ArticleFoods (Basel, Switzerland)2020
Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Gamma irradiation as a grape pomace stabilisation treatment: Assessment of phenolic and mineral preservation.Food chemistry: X · 2026Article
- Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model.Foods (Basel, Switzerland) · 2024Article
- Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics.Foods (Basel, Switzerland) · 2022Article
- Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination ofSensors (Basel, Switzerland) · 2022Article
- Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network.Frontiers in plant science · 2021Article
- Application of Ultraviolet-Visible Absorption Spectroscopy with Machine Learning Techniques for the Classification of Cretan Wines.Foods (Basel, Switzerland) · 2020Article
- Application and interpretation of deep learning methods for the geographical origin identification ofRSC advances · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (
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Registered trials
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