An exploratory study for the technological ...
Document type :
Article dans une revue scientifique: Article original
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Title :
An exploratory study for the technological classification of egg white powders based on infrared spectroscopy
Author(s) :
Grassi, Silvia [Auteur]
Università degli Studi di Milano = University of Milan [UNIMI]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Alamprese, Cristina [Auteur]
Università degli Studi di Milano = University of Milan [UNIMI]
Università degli Studi di Milano = University of Milan [UNIMI]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Alamprese, Cristina [Auteur]
Università degli Studi di Milano = University of Milan [UNIMI]
Journal title :
LWT
Volume number :
96
Pages :
469-475
Publication date :
2018-10
HAL domain(s) :
Chimie/Chimie théorique et/ou physique
English abstract : [en]
This work aims at the evaluation of FT-NIR and FT-IR spectroscopy as rapid, easy, and cost-effective tools for the classification of egg white powder (EWP) based on its technological properties. Up to 100 commercial ...
Show more >This work aims at the evaluation of FT-NIR and FT-IR spectroscopy as rapid, easy, and cost-effective tools for the classification of egg white powder (EWP) based on its technological properties. Up to 100 commercial spray-dried EWP samples with known gelling and foaming properties were used to acquire FT-NIR and FT-IR spectra. An appropriate data-splitting algorithm (Duplex) was applied in order to create, for each dataset, a calibration set and a representative validation test set for prediction. Different spectral pre-treatments and their combinations were investigated for the calculation of Partial Least Squares–Discriminant Analysis models in order to classify samples according to gel strength, foam height, and foam instability. A variable selection strategy based on the so-called Variable Importance in Projection scores was also evaluated. Both FT-NIR and FT-IR spectroscopy showed good potential in discriminating EWP samples with different technological properties. Correct classification percentages in prediction ranging from 59% to 89% were obtained with the best models calculated with selected wavenumbers. These results show a promising industrial perspective, demonstrating the possibility of developing cheap and fast instruments spanning a limited spectral range, which can be implemented on the production lines for EWP sorting and quality control.Show less >
Show more >This work aims at the evaluation of FT-NIR and FT-IR spectroscopy as rapid, easy, and cost-effective tools for the classification of egg white powder (EWP) based on its technological properties. Up to 100 commercial spray-dried EWP samples with known gelling and foaming properties were used to acquire FT-NIR and FT-IR spectra. An appropriate data-splitting algorithm (Duplex) was applied in order to create, for each dataset, a calibration set and a representative validation test set for prediction. Different spectral pre-treatments and their combinations were investigated for the calculation of Partial Least Squares–Discriminant Analysis models in order to classify samples according to gel strength, foam height, and foam instability. A variable selection strategy based on the so-called Variable Importance in Projection scores was also evaluated. Both FT-NIR and FT-IR spectroscopy showed good potential in discriminating EWP samples with different technological properties. Correct classification percentages in prediction ranging from 59% to 89% were obtained with the best models calculated with selected wavenumbers. These results show a promising industrial perspective, demonstrating the possibility of developing cheap and fast instruments spanning a limited spectral range, which can be implemented on the production lines for EWP sorting and quality control.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Collections :
Submission date :
2021-11-16T08:23:26Z
2024-02-21T07:23:57Z
2024-02-21T07:23:57Z