Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour Article Swipe
Yuling Wang
,
Chen Zhang
,
Xin‐Hua Li
,
Longzhu Xing
,
M. Lv
,
Hong-Ju He
,
Leiqing Pan
,
Xingqi Ou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14132393
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/foods14132393
This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algorithms were employed to mine the relationship between the full-range spectra (900–1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (RP = 0.9370–0.9430, RMSEP = 0.3450–0.4043%, and RPD = 3.1348–3.4998). Through a comparative evaluation of five wavelength selection techniques, 25–30 optimal wavelengths were identified, enabling the development of optimized SVR models. The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (RP = 0.9190–0.9385, RMSEP = 0.3927–0.5743%, and RPD = 3.0424–3.2509). The model’s robustness was confirmed through external validation and statistical analyses (p > 0.05 for F-test and t-test). The results highlight the effectiveness of micro-NIRS combined with iWOA-SVR for the nondestructive gluten quality assessment of wheat flour, providing a more valuable reference for expanding the use of NIRS technology and developing portable specialized NIRS equipment for industrial-level applications in the future.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods14132393
- https://www.mdpi.com/2304-8158/14/13/2393/pdf?version=1751948403
- OA Status
- gold
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412093537
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412093537Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods14132393Digital Object Identifier
- Title
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Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat FlourWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-07Full publication date if available
- Authors
-
Yuling Wang, Chen Zhang, Xin‐Hua Li, Longzhu Xing, M. Lv, Hong-Ju He, Leiqing Pan, Xingqi OuList of authors in order
- Landing page
-
https://doi.org/10.3390/foods14132393Publisher landing page
- PDF URL
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https://www.mdpi.com/2304-8158/14/13/2393/pdf?version=1751948403Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2304-8158/14/13/2393/pdf?version=1751948403Direct OA link when available
- Concepts
-
Gluten, Wheat gluten, Quality (philosophy), Computer science, Algorithm, Machine learning, Artificial intelligence, Food science, Chemistry, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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