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Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network
The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349398/ https://www.ncbi.nlm.nih.gov/pubmed/32599728 http://dx.doi.org/10.3390/s20123566 |
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author | Curto, Belén Moreno, Vidal García-Esteban, Juan Alberto Blanco, Francisco Javier González, Inmaculada Vivar, Ana Revilla, Isabel |
author_facet | Curto, Belén Moreno, Vidal García-Esteban, Juan Alberto Blanco, Francisco Javier González, Inmaculada Vivar, Ana Revilla, Isabel |
author_sort | Curto, Belén |
collection | PubMed |
description | The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations. |
format | Online Article Text |
id | pubmed-7349398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73493982020-07-22 Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network Curto, Belén Moreno, Vidal García-Esteban, Juan Alberto Blanco, Francisco Javier González, Inmaculada Vivar, Ana Revilla, Isabel Sensors (Basel) Article The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations. MDPI 2020-06-24 /pmc/articles/PMC7349398/ /pubmed/32599728 http://dx.doi.org/10.3390/s20123566 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Curto, Belén Moreno, Vidal García-Esteban, Juan Alberto Blanco, Francisco Javier González, Inmaculada Vivar, Ana Revilla, Isabel Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title | Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title_full | Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title_fullStr | Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title_full_unstemmed | Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title_short | Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network |
title_sort | accurate prediction of sensory attributes of cheese using near-infrared spectroscopy based on artificial neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349398/ https://www.ncbi.nlm.nih.gov/pubmed/32599728 http://dx.doi.org/10.3390/s20123566 |
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