Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.

This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fre...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mahmoud Abdel-Sattar, Rashid S Al-Obeed, Abdulwahed M Aboukarima, Dalia H Eshra
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/57f61dbd633a49f4b0cb1ba3d198a882
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:57f61dbd633a49f4b0cb1ba3d198a882
record_format	dspace
spelling	oai:doaj.org-article:57f61dbd633a49f4b0cb1ba3d198a8822021-12-02T20:04:46ZDevelopment of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.1932-620310.1371/journal.pone.0251185https://doaj.org/article/57f61dbd633a49f4b0cb1ba3d198a8822021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251185https://doaj.org/toc/1932-6203This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fresh peach fruit. Multiple regression analysis (MLR) and an artificial neural network (ANN) were employed. An ANN model was developed with six inputs and 15 neurons in the first hidden layer for the prediction of six chemical composition parameters. The results confirmed that the ANN model R2 = 974-0.998 outperformed the MLR models R2 = 0.473-0.840 using testing dataset. Moreover, sensitivity analysis revealed that the juice volume was the most dominating parameter for the prediction of titratable acidity, TSS/titratable acidity and vitamin C with corresponding contribution values of 39.97%, 50.40%, and 33.08%, respectively. In addition, sphericity percent contributed by 23.70% to anthocyanin and by 24.08% to total carotenoids. Furthermore, hue on TSS prediction was the highest compared with the other parameters, with a contribution percentage of 20.86%. Chroma contributed by different values to all variables in the range of 5.29% to 19.39%. Furthermore, fruit weight contributed by different values to all variables in the range of 16.67% to 23.48%. The ANN prediction method denotes a promising methodology to estimate targeted chemical composition levels of fresh peach fruits. The information of peach quality reported in this investigation can be used as a baseline for understanding and further examining peach fruit quality.Mahmoud Abdel-SattarRashid S Al-ObeedAbdulwahed M AboukarimaDalia H EshraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0251185 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mahmoud Abdel-Sattar Rashid S Al-Obeed Abdulwahed M Aboukarima Dalia H Eshra Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
description	This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fresh peach fruit. Multiple regression analysis (MLR) and an artificial neural network (ANN) were employed. An ANN model was developed with six inputs and 15 neurons in the first hidden layer for the prediction of six chemical composition parameters. The results confirmed that the ANN model R2 = 974-0.998 outperformed the MLR models R2 = 0.473-0.840 using testing dataset. Moreover, sensitivity analysis revealed that the juice volume was the most dominating parameter for the prediction of titratable acidity, TSS/titratable acidity and vitamin C with corresponding contribution values of 39.97%, 50.40%, and 33.08%, respectively. In addition, sphericity percent contributed by 23.70% to anthocyanin and by 24.08% to total carotenoids. Furthermore, hue on TSS prediction was the highest compared with the other parameters, with a contribution percentage of 20.86%. Chroma contributed by different values to all variables in the range of 5.29% to 19.39%. Furthermore, fruit weight contributed by different values to all variables in the range of 16.67% to 23.48%. The ANN prediction method denotes a promising methodology to estimate targeted chemical composition levels of fresh peach fruits. The information of peach quality reported in this investigation can be used as a baseline for understanding and further examining peach fruit quality.
format	article
author	Mahmoud Abdel-Sattar Rashid S Al-Obeed Abdulwahed M Aboukarima Dalia H Eshra
author_facet	Mahmoud Abdel-Sattar Rashid S Al-Obeed Abdulwahed M Aboukarima Dalia H Eshra
author_sort	Mahmoud Abdel-Sattar
title	Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
title_short	Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
title_full	Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
title_fullStr	Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
title_full_unstemmed	Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
title_sort	development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/57f61dbd633a49f4b0cb1ba3d198a882
work_keys_str_mv	AT mahmoudabdelsattar developmentofanartificialneuralnetworkasatoolforpredictingthechemicalattributesoffreshpeachfruits AT rashidsalobeed developmentofanartificialneuralnetworkasatoolforpredictingthechemicalattributesoffreshpeachfruits AT abdulwahedmaboukarima developmentofanartificialneuralnetworkasatoolforpredictingthechemicalattributesoffreshpeachfruits AT daliaheshra developmentofanartificialneuralnetworkasatoolforpredictingthechemicalattributesoffreshpeachfruits
_version_	1718375543813963776

Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.

Ejemplares similares