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Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibr...

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Autores principales: Przybył, Krzysztof, Duda, Adamina, Koszela, Krzysztof, Stangierski, Jerzy, Polarczyk, Mariusz, Gierz, Łukasz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014237/
https://www.ncbi.nlm.nih.gov/pubmed/31963128
http://dx.doi.org/10.3390/s20020499
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author Przybył, Krzysztof
Duda, Adamina
Koszela, Krzysztof
Stangierski, Jerzy
Polarczyk, Mariusz
Gierz, Łukasz
author_facet Przybył, Krzysztof
Duda, Adamina
Koszela, Krzysztof
Stangierski, Jerzy
Polarczyk, Mariusz
Gierz, Łukasz
author_sort Przybył, Krzysztof
collection PubMed
description In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.
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spelling pubmed-70142372020-03-09 Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks Przybył, Krzysztof Duda, Adamina Koszela, Krzysztof Stangierski, Jerzy Polarczyk, Mariusz Gierz, Łukasz Sensors (Basel) Article In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits. MDPI 2020-01-16 /pmc/articles/PMC7014237/ /pubmed/31963128 http://dx.doi.org/10.3390/s20020499 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
Przybył, Krzysztof
Duda, Adamina
Koszela, Krzysztof
Stangierski, Jerzy
Polarczyk, Mariusz
Gierz, Łukasz
Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title_full Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title_fullStr Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title_full_unstemmed Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title_short Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
title_sort classification of dried strawberry by the analysis of the acoustic sound with artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014237/
https://www.ncbi.nlm.nih.gov/pubmed/31963128
http://dx.doi.org/10.3390/s20020499
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