ArticleFoods (Basel, Switzerland)2023
Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment.Foods (Basel, Switzerland) · 2026Review
- Hyperspectral Imaging-Based Rapid Assessment of Chinese Yam Quality for Dried Slice Processing.Foods (Basel, Switzerland) · 2026Article
- Next-Generation Harvester Technologies: Synergizing Smart Grading and Biomechanical Damage Control in Mechanized Tomato Production.Sensors (Basel, Switzerland) · 2026Review
- Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection.Foods (Basel, Switzerland) · 2026Review
- Recent advancements on innovative technologies and applications for non-destructive monitoring of tomato quality.Frontiers in plant science · 2026Review
- Molecular Mechanisms and Biomedical Applications of Ferritin Nanocages: A Comprehensive Review of Self-Assembly, Engineering, and Multifunctional Delivery Platforms.International journal of nanomedicine · 2026Review
- Design of a Portable Nondestructive Instrument for Apple Watercore Grade Classification Based on 1DQCNN and Vis/NIR Spectroscopy.Micromachines · 2025Article
- Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review.Foods (Basel, Switzerland) · 2025Review
- Principles, Applications, and Future Evolution of Agricultural Nondestructive Testing Based on Microwaves.Sensors (Basel, Switzerland) · 2025Review
- Application of Image Computing in Non-Destructive Detection of Chinese Cuisine.Foods (Basel, Switzerland) · 2025Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.Foods (Basel, Switzerland) · 2025Review
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Non-destructive analysis ofFrontiers in chemistry · 2025Article
- Assessing leaf nitrogen concentration in rice using RGB imaging: a comparative study at leaf, canopy, and plot scales.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R
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