Evidence map›Paper›PMID 35053955›Full record

ArticleFoods (Basel, Switzerland)2022

Effects of Thickness Fraction Process on Physicochemical Properties, Cooking Qualities, and Sensory Characteristics of Long-Grain Rice Samples.

Sara E Jarma Arroyo, Terry J Siebenmorgen, Han-Seok Seo

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sara E Jarma ArroyoDepartment of Food Science, University of Arkansas, 2650 North Young Avenue, Fayetteville, AR 72704, USA.
Terry J SiebenmorgenDepartment of Food Science, University of Arkansas, 2650 North Young Avenue, Fayetteville, AR 72704, USA.
Han-Seok SeoDepartment of Food Science, University of Arkansas, 2650 North Young Avenue, Fayetteville, AR 72704, USA.ORCID 0000-0001-9845-8199

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A process of removing thinner kernels of rough rice, i.e., thickness fraction process, has been suggested as a method for increasing milling yields in the rice industry. This study aimed at determining whether physicochemical properties, cooking qualities, and sensory characteristics of rice samples could be changed by the addition of a thickness fraction into the rice process stream. Each of four long-grain rice cultivar lots was assigned into two groups: unfractionated and thickness-fractionated. For the thickness-fractionated group, thin rice kernels (<1.9 mm) of rough rice were discarded from unfractionated rice samples. Unfractionated and thickness-fractionated rice samples were compared with respect to physicochemical properties, cooking qualities, and sensory characteristics. The results showed that the removal of such thin kernels decreased the breakage and chalkiness rates and increased head rice yields. Fractionated rice samples exhibited lower amylose contents and crude protein contents but higher gelatinization temperatures than unfractionated rice samples. While the optimum cooking duration and width-expansion ratios of thickness-fractionated rice samples were higher than those of unfractionated ones, there was a negligible impact of the thickness fraction process on sensory characteristics of long-grain rice samples. In conclusion, the thickness fraction process affects physicochemical properties and cooking qualities more than the sensory characteristics of rice samples.

Indexed as

cookingfractiongradingphysicochemicalricesensorythickness

Identifiers

PMID35053955
PMCPMC8775037

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.