Combining VNIR and NIR hyperspectral imaging techniques with a data fusion strategy for the determination of fat content, acid value, and storage time of walnuts.
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| Titel: | Combining VNIR and NIR hyperspectral imaging techniques with a data fusion strategy for the determination of fat content, acid value, and storage time of walnuts. |
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| Autoren: | Zhao Z; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China., Qiu J; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China., Liu X; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China., Chen M; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; National&Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China., Cao W; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China., Chang S; College of Life Sciences and Food Engineering, Hebei University of Engineering, 19 Taiji Road, Handan, Hebei 056000, China. Electronic address: keyancsm@126.com., Zhao X; College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University, Baoding 071002, China. Electronic address: zhaoxinzj@hbu.edu.cn. |
| Quelle: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2025 Dec 15; Vol. 343, pp. 126355. Date of Electronic Publication: 2025 Jun 06. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE |
| Imprint Name(s): | Publication: Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994- |
| MeSH-Schlagworte: | Juglans*/chemistry , Hyperspectral Imaging*/methods , Fats*/analysis , Food Storage* , Acids*/analysis , Nuts*/chemistry, Spectroscopy, Near-Infrared/methods ; Least-Squares Analysis ; Support Vector Machine |
| Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fat content and acid value are critical indicators for evaluating walnut quality. This study employs two hyperspectral imaging techniques (visible near-infrared (VNIR) and NIR) in combination with low-level and mid-level fusion strategies (LLF and MLF) for the prediction of these indicators across different storage periods and classify walnuts accordingly. Prediction models, including partial least squares regression (PLSR), particle swarm optimization-support vector regression (PSO-SVR), and random forest (RF), were developed after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The MLF strategy was best in predicting fat content based on RF combined with uninformative variable elimination (UVE), achieving an R (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Acid value; Data fusion; Fat content; Hyperspectral imaging; Storage period classification; Walnut |
| Substance Nomenclature: | 0 (Fats) 0 (Acids) |
| Entry Date(s): | Date Created: 20250607 Date Completed: 20250716 Latest Revision: 20250716 |
| Update Code: | 20250717 |
| DOI: | 10.1016/j.saa.2025.126355 |
| PMID: | 40482530 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Fat content and acid value are critical indicators for evaluating walnut quality. This study employs two hyperspectral imaging techniques (visible near-infrared (VNIR) and NIR) in combination with low-level and mid-level fusion strategies (LLF and MLF) for the prediction of these indicators across different storage periods and classify walnuts accordingly. Prediction models, including partial least squares regression (PLSR), particle swarm optimization-support vector regression (PSO-SVR), and random forest (RF), were developed after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The MLF strategy was best in predicting fat content based on RF combined with uninformative variable elimination (UVE), achieving an R <subscript>p</subscript><sup>2</sup> of 0.8706, an RMSEP of 0.0083, and an RPD of 2.7797. The LLF strategy showed optimal performance in predicting acid value based on PSO-SVR combined with uninformative variable elimination-competitive adaptive reweighted sampling (UVE-CARS), achieving an R <subscript>p</subscript><sup>2</sup> of 0.9694, an RMSEP of 0.0369, and an RPD of 5.7202. Storage period classification achieved 100 % accuracy for walnuts stored for 6 and 18 months using both VNIR and NIR spectral data. These findings highlight the great potential of hyperspectral imaging in combination with data fusion for rapid, nondestructive walnut quality assessment and storage period identification.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
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| ISSN: | 1873-3557 |
| DOI: | 10.1016/j.saa.2025.126355 |