Quality Assessment and Prediction of Peanut Storage Life Based on Deep Learning.

Yipeng Zhou, Xingchen Sun, Wenjing Yan, Mingwen Bi, Yiwen Shao, Kexin Chen

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. 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

6 authors.

Yipeng ZhouNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.

Xingchen SunNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.

Wenjing YanNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.

Mingwen BiBusiness School, Beijing Wuzi University, No. 321 Fuhe Street, Tongzhou District, Beijing 101149, China.

Yiwen ShaoNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.

Kexin ChenNational Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.ORCID 0009-0009-3800-8968

Funding

Beijing Scholars Program under Grant 099Beijing Wuzi University Youth Research Fund Project 2025XJQN07National Key Technology R&DProgram of China under Grant 2022YFF0606803National Natural Science Foundation of China under Grant 62433002National Natural Science Foundation of China under Grant 62476014Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions under Grant BPHR20220104

6 · The paper itself

Abstract

As a globally significant oilseed and food crop, peanuts exhibit significant quality changes influenced by storage conditions. This study monitored six key quality indicators-including fatty acid content, carbonyl content, peroxide value, acid value, phenylacetaldehyde and moisture content-in peanut samples stored for 30 weeks under varying temperature and humidity conditions. A Deep Clustering Network (DCN) was employed for quality grading, yielding superior results compared to Deep Empirical Correlation (DEC) and K-Means++ clustering methods, thereby establishing effective quality grading standards. Building upon this, a D-SCSformer time series prediction model was constructed to forecast quality indicators. Through dimensionality-segmented embedding and statistical feature fusion, it achieved strong predictive performance (MSE = 0.2012, MAE = 0.2884, RMSE = 0.4387, and R

Indexed as

deep clusteringdeep learningmultivariate time seriespeanutsquality gradingwarehouse management

Identifiers

PMID41683034

DOI10.3390/foods15030446

PMCPMC12896682

What OpenQuestion holds

Textmetadata

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.