Multi-sensor spectral fusion to model grape composition using deep learning
Metadata
Show full item recordAuthor
Moreno Gutiérrez, SalvadorEditorial
Elsevier
Materia
Multi-block Chemometrics Spectrometer Multilayer perceptron Spectroscopy Amino acids Nitrogen compounds
Date
2023-06-02Referencia bibliográfica
S. Gutiérrez et al. Multi-sensor spectral fusion to model grape composition using deep learning. Information Fusion 99 (2023) 101865[https://doi.org/10.1016/j.inffus.2023.101865]
Sponsorship
Spanish State Research Agency through project PID2019-105381GA-I00 (iScience); Open access charge: Universidad de Granada / CBUAAbstract
Spectral instruments can be useful for the rapid assessment of chemical compounds in different targets, and
their use have been already reported for the modeling of grape composition comparing two spectral ranges.
Still, with the increased easiness of acquiring data with several sensors, it would be valuable to explore
spectral fusion techniques for the modeling with deep learning, seeking to obtain improved performance.
Therefore, the objective of this work was to develop multi-sensor spectral fusion approaches for the deep
learning modeling of grape composition. From 128 grape samples, two spectra per sample were acquired from
two different ranges using two sensors (visible and shortwave near infrared, 570–1000 nm; and wider NIR
1100–2100 nm). From each sample, 15 grape nitrogen compounds were analyzed by wet chemistry. Three
different data fusion approaches are defined using neural networks and deep learning, testing several ways of
structuring and merging the input spectra. Statistical analyses supported that (i) the proposed deep learning
fusion architectures performed better than single spectral range models, and (ii) neural networks have better
modeling capabilities than partial least squares in spectral fusion. The results demonstrate the potential of
deep learning for spectral data fusion in grape nitrogen composition regression, and potentially other traits in
food and agriculture spectroscopy