Development and Kohonen neural network based cluster analysis of red rice cookies enriched with blends of different
1Department of Biotechnology, Jaypee Institute of Information Technology, Noida, India.
Abstract:
This study aimed to prepare cookies using red rice flour (RRF) and edible seed flour (ESF) and analyze their nutritional and physical characteristics using exploratory methods. Effect of blended mixtures were investigated with varying levels of ESF to RRF in mass incremental ratios of 0 %:100 %, 25 %:75 %, 50 %:50 %, 75 %:25 % and 100 %:0 %. According to one-way analysis of variance (ANOVA), fortified cookies contained significantly higher levels (p < 0.05) of protein, fibers; with reduced fat content and spread-ratio. Results were analyzed using Kohonen's Self-Organizing Maps (KSOMs), which grouped samples based on similar nutritional and physical properties. Analysis yielded desirable Davies-Bouldin (0.14-0.30) and Silhouette indices (0.97-0.98), indicating well-separated clusters with minimal overlap. Cookies blend containing 25 % ESF+ 75 % RRF exhibited higher sensory attributes, minerals, amino-acids and antioxidants compared to control cookies, hence demonstrating ESF and RRF as promising functional ingredients to improve nutritional quality.
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