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OPTIMIZATION OF PROTEIN CONCENTRATE PREPARATION FROM BAMBARA BEAN USING RESPONSE SURFACE METHODOLOGY
Author(s) -
MUNE MARTIN ALAIN MUNE,
MBOME ISRAËL LAPE,
MINKA SAMUEL RENE
Publication year - 2010
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.2008.00281.x
Subject(s) - response surface methodology , yield (engineering) , lysine , chemistry , food science , legume , ingredient , central composite design , extraction (chemistry) , chromatography , biochemistry , botany , biology , amino acid , materials science , metallurgy
Response surface methodology was used to optimize the preparation of protein concentrate from Bambara bean flour. A central composite rotatable design of experiments was used to investigate the effects of the two statistically significant factors, namely pH and NaCl concentration on four responses: yield (%), protein content (%), reactive lysine (g / 16 g N) and iron content (mg / 100 g). A second‐order polynomial model was used for predicting the responses. Regression analysis indicated that more than 81.7% of the variation was explained by the fitted models. The results showed that under optimum conditions (pH and NaCl concentration of 8.99 and 0.17 M, respectively) the yield was ≥ 28%, protein content ≥ 71.25%, reactive lysine ≥ 1.77 g / 16 g N and iron content ≥ 18 mg / 100 g. The suitability of the model employed was confirmed by the agreement between the experimental and predicted values for yield, protein content and reactive lysine.PRACTICAL APPLICATIONS This study was undertaken to find out the optimum input conditions (pH and NaCl concentration) at which protein concentrate yield, protein content, reactive lysine and iron content from Bambara bean had the optimal productivity. The results obtained will permit the development of an appropriate process for protein extraction from this under‐utilized grain legume. It would then be possible to efficiently predict the conditions of pH and NaCl concentration, which permit the obtaining of protein extracts with desired nutritional quality for use as a food ingredient. Furthermore, the protein extracts could turn out to have some functional properties required in food product development.