ArticleFoods (Basel, Switzerland)2025
Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents.
Article in Foods (Basel, Switzerland), 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.
- Sulfur Dioxide in Food: A Review of Its Preservative Mechanisms, Safety Concerns, and the Rise of Smart Detection Systems.Comprehensive reviews in food science and food safety · 2026Review
- Research progress of polysaccharide detection methods in traditional Chinese medicine.Chinese herbal medicines · 2026Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
The lily, valued for its edibility and medicinal properties, is rich in essential nutrients. However, storage conditions and sulfur fumigation during processing can degrade key nutrients like polysaccharides, phenols, and sulfur dioxide. To address this, we applied a deep learning model combined with hyperspectral imaging for the rapid prediction of nutrient quality. The CLSTM (convolutional neural network-long short-term memory) model, utilizing variable combination population analysis (VCPA) for wavelength selection, effectively differentiated sulfur fumigation patterns in lilies. In terms of nutrient content prediction, the CLSTM model combined with full-wavelength data demonstrated superior performance, achieving an R
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Registered trials
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