Machine-Learning-Algorithm-Assisted Portable Miniaturized NIR Spectrometer for Rapid Evaluation of Wheat Flour Processing Applicability.

Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

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 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Multi-Harvest Assessment of Physicochemical and Rheological Quality of Wheat Flours From Different Processors in Southern Brazil.Journal of food science · 2026
Article
Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour.Foods (Basel, Switzerland) · 2025
Article

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5 · Who and what money

Authors and funding

8 authors.

Yuling WangSchool of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China.

Chen ZhangSchool of Information Engineering, Xinxiang Institute of Engineering, Xinxiang 453700, China.

Xinhua LiSchool of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China.

Longzhu XingSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.

Mengchao LvSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.

Hongju HeSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.ORCID 0000-0001-7112-5909

Leiqing PanCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.ORCID 0000-0001-5587-3544

Xingqi OuSchool of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China.

Funding

Central China Scholars Program 244000510003Henan Institute of Science and Technology Project 2021410707000060, 2024410707000136Henan Province Agricultural Varieties Joint Research Program 2022010101Henan Province Science and Technology Project 221100110300, 242102210105

6 · The paper itself

Abstract

In this investigation, we established an intelligent computational framework comprising a novel starfish-optimization-algorithm-optimized support vector regression (SOA-SVR) model and a multi-algorithm joint strategy to evaluate the processing applicability of wheat flour in terms of sedimentation value (SV) and falling number (FN) using near-infrared (NIR) data (900-1700 nm) obtained using a miniaturized NIR spectrometer. By employing an improved whale optimization algorithm (iWOA) coupled with a successive projections algorithm (SPA), we selected the 20 most informative wavelengths (MIWs) from the full range spectra, allowing the iWOA/SPA-SOA-SVR model to predict SV with correlation coefficient and root-mean-square errors in prediction (R

Indexed as

falling numbermachine learningNIRsedimentation valuewheat flour

Identifiers

PMID40428578

DOI10.3390/foods14101799

PMCPMC12111035

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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.