Evidence map›Paper›PMID 41657720›Full record

ArticleCurrent research in food science2026

Integrated hyperspectral-RGB modeling for the estimation of wheat protein and dough rheological properties using machine and deep transfer learning.

Shaohua Zhang, Qianya Cheng, Tiantian Wang, Changjun Dai, Jichun Tian, Wei Feng, Chenyang Wang, Tiancai Guo, Li He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Shaohua ZhangCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Qianya ChengCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Tiantian WangCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Changjun DaiHeilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, 150000, China.
Jichun TianCollege of Agronomy, Shandong Agricultural University, Taian, Shandong, 271018, China.
Wei FengCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Chenyang WangCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Tiancai GuoCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.
Li HeCollege of Agronomy, Henan Agricultural University/State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Zhengzhou, Henan, 450046, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid non-destructive estimation of wheat protein properties and dough rheological characteristics is of great significance for ensuring processing quality, food safety, and variety classification. This study established a multi-source remote sensing framework integrating hyperspectral and red-green-blue (RGB) data. By extracting wavelet features (WFs) and color indices (CIs), and applying Pearson correlation analysis combined with the successive projections algorithm-variance inflation factor (SPA-VIF) for feature selection, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and attention-based multi-task learning (MTL-AM) were employed to predict wheat protein and rheological traits. The results showed that the fusion of wavelet features with RGB images significantly improved model prediction accuracy, and that the estimation performance of XGBoost and MTL-AM was significantly higher than that of PLSR. To further improve the estimation accuracy of the farinograph quality number (FQN), the combined MTL-AM-XGBoost model achieved the highest accuracy (R

Indexed as

Attention mechanismMulti-source data fusionMulti-task learningPearson-SPA-VIFWheat quality

Identifiers

PMID41657720
PMCPMC12882710

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.