Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

Taotao Shi; Yuan Gao; Jingyan Song; Min Ao; Xin Hu; Wanneng Yang; Wei Chen; Yanyan Liu; Hui Feng

doi:10.1016/j.foodchem.2024.140651

July 29, 2024Food ChemistryOpen Access

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

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Key Points

VIS-NIR hyperspectral imaging accurately quantifies wheat grains non-destructively, overcoming traditional destructive analytical limitations.
Stepwise linear regression across 400 to 1700 nm wavelengths models hundreds of nutrients simultaneously, enabling rapid optical prediction.
Hyperspectral imaging combined with deep learning supports non-destructive grain analysis, suggesting strong utility for food processing tasks.

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Implication

Experimental analysis demonstrates non-destructive nutrient quantification in wheat grains via hyperspectral imaging, highlighting efficient quality screening for food processing.

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Cite This Study

Shi et al. (2024) studied this question.

synapsesocial.com/papers/68e5ea28b6db64358757e6c7 https://doi.org/10.1016/j.foodchem.2024.140651

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Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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