Evidence map›Paper›PMID 41140598›Full record

ArticleFood chemistry: X2025

MSI-FusionNet: a multi-modal spectral-image fusion network for sorghum variety identification.

Xinjun Hu, Mingkui Dai, Anjun Li, Ying Liang, Wei Lu, Jiahao Zeng, Jianheng Peng, Jianping Tian, Manjiao Chen, Liangliang Xie

Abstract read
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In one paragraph

Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Xinjun HuSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Mingkui DaiSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Anjun LiAnhui Province Key Laboratory of Intelligent Solid-state Fermentation Technology, Bozhou 236800, China.
Ying LiangAnhui Province Key Laboratory of Intelligent Solid-state Fermentation Technology, Bozhou 236800, China.
Wei LuAnhui Province Key Laboratory of Intelligent Solid-state Fermentation Technology, Bozhou 236800, China.
Jiahao ZengSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Jianheng PengSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Jianping TianSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Manjiao ChenSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.
Liangliang XieSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compositional differences among sorghum varieties influence the brewing process, flavor characteristics, and overall quality of Baijiu. This study proposes a Multi-Modal Spectral-Image Fusion Network (MSI-FusionNet) data fusion model for rapid and accurate identification of sorghum varieties. This model integrates one-dimensional spectral data obtained through hyperspectral imaging with two-dimensional image data captured using industrial microscopes. The model identifies 12 sorghum varieties with an accuracy of 93.33 %. Compared with using spectral or image data alone, MSI-FusionNet improves accuracy by 11.11 % and 29.63 %, respectively. To balance performance and efficiency, various classic 2D convolutional neural network (2DCNN) architectures were evaluated. The MSI-FusionNet model with ShuffleNetV2 as the 2DCNN structure demonstrated superior efficiency, significantly reducing model complexity and computational cost while maintaining high accuracy. MSI-FusionNet offers an efficient and accurate solution for identifying sorghum varieties for liquor enterprises, supporting the stability of Baijiu flavor and quality, and providing valuable technical support for the brewing industry.

Indexed as

MSI-FusionNetMulti-modal data fusionSorghumVariety identification

Identifiers

PMID41140598
PMCPMC12546670

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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.