Abstract
Glycine, the simplest free amino acid. It is one of the important factors affecting the flavor of beef. In this study, a fast and non-destructive method combining near-infrared hyperspectral (900–1700 nm) and textural data was first proposed to determine the content and distribution of glycine in beef. On the basis of spectral information pre-processing, spectral features were extracted by the interval variable iterative space shrinkage approach, competitive adaptive reweighting algorithm, and uninformative variable elimination (UVE). The glycine content prediction models were established by partial least squares regression, least squares support vector machine, and the optimized shallow full convolutional neural network (SFCN). Among them, the UVE-SFCN model was found to show better results with prediction set determination coefficient (RP2) of 0.8725. Furthermore, textural features were extracted by the gray-level co-occurrence matrix and fused with the spectral information of the best feature band to obtain an optimized UVE-FSCN-fusion model (RP2 = 0.9005, root mean square error = 0.3075, residual predictive deviation = 0.2688). Compared with the full spectrum and characteristic wavelength spectrum models, RP2 was improved by 6.41% and 3.10%. The best fusion model was visualized to represent the distribution of glycine in beef. The results showed that the prediction and visualization of glycine content in beef were feasible and effective, and provided a theoretical basis for the hyperspectral study of meat quality monitoring or the establishment of an online platform.
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Data Availability
The dataset analyzed during the current study is available from the corresponding author on reasonable request.
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Funding
This work was supported by the Ningxia Hui Autonomous Region fund (2022AAC05022) and Key R & D plan of the Autonomous Region (2019BEH03002).
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All authors contributed to the study conception and design. Material preparation were performed by Yu Lv, Fujia Dong, and Sijia Liu. Data collection and analysis were performed by Yu Lv and Fujia Dong. The first draft of the manuscript was written by Yu Lv, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Yu Lv: Writing, original draft; writing, review and editing; conceptualization; formal analysis; investigation.
Fujia Dong: Data curation, formal analysis, investigation.
Jiarui Cui: Data curation, Formal analysis, methodology, software.
Jie Hao: Investigation, visualization, writing—review and editing.
Ruiming Luo: Funding acquisition, resources.
Songlei Wang: Funding acquisition, project administration, resources.
Argenis Rodas-Gonzalez: Supervision.
Sijia Liu: Validation, visualization, writing—review and editing.
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Yu Lv declares that he has no conflict of interest. Fujia Dong declares that he has no conflict of interest. Jiarui Cui declares that he has no conflict of interest. Jie Hao declares that he has no conflict of interest. Ruiming Luo declares that he has no conflict of interest. Songlei Wang declares that he has no conflict of interest. Argenis Rodas-Gonzalez declares that he has no conflict of interest. Sijia Liu declares that he has no conflict of interest.
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Lv, Y., Dong, F., Cui, J. et al. Fusion of Spectral and Textural Data of Hyperspectral Imaging for Glycine Content Prediction in Beef Using SFCN Algorithms. Food Anal. Methods 16, 413–425 (2023). https://doi.org/10.1007/s12161-022-02425-w
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DOI: https://doi.org/10.1007/s12161-022-02425-w