Integration of physicochemical and instrumental quality data to estimate the texture of polished rice

Author:

Bento Juliana Aparecida Correia1ORCID,Silva Marília Araújo2,Rosário Neto Antônio3ORCID,Souza Neto Menandes Alves de2ORCID,Narciso Marcelo Gonçalves4ORCID,Colombari Filho José Manoel4,Bassinello Priscila Zaczuk5ORCID

Affiliation:

1. Universidade Federal de Mato Grosso, Brazil; Universidade Federal de Goiás (UFG), Brazil

2. Universidade Federal de Goiás (UFG), Brazil

3. Universidade Federal de Lavras (UFLA), Brazil

4. Empresa Brasileira de Pesquisa Agropecuária Arroz e Feijão (EMBRAPA), Brazil

5. Empresa Brasileira de Pesquisa Agropecuária Alimentos e Territórios (EMBRAPA), Brazil

Abstract

ABSTRACT: This research evaluated different rice genotypes regarding physicochemical and instrumental parameters of grain quality and associated the data with sensory analysis to support the creation of rules for classification of the culinary quality of rice (texture), based on isolated or combined parameters. The combination of amylose content and gelatinization temperature was able to predict the rice quality. According to the sensorial panel, the instrumental stickiness was able to segregate rice with very low amylose content or waxy to the other ones. Regarding pasting properties, rice that presented high final viscosity (310-480 RVU), setback (165-245 RVU), and pasting temperature (78 - 88 °C), and low values for breakdown (15-120 RVU), associated with a high stickiness (>-5N) was desirable by the Brazilian consumers. The classification rules created through the relationship between the physicochemical parameters and the texture profile evaluated by the sensory panel will help to verify the culinary profile of the rice samples (through free software), which makes it easier to predict the probability of rice meeting the desired quality standards.

Publisher

FapUNIFESP (SciELO)

Subject

General Veterinary,Agronomy and Crop Science,Animal Science and Zoology

Reference30 articles.

1. AACC International Approved Methods,2012

2. XLSTAT statistical and data analysis solution,2021

3. Rice grain protein composition influences instrumental measures of rice cooking and eating quality.;BALINDONG J. L.;Journal of Cereal Science,2018

4. Encyclopedia of Food Grains (Second Edition).;BAO J. S.,2016

5. Prediction models of rice cooking quality;BORRIES G. V.;Cereal Chemistry,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3