Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires sam...

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Bibliographic Details
Main Authors: Deriu, M.A (Author), Gucciardi, A. (Author), Martos, V.M (Author), Michelucci, U. (Author), Sperti, M. (Author), Venturini, F. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2023
Subjects:
00–01
99–00
Artificial intelligence
Chemical analysis
Convolution
Convolutional neural network
Convolutional neural networks
Costs
Extraction
Fluorescence
Fluorescence spectra
Fluorescence spectroscopy
Low-costs
Machine learning
Machine-learning
Neural network application
Olive oil
Optical sensor
Physicochemical property
Production cycle
Quality assurance
Quality control
Quality control 2010 MSC
Time series analysis
Ultraviolet spectroscopy
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