Application of response surface methodology for extraction optimization of germinant pumpkin seeds protein
Introduction
The seeds of plants affiliated to the family of Cucurbitaceae produce a number of proteins and peptides (Wang & Ng, 2003). Pumpkin (Cucurbita moschata) has received considerable attention in recent years because of the nutritional and health protective value of the proteins from the seeds. Fluted pumpkin seed flours were used as protein supplements in a variety of local foods (Giami and Bekebain, 1992, Sunday and Issac, 1999). A number of different basic polypeptides, isolated from the soluble and cell wall-derived fractions of seeds of pumpkin (Cucurbita maxima), have antifungal activity (Vassiliou, Neumann, Condron, & Polya, 1998). Arginine–glutamate-rich protein (Ng, Parkash, & Tso, 2002) from brown pumpkin seeds was demonstrated to inhibit translation in the rabbit reticulocyte lysate system. In vitro protein digestibility of bread improved when pumpkin seed proteins were added (El-Soukkary, 2001). Preliminary investigations in our laboratories showed that germinant pumpkin seeds reduced blood glucose levels of alloxan-diabetic rats but fresh pumpkin seeds did not (Li et al., 2003, Cai et al., 2003).
The protein content of the pumpkin varies from 24.5% to 36.0% (Jin Bo Dong and Huiru, 1995, Sunday and Issac, 1999, Wang et al., 2001) in different regions of the world. Arginine–glutamate-rich proteins from fresh seeds of ripe brown pumpkins was extracted in 10 mM Tris–HCl buffer, and pH 7.2 (Ng et al., 2002). Basic antifungal protein from seeds of pumpkin (Cucurbita maxima) was extracted with 1 M NaCl in 10 mM phosphate (Na+, pH 8.0) (Vassiliou et al., 1998), but there are few studies on extraction conditions of germinant pumpkin seed protein. The solubility of a protein, as well as its functionality as a nutritional ingredient, may be affected by various parameters, such as pH, temperature, ionic force, salt or solvent type, extraction time, solid–solvent ratio and presence of components causing linking (Mizubuti et al., 2000).
When many factors and interactions affect desired response, response surface methodology (RSM) is an effective tool for optimizing the process (Triveni, Shamala, & Rastogi, 2001). As the needed information about the shape of the response surface is applied, RSM is an effective statistical method that uses a minimum of resources and quantitative data from an appropriate experimental design to determine and simultaneously solve a multivariate equation (Kalaimahan & Tapobrata, 1995). Response surface experiments attempt to identify the response that can be thought of as a surface over the explanatory variables’ experimental space. It usually uses an experimental design such as central-composite experimental design (CCED) to fit an empirical, full second-order polynomial model. A central-composite experimental design, coupled with a full second-order polynomial model, is a very powerful combination that usually provides an adequate representation of most continuous response surfaces over a relatively broad factor domain (Deming, 1990).
The purpose of the present work was to optimize and to study the effect of liquid:solid ratio, NaCl concentration and reaction time for the production of protein from germinant pumpkin seeds.
Section snippets
Extraction of protein
Flayed seeds of the brown pumpkin (0.1 kg) were pulverized after culturing to germinate at 35 °C for four days and NaCl solution, at the established volume and concentration, was added and then homogenized at the established time. To the supernatant obtained after centrifugation, five volumes of acetone were added and left for 8 h at 5 °C. Then the precipitation was collected and freeze-dried. The soluble protein content was then determined in duplicate.
Methods
Association of Official Analytical
Fitting the models
The application of RSM yields the following regression equation, which is an empirical relationship between protein yield and the test variable in coded units, as given in the following equation:
Each of the observed values, Y0, is compared with the predicted value, Yi calculated from the model, as depicted in Fig. 1. We can see that Y0 accords with Yi.
The significance of each coefficient was
Conclusions
The production of protein from germinant pumpkin seeds was optimized using ‘Statsoft Statistica’ version 5.5 software. The three independent variables involved in the optimisation are liquid:solid ratio (x1), NaCl concentration (x2) and reaction time (x3). The Student t test and p value indicated that the variable with the largest effect was the liquid:solid ratio (x1). This is followed by the quadratic effect of liquid:solid ratio (x1x1) and interaction effect of liquid:solid ratio and NaCl
Acknowledgements
We gratefully acknowledge the financial support received in the form of a research Grant (Project No: 31070664) from the National natural Science Foundation of China.
References (18)
- et al.
Purification and characterization of moschins, arginine–glutamate-rich proteins with translation-inhibiting activity from brown pumpkin (Cucurbita moschata) seeds
Protein Expression and Purification
(2002) - et al.
Optimised production and utilization of exopolysaccharide from Agrobacterium radiobacter
Process Biochemistry
(2001) - et al.
Purification and mass spectrometry – assisted sequencing of basic antifungal proteins from seeds of pumpkin (Cucurbita maxima)
Plant Science
(1998) - et al.
Isolation of cucurmoschin, a novel antifungal peptide abundant in arginine, glutmate and glycine residues from black pumpkin seeds
Peptides
(2003) - Amin, Nor Aishah Saidina., & Anggoro, Didi Dwi. (2004). Optimization of direct conversion of methane to liquid fuels...
- Association of Official Analytical Chemists (1990). Official methods of analysis. Washington, DC: Association of...
Quality by design-part 5
Chemtech
(1990)- Dong, Jin Bo., & Huiru. (1995). Differences between various-maturity seeds of cucurbita ficifolia and their germinative...
Evaluation of pumpkin seed products for bread fortification
Plant Foods for Human Nutrition
(2001)
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