Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network

Introduction Science of rheology has numerous applications in various fields of the food industry such as process assessment, acceptance of products and sales. Fluid behavior changes during processing due to an adverse change in the consistency and due to the combined operations such as mixing, heat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	A. A Jafari, E Tatar
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2018
Materias:	digital image processing pattern recognition real time viscosity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b2ff7d940d424cff92790a062e9cca20
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b2ff7d940d424cff92790a062e9cca20
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN FA
topic	digital image processing pattern recognition real time viscosity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	digital image processing pattern recognition real time viscosity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 A. A Jafari E Tatar Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
description	Introduction Science of rheology has numerous applications in various fields of the food industry such as process assessment, acceptance of products and sales. Fluid behavior changes during processing due to an adverse change in the consistency and due to the combined operations such as mixing, heating, cooling, etc. In this regard, viscosity is an important factor for quality assessment in most of the materials. To measure the viscosity, Viscometer devices are used which are directly in contact with the material. Working with these devices is time consuming, costly, under the influence of human factors and in some cases periodic calibration is required. Materials and Methods Date syrup was used as a viscous material in this study because it industrially is produced. An apparatus including a reservoir with an outlet orifice at the bottom was made to provide free flow of the liquid. Two sets of circular and rectangular orifices with different dimensions were used to investigate the effect of the orifice characteristics on the shape of the flow. Firstly, date syrup viscosity was measured by a conventional viscometer at 5 temperature levels and 6 concentration levels and behavior of the syrup were studied. Free flow of date syrup was photographed in the aforementioned temperatures and concentrations. On the other hand extracted features from the images were used as inputs to the neural network to give outputs as a fluid flow behavior index and consistency index. Measurement data were divided to three sets including training, validation and test sets whereas 70% of the data were used for training the neural networks, 15% as the validation set and 15% for testing the networks. Results and Discussion Results showed that similar to most of the liquids, viscosity of date syrup decreases when temperature increases. The experiments also revealed that the date syrup behavior is expressible with power law and can be determined using power equation. Date syrup has different behavior at different concentration levels. It changes from a pseudoplastic liquid to a Newtonian and then a dilatant liquid when concentration increases. Flow behavior index and consistency index corresponding to all three behavior of the fluid were determined. Results showed that the neural networks were able to accurately estimate the behavior and consistency indices with coefficient of correlations up to 0.99. Networks with three hidden layers were completely suitable for the estimation of the indices. These results revealed that in spite of different behavior of the liquid ranged from pseudoplastic to dilatant, the method was still able to determine the apparent viscosity of the fluid. Although the circular orifices were more efficient in determination of the indices than the rectangular orifices, there was not a significant difference between the uses of circular or rectangular orifices as well as no significant different between the orifices with different dimensions. The correlation between the actual and estimated values for fluid flow behavior index and consistency index was 0.98 whereas the mean square error of the validation sets was about 0.0138 which showed the accuracy of the method. Conclusions In this study a new method of viscosity determination was proposed. Machine vision was employed to estimate the viscosity based on the visual characteristics of the fluid free flow. Date syrup as a liquid with different rheological behaviors was used to assess the performance of the method. The strong correlation between the extracted features and fluid flow behavior index as well as a consistency index proved the reliability and accuracy of the method for viscosity estimation.
format	article
author	A. A Jafari E Tatar
author_facet	A. A Jafari E Tatar
author_sort	A. A Jafari
title	Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
title_short	Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
title_full	Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
title_fullStr	Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
title_full_unstemmed	Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network
title_sort	estimation of date syrup viscosity using machine vision and artificial neural network
publisher	Ferdowsi University of Mashhad
publishDate	2018
url	https://doaj.org/article/b2ff7d940d424cff92790a062e9cca20
work_keys_str_mv	AT aajafari estimationofdatesyrupviscosityusingmachinevisionandartificialneuralnetwork AT etatar estimationofdatesyrupviscosityusingmachinevisionandartificialneuralnetwork
_version_	1718429860448174080
spelling	oai:doaj.org-article:b2ff7d940d424cff92790a062e9cca202021-11-14T06:34:41ZEstimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network2228-68292423-394310.22067/jam.v8i2.61681https://doaj.org/article/b2ff7d940d424cff92790a062e9cca202018-09-01T00:00:00Zhttps://jame.um.ac.ir/article_32996_38114ecc4b62dd030299728763d7aa6e.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Introduction Science of rheology has numerous applications in various fields of the food industry such as process assessment, acceptance of products and sales. Fluid behavior changes during processing due to an adverse change in the consistency and due to the combined operations such as mixing, heating, cooling, etc. In this regard, viscosity is an important factor for quality assessment in most of the materials. To measure the viscosity, Viscometer devices are used which are directly in contact with the material. Working with these devices is time consuming, costly, under the influence of human factors and in some cases periodic calibration is required. Materials and Methods Date syrup was used as a viscous material in this study because it industrially is produced. An apparatus including a reservoir with an outlet orifice at the bottom was made to provide free flow of the liquid. Two sets of circular and rectangular orifices with different dimensions were used to investigate the effect of the orifice characteristics on the shape of the flow. Firstly, date syrup viscosity was measured by a conventional viscometer at 5 temperature levels and 6 concentration levels and behavior of the syrup were studied. Free flow of date syrup was photographed in the aforementioned temperatures and concentrations. On the other hand extracted features from the images were used as inputs to the neural network to give outputs as a fluid flow behavior index and consistency index. Measurement data were divided to three sets including training, validation and test sets whereas 70% of the data were used for training the neural networks, 15% as the validation set and 15% for testing the networks. Results and Discussion Results showed that similar to most of the liquids, viscosity of date syrup decreases when temperature increases. The experiments also revealed that the date syrup behavior is expressible with power law and can be determined using power equation. Date syrup has different behavior at different concentration levels. It changes from a pseudoplastic liquid to a Newtonian and then a dilatant liquid when concentration increases. Flow behavior index and consistency index corresponding to all three behavior of the fluid were determined. Results showed that the neural networks were able to accurately estimate the behavior and consistency indices with coefficient of correlations up to 0.99. Networks with three hidden layers were completely suitable for the estimation of the indices. These results revealed that in spite of different behavior of the liquid ranged from pseudoplastic to dilatant, the method was still able to determine the apparent viscosity of the fluid. Although the circular orifices were more efficient in determination of the indices than the rectangular orifices, there was not a significant difference between the uses of circular or rectangular orifices as well as no significant different between the orifices with different dimensions. The correlation between the actual and estimated values for fluid flow behavior index and consistency index was 0.98 whereas the mean square error of the validation sets was about 0.0138 which showed the accuracy of the method. Conclusions In this study a new method of viscosity determination was proposed. Machine vision was employed to estimate the viscosity based on the visual characteristics of the fluid free flow. Date syrup as a liquid with different rheological behaviors was used to assess the performance of the method. The strong correlation between the extracted features and fluid flow behavior index as well as a consistency index proved the reliability and accuracy of the method for viscosity estimation.A. A JafariE TatarFerdowsi University of Mashhadarticledigital image processingpattern recognitionreal timeviscosityAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 8, Iss 2, Pp 309-320 (2018)

Estimation of Date Syrup Viscosity using Machine Vision and Artificial Neural Network

Ejemplares similares