ArticleFood science & nutrition2025
Nondestructive Evaluation of Soluble Solid Content of Cucumbers Based on VIS-NIR and SWIR Hyperspectral Images.
Article in Food science & nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- CounterfeitFood science & nutrition · 2026Article
- Next-Generation Harvester Technologies: Synergizing Smart Grading and Biomechanical Damage Control in Mechanized Tomato Production.Sensors (Basel, Switzerland) · 2026Review
- Nondestructive Evaluation of Soluble Solid Content of Cucumbers Based on VIS-NIR and SWIR Hyperspectral Images.Food science & nutrition · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
Soluble solid content (SSC) is a key indicator for evaluating cucumber quality, directly influencing its commercial value. In China's cucumber sorting factories, SSC is typically assessed using random sampling and destructive methods, which are unsuitable for large-scale and continuous detection. Therefore, this study employs hyperspectral imaging technology to evaluate the capability of visible-near infrared (VIS-NIR) and shortwave infrared (SWIR) spectroscopy for nondestructive SSC detection. In the experiment, hyperspectral data of cucumbers at different growth stages were collected in the VIS-NIR and SWIR. Using a partial least squares regression (PLSR) model, SSC prediction performance was compared across three spectral preprocessing methods and three sensitive wavelength selection methods. The optimal prediction models for SSC in the VIS-NIR and SWIR spectral ranges were established. The results showed that the optimal model in the VIS-NIR was the savitzky-golay smoothing (SG)-fullwave-PLSR model, with an R
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