Evidence map›Paper›PMID 39296098›Full record

ArticleHeliyon2024

Predicting the properties of wheat flour from grains during debranning: A machine learning approach.

Seokho Kang, Yonggik Kim, Oladayo S Ajani, Rammohan Mallipeddi, Yushin Ha

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Seokho KangDepartment of Bio-industrial Machinery Engineering, College of Agriculture and Life Sciences, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Yonggik KimDepartment of Bio-industrial Machinery Engineering, College of Agriculture and Life Sciences, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Oladayo S AjaniDepartment of Artificial Intelligence, College of IT Engineering, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Rammohan MallipeddiDepartment of Artificial Intelligence, College of IT Engineering, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Yushin HaDepartment of Bio-industrial Machinery Engineering, College of Agriculture and Life Sciences, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the food industry, meeting food quality demands is challenging. The quality of wheat flour, one of the most commonly used ingredients, depends on the extent of debranning done to remove the aleurone layer before milling. Therefore, the end product management can be simplified by predicting the properties of wheat flour during the debranning stage. Therefore, the chemical and rheological properties of grains were analyzed at different debranning durations (0, 30, 60 s). Then the images of wheat grain were taken to develop a regression model for predicting the chemical quality (i.e., ash, starch, fat, and protein contents) of the wheat flour. The resulting regression model comprises a convolutional neural network and is evaluated using the coefficient of determination (

Indexed as

Ash contentChemical propertyFood qualityPreprocessingRegression model

Identifiers

PMID39296098
PMCPMC11408036

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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.