Nutritional composition and amino acid profile of analog rice formulated from local corn and legumes in East Nusa Tenggara, Indonesia

East Nusa Tenggara (NTT) Province relies heavily on rice, yet limited production highlights the need for food diversification using local resources such as corn and legumes. This study aimed to evaluate the chemical composition and amino acid profile of analog rice formulated from local corn, arbila beans, and cowpeas. The research consisted of three stages: flour preparation, formulation, and analog rice production. Five formulations were developed with corn:arbila:cowpea ratios of 100:0:0 (F1), 88:10:10 (F2), 70:15:15 (F3), 65:25:10 (F4), and 65:10:25 (F5). Proximate composition and amino acid profiles were analyzed using HPLC, and data were subjected to ANOVA followed by DMRT at a 5% significance level. Formulation F4 (65:25:10) demonstrated the best chemical characteristics, with 7.18% moisture, 2.44% ash, 14.63% protein, 6.22% fat, 69.55% carbohydrate, and 9.57% crude fiber. Analog rice from F4 contained 14 amino acids, including seven essential and seven non-essential, with glutamic acid, aspartic acid, lysine, and leucine being the most abundant. Lysine and leucine are vital for human nutrition, particularly in reducing childhood stunting. These findings indicate that analog rice made from local corn and legumes may serve as a sustainable protein source to enhance food security and support efforts to reduce malnutrition and stunting.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-01-15
DOI
https://doi.org/10.1051/bioconf/202621002008/pdf
Primary Topic
Food composition and properties
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nutritional composition and amino acid profile of analog rice formulated from local corn and legumes in East Nusa Tenggara, Indonesia

Paulus Risan Funan Lalong, Maria Novalina Naben, Joanivita P. G. Soru
Springer Link (Chiba Institute of Technology)
Food composition and properties
article

Nutritional composition and amino acid profile of analog rice formulated from local corn and legumes in East Nusa Tenggara, Indonesia

Paulus Risan Funan Lalong, Maria Novalina Naben, Joanivita P. G. Soru
article en

Abstract

East Nusa Tenggara (NTT) Province relies heavily on rice, yet limited production highlights the need for food diversification using local resources such as corn and legumes. This study aimed to evaluate the chemical composition and amino acid profile of analog rice formulated from local corn, arbila beans, and cowpeas. The research consisted of three stages: flour preparation, formulation, and analog rice production. Five formulations were developed with corn:arbila:cowpea ratios of 100:0:0 (F1), 88:10:10 (F2), 70:15:15 (F3), 65:25:10 (F4), and 65:10:25 (F5). Proximate composition and amino acid profiles were analyzed using HPLC, and data were subjected to ANOVA followed by DMRT at a 5% significance level. Formulation F4 (65:25:10) demonstrated the best chemical characteristics, with 7.18% moisture, 2.44% ash, 14.63% protein, 6.22% fat, 69.55% carbohydrate, and 9.57% crude fiber. Analog rice from F4 contained 14 amino acids, including seven essential and seven non-essential, with glutamic acid, aspartic acid, lysine, and leucine being the most abundant. Lysine and leucine are vital for human nutrition, particularly in reducing childhood stunting. These findings indicate that analog rice made from local corn and legumes may serve as a sustainable protein source to enhance food security and support efforts to reduce malnutrition and stunting.

Springer Link (Chiba Institute of Technology)
Ministrstvo za visoko šolstvo, znanost in tehnologijo
Zero hunger
Openalex Percentile: Top 86%
Food composition and properties
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.