ArticleFrontiers in plant science2022
Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Quality Detection for Dragon Fruit Based on the End-of-Arm Spectral Sensor of the Harvesting Robot.Foods (Basel, Switzerland) · 2026Article
- Detection of Hidden Pest Rice Weevil (Foods (Basel, Switzerland) · 2026Article
- Advances in Kiwifruit Postharvest Management: Convergence of Physiological Insights, Omics, and Nondestructive Technologies.Current issues in molecular biology · 2025Review
- Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.Heliyon · 2024Article
- Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.Food science & nutrition · 2024Article
- Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis.Scientific reports · 2024Article
- Non-destructive assessment of apple internal quality using rotational hyperspectral imaging.Frontiers in plant science · 2024Article
- Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering.Frontiers in plant science · 2024Article
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14 authors.
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Abstract
The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the
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