Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning

Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning

Spectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models...

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Bibliographic Details
Main Authors: Jean Frederic Isingizwe Nturambirwe, Eslam A. Hussein, Mattia Vaccari, Christopher Thron, Willem Jacobus Perold, Umezuruike Linus Opara
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Foods
Subjects:
variable selection
model optimisation
defect classification
machine learning
baseline
uncertainty quantification
Online Access:https://www.mdpi.com/2304-8158/12/1/210

Internet

https://www.mdpi.com/2304-8158/12/1/210

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