TITLE:
Optimization of Protein Content and Dietary Fibre in a Composite Flour Blend Containing Rice (Oryza sativa), Sorghum [Sorghum bicolor (L.) Moench] and Bamboo (Yushania alpine) Shoots
AUTHORS:
Wafula Nobert Wanjala, Omwamba Mary, Mahungu Symon
KEYWORDS:
Optimization, Protein, Dietary Fibre, Bamboo Shoots, Mixture Analysis
JOURNAL NAME:
Food and Nutrition Sciences,
Vol.11 No.8,
August
24,
2020
ABSTRACT: Initiatives on tackling food insecurity among global emerging economies
are being focused on enriching native staple foods with locally available
nutritious underutilized crops. The objective of this study was to optimize
protein content and dietary fibre in rice (Oryza sativa) flour
using Sorghum (Sorghum bicolor L.) and Bamboo shoots (Yushania alpine). An extreme vertices design of mixture approach with 11 runs was employed
in the study using MINITAB® software. The 11 blends from 11 generated runs and individual ingredient
samples were analyzed for nutritional composition. Energy value and
energy-to-protein ratio for the samples was calculated. Bamboo shoots flour
(BSF) had the highest content for all proximate components except total carbohydrates
on dry weight basis. Rice had the highest content of total carbohydrates at 77.71% and energy to protein
ratio of 53.72 kcal/g. Sorghum had the highest mean total phenolic and condensed tannins of 45.512 (mg GAE/kg)
and 2.512 (mg CE/g) while rice the least with 0.042 (mg GAE/kg) and 0.102 (mg
CE/g), respectively. Fresh bamboo shoots had the highest level content of HCN of 117.81 mg/kg. Other
dried ingredients had a mean HCN content of 2.313, 1.584 and 0.066 mg/kg for
dried BSF, sorghum and rice respectively. Increasing the quantity of BSF and
sorghum flour in the blends consequentially increased the protein content, dietary
fibre and total minerals. Optimum blend was established to be 50:27:23 for
rice, sorghum and BSF, respectively. This blend had 13.4% protein, 6.2% dietary
fibre and 3.9% total minerals. Regression analysis showed that apart from dry
matter, all other constituents were significantly predictable during
optimization with R2 > 0.7530. Cluster analysis
showed that the nutritional components analyzed are in four main clusters.
Cluster 1: Dry matter and protein digestibility, cluster 2: Carbohydrates,
energy value and energy ratio, cluster 3: Protein, fibre and ash while cluster
4: Crude fat only. These findings of the optimum composite ratio and other
blends could contribute in addressing the food insecurity for low income countries.