ArticleFrontiers in plant science2023
Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 29 citations in OpenAlex.
- Rapid and Non-Destructive Detection of Moisture Content in Dried Areca Nuts Based on Near-Infrared Spectroscopy Combined with Machine Learning.Foods (Basel, Switzerland) · 2026Article
- Study on Quality Detection Methods for Table Grapes Based on Spectral and Imaging Information.Sensors (Basel, Switzerland) · 2026Article
- Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses.Plant biotechnology journal · 2026Article
- Integrating hyperspectral features and physiological parameters for quantitative estimation of fig rust severity.Frontiers in plant science · 2026Article
- Application of fermented rose-mulberry fruit composite to enhance the antioxidant capacity, flavor, and sensory characteristics of mulberry wine.RSC advances · 2025Article
- Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models.Foods (Basel, Switzerland) · 2025Article
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- From data to decisions: a paradigm shift in fruit agriculture through the integration of multi-omics, modern phenotyping, and cutting-edge bioinformatic tools.Frontiers in plant science · 2025Review
- Non-destructive analysis ofFrontiers in chemistry · 2025Article
- Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.Forestry research · 2024Article
- Non-destructive prediction of anthocyanin concentration in whole eggplant peel using hyperspectral imaging.PeerJ · 2024Article
- Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Being rich in anthocyanin is one of the most important physiological traits of mulberry fruits. Efficient and non-destructive detection of anthocyanin content and distribution in fruits is important for the breeding, cultivation, harvesting and selling of them. This study aims at building a fast, non-destructive, and high-precision method for detecting and visualizing anthocyanin content of mulberry fruit by using hyperspectral imaging. Visible near-infrared hyperspectral images of the fruits of two varieties at three maturity stages are collected. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and stacked auto-encoder (SAE) are used to reduce the dimension of high-dimensional hyperspectral data. The least squares-support vector machine and extreme learning machine (ELM) are used to build models for predicting the anthocyanin content of mulberry fruit. And genetic algorithm (GA) is used to optimize the major parameters of models. The results show that the higher the anthocyanin content is, the lower the spectral reflectance is. 15, 7 and 13 characteristic variables are extracted by applying CARS, SPA and SAE respectively. The model based on SAE-GA-ELM achieved the best performance with R
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