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Research, Society and Development, 8(9), p. 163984848, 2020

DOI: 10.33448/rsd-v9i8.4848

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Processing and physicochemical characterization of a water-soluble extract based on Pará nut (Bertholletia excelsa) and baru nut (Dipteryx alata Vogel)

This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

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Abstract

Despite the nutritional and functional qualities of Pará nut (Bertholletia excelsa) and of Baru nut (Dipteryx alata Vogel), the industrial exploitation of these two nuts is still scarce. With this, the elaboration of a water-soluble extract based on Pará and Baru nuts could be consider an alternative for the consumption of foods based on these vegetables, both little used industrially. Thus, by way of a simplex centroid experimental mixture design, this study aimed to elaborate water-soluble vegetable extracts based on Pará and Baru nuts. This study allowed evaluate the behavior of the physicochemical and physical characteristics of the processed vegetable extracts. The special cubic model obtained the best fit for the response variables, both for the physicochemical characterizations of moisture, lipid, protein, ash, carbohydrate, pH value and acidity, and for the physical characteristics of luminosity, chroma and hue angle. The results obtained showed that the model is highly predictive (all R2 > 97%). The water content directly influenced the nutritional aspects of the water-soluble extracts, where a decrease in the proportion of water in the mixture provided greater content of proximate composition. The behavior of the color parameter predicted by the model showed that variations in the proportions of the nuts concentrations changes the behavior of the luminosity indexes. The pH had higher values when there was an increase in the concentration of Pará nuts, while an increase in the proportion of baru nuts decreased the acidity of the water-soluble extract. The mathematical models used allow to evaluate the effects of the interactions between variables and responses, highlighting their applicability in the food industry.