ArticleFoods (Basel, Switzerland)2023
Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Non-Destructive Detection of Internal Quality of Sanhua Plum Based on Multi-Source Information Fusion.Foods (Basel, Switzerland) · 2026Article
- Recent advancements on innovative technologies and applications for non-destructive monitoring of tomato quality.Frontiers in plant science · 2026Review
- Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging.Foods (Basel, Switzerland) · 2025Article
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology.Foods (Basel, Switzerland) · 2024Article
- Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.Foods (Basel, Switzerland) · 2024Article
- Optimisation of an Industrial Optical Sorter of Legumes for Gluten-Free Production Using Hyperspectral Imaging Techniques.Foods (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R
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Registered trials
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