Interpretable machine learning models to predict cadmium in wheat for safe production and soil management.

Qi-Xin Lü, Zhi-Xian Tang, Zhong Tang, Ge Dong, Zhong-Rui Xu, Fang-Jie Zhao, Peng Wang

Abstract read

In one paragraph

Article in Fundamental research, 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.

Integrating Blockchain Traceability and Deep Learning for Risk Prediction in Grain and Oil Food Safety.Foods (Basel, Switzerland) · 2026
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

7 authors.

Qi-Xin LüCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Zhi-Xian TangCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Zhong TangCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Ge DongCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Zhong-Rui XuCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Fang-Jie ZhaoCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Peng WangCenter for Agricultural and Environmental Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of cadmium (Cd) concentrations in wheat grain is essential for ensuring food safety and sustainable agriculture. Here, we developed a predictive model using nine machine learning (ML) algorithms based on a dataset of 1,339 soil-wheat grain pairs, with a focus on soil properties. The results showed that the eXtreme Gradient Boosting (XGBoost) model outperformed others, achieving superior predictive accuracy (

Indexed as

CadmiumMachine learningPrediction modelSoil thresholdsWheat

Identifiers

PMID42272492

DOI10.1016/j.fmre.2025.05.001

PMCPMC13247447

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LicenceCC BY

Read underepoch 390

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.