Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression.

Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Detection of Rice Prolamin and Glutelin Content Using Hyperspectral Imaging Combined with Feature Selection Algorithms and Multivariate Regression Models.Foods (Basel, Switzerland) · 2025
Article

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5 · Who and what money

Authors and funding

5 authors.

Zhongjie TangSchool of Information Engineering, Huzhou University, Huzhou 313000, China.

Shanlin MaInstitute of Crop Science, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China.

Hengnian QiSchool of Information Engineering, Huzhou University, Huzhou 313000, China.ORCID 0000-0002-1927-7160

Xincheng ZhangInstitute of Crop Science, Huzhou Academy of Agricultural Sciences, Huzhou 313000, China.

Chu ZhangSchool of Information Engineering, Huzhou University, Huzhou 313000, China.ORCID 0000-0001-6760-3154

Funding

Huzhou Public Welfare Project 2022GZ27Research Initiation Projects of Huzhou Academy of Agricultural Sciences Not Applicable

6 · The paper itself

Abstract

The brown rice rate (BRR), milled rice rate (MRR), and head rice rate (HRR) are important indicators of rice milling quality. The simultaneous detection of these three metrics holds significant economic value for rice milling quality assessments. In this study, hyperspectral imaging was employed to estimate the rice milling quality attributes of two rice varieties (Xiushui121 and Zhehujing26). Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Networks (CNNs), and Backpropagation Neural Networks (BPNNs) were used to establish both single-task and multi-task models for the prediction of milling quality attributes. Most multi-task models demonstrated a higher prediction accuracy compared with their corresponding single-task models. Among single-task models, BPNNs outperformed the others in predicting BRR and HRR, with correlation coefficients (

Indexed as

brown rice ratehead rice ratehyperspectral imagingmilled rice ratemulti-task learning

Identifiers

PMID40509505

DOI10.3390/foods14111977

PMCPMC12155452

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LicenceCC BY

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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.