Evidence map›Paper›PMID 41017617›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

A Cost-Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy.

Jiang Shi, Erkui Yue, Xuejin Zhu, Lin Zhao, Weifeng Chen, Xiangqun Yu, Hongliang Huang, Ying Qian, Zhengfang Zhang, Jianguo Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. 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

10 authors.

Jiang ShiInstitute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, P. R. China.
Erkui YueInstitute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, P. R. China.
Xuejin ZhuCollege of Science, Hangzhou Dianzi University, Hangzhou, 310018, P. R. China.
Lin ZhaoInstitute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, P. R. China.
Weifeng ChenSchool of Information, Zhejiang University of Finance and Economics, Hangzhou, 310018, P. R. China.
Xiangqun YuInstitute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, P. R. China.
Hongliang HuangCollege of Science, Hangzhou Dianzi University, Hangzhou, 310018, P. R. China.
Ying QianShaoxing Keqiao District Agricultural Technology Training School, Shaoxing, 312031, P. R. China.
Zhengfang ZhangCollege of Science, Hangzhou Dianzi University, Hangzhou, 310018, P. R. China.ORCID https://orcid.org/0000-0001-8973-0250
Jianguo WuCollege of Horticulture Science, Zhejiang A&F University, Hangzhou, 311300, P. R. China.

Funding

Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of China LHZQN25C130005Natural Science Foundation of Zhejiang Province LY21A010011Natural Science Foundation of Zhejiang Province LY21F020029the Science and Technology Innovation and Demonstration Extension Fund of Hangzhou Academy of Agricultural Sciences 2022HNCT-08the Science and Technology Innovation and Demonstration Extension Fund of Hangzhou Academy of Agricultural Sciences 2025HNCT-10
6 · The paper itself

Abstract

In fresh maize breeding, developing robust and accurate near-infrared (NIR) calibration models traditionally requires significant time, cost, and labor. To address these challenges, a novel machine learning approach is proposed using a Prediction-Correction Neural Network (PCNN) that enables effective modeling from small sample sets augmented with synthetic data based on NIR spectroscopy. For key quality traits such as amylopectin, protein, crude fiber, and total sugar, the PCNN achieved residual predictive deviation (RPD) values between 2.821 and 4.862, and coefficients of determination (

Indexed as

Machine LearningZea maysCalibrationCost-Benefit AnalysisNeural Networks, ComputerSpectroscopy, Near-Infraredfresh maize kernelnear‐infrared spectroscopyprediction‐correction neural networkscalabilitysmall sample set

Identifiers

PMID41017617
PMCPMC12697840

What OpenQuestion holds

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LicenceCC BY
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.