ArticleFoods (Basel, Switzerland)2022
Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Ultrasound-Assisted Extraction of Bioactives fromFoods (Basel, Switzerland) · 2025Article
- Development and Prediction of a Non-Destructive Quality Index (Qi) for Stored Date Fruits Using VIS-NIR Spectroscopy and Artificial Neural Networks.Foods (Basel, Switzerland) · 2025Article
- Construction and optimization of quantitative analysis models for pigments in broccoli (Food chemistry: X · 2025Article
- A precise and high-throughput assay for stem structural characteristics deepens understanding of lodging resistance in sorghum.BMC plant biology · 2025Article
- Enhancing beer authentication, quality, and control assessment using non-invasive spectroscopy through bottle and machine learning modeling.Journal of food science · 2025Article
- Genome-Wide Association Study of Grape Texture Based on Puncture.International journal of molecular sciences · 2024Article
- Non-Destructive Evaluation of the Physiochemical Properties of Milk Drink Flavored with Date Syrup Utilizing VIS-NIR Spectroscopy and ANN Analysis.Foods (Basel, Switzerland) · 2024Article
- Utilizing VIS-NIR Technology to Generate a Quality Index (QFoods (Basel, Switzerland) · 2024Article
- Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS.Food chemistry: X · 2023Article
- Modeling Mathematical Relationship with Weight Loss and Texture on Table Grapes of 'Red Globe' and 'Wink' during Cold and Ambient Temperature Storage.Foods (Basel, Switzerland) · 2023Article
- Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins.Foods (Basel, Switzerland) · 2023Article
- Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors.Foods (Basel, Switzerland) · 2023Article
- Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy.Scientific reports · 2023Article
- Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters.Micromachines · 2022Article
- Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models.Foods (Basel, Switzerland) · 2022Article
- Advances in NIR Spectroscopy Analytical Technology in Food Industries.Foods (Basel, Switzerland) · 2022Article
- Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this article, a combination of non-destructive NIR spectroscopy and machine learning techniques was applied to predict the texture parameters and the total soluble solids content (TSS) in intact berries. The multivariate models obtained by building artificial neural networks (ANNs) and applying partial least squares (PLS) regressions showed a better prediction ability after the elimination of uninformative spectral ranges. A very good prediction was obtained for TSS and springiness (R
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