± 2.7 39.1 ± 6.2 48.2 ± 5.7 27.5 ± 2.5 57.2 ± 5.1 44.8 ± 6.2 32.4 ± 1.7 31.2 ± 3.1 33.0 ± 4.3 32.7 ± 5.3 33.1 ± 6.0 44.7 ± 7.1

56.04 ± 7.7 62.17 ± 8.3 67.64 ± 8.1 64.58 ± 6.7 69.39 ± 6.2 57.79 ± 8.7 70.05 ± 5.4 62.83 ± 9.1 52.36 ± 4.6 51.71 ± 6.2 50.99 ± 7.9 52.41 ± 7.3 52.93 ± 6.6 73.77 ± 8.7

Table 2. Physicochemical properties of pepper, tomato and groundnut₣



Properties

Pepper

Tomato

Unit Mass (g)

9.06 ± 1.02

42.45 ± 2.15

Groundnut 1.0 ± 0.00

Colour (A)

0.68 ± 0.01

0.40 ± 0.01

0.31 ± 0.01

Specific Gravity

0.73 ± 0.01

0.97 ± 0.00

0.86 ± 0.01

Moisture Content (%wb)

88.50 ± 4.20

91.61 ± 3.40

7.75 ±1.04

Ph

4.85 ± 0.74

4.60 ± 0.91

5.8 ± 1.04

Fat (%)

0.20 ± 0.00

0.23 ± 0.00

44.7 ± 7.10

Titratable Acidity (%)

0.51 ± 0.00

0.68 ± 0.01

2.1 ± 1.01

Fibre (%)

1.42 ± 0.01

0.71 ± 0.00

1.14 ± 0.02

Protein (%)

1.16 ± 0.01

0.83 ± 0.01

22.07 ±2.46

Carbohydrate (%)

7.56 ± 1.77

5.71 ± 1.93

24.34 ± 3.33

All the values are mean of three determinations.

The average value of the reading was 59.40 ± 7.01%. The maximum value was marginally lower than 73.77% of control. Reason for this cannot be explained. Visual illustration of the relationship (Fig. 2) revealed a polynomial relationship. The saddle point is at the centre. Analysis of variance of the data showed significant effect (p

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