Preferred Language
Articles
/
iher-pABVTCNdQwCNJEx
Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage
...Show More Authors
Abstract<sec><label></label><p>This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (<italic>p</italic> > 0.05) for lightness (<italic>L</italic>*), redness‐greenness (<italic>a</italic>*), yellowness‐blueness (<italic>b</italic>*), total color differences (∆<italic>E</italic>), hue angle (<italic>h</italic>), chroma (<italic>C</italic>), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well‐predicted milk fat and moisture content using quadratic and two‐factor interaction models with mean errors of .00858–.01260 and correlation coefficient of .8051–.8205. Stability tests showed <italic>L</italic>* and WI reduced while <italic>a</italic>*, <italic>b</italic>*, Δ<italic>E</italic>, <italic>h</italic>, <italic>C</italic>, YI, and BI increased during the storage. NP milk had 77.21% higher half‐life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production.</p></sec><sec><title>Practical applications

The findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real‐time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro‐fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non‐thermal pasteurized milk. This study also provides a step‐by‐step protocol, facilitating the implementation of emerging technologies in the food industry.

Scopus Clarivate Crossref
View Publication
Publication Date
Sat Jun 01 2024
Journal Name
Innovative Food Science &amp; Emerging Technologies
Non-thermal pasteurization of milk by elongated electrode moderate electrical field: Chemical and sensory analysis during cold storage and shelf-life determination
...Show More Authors

View Publication
Scopus (2)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Tue Aug 01 2023
Journal Name
Innovative Food Science &amp; Emerging Technologies
Non-thermal pasteurization of milk by an innovative energy-saving moderate electrical field equipped with elongated electrodes and process optimization
...Show More Authors

View Publication
Scopus (8)
Crossref (7)
Scopus Clarivate Crossref
Publication Date
Fri Mar 01 2024
Journal Name
International Journal Of Medical Informatics
An artificial intelligence approach to predict infants’ health status at birth
...Show More Authors

View Publication
Scopus (4)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Harnessing Technological Innovation and Artificial Intelligence in Iraqi Commercial Banks to Achieve Sustainability
...Show More Authors

View Publication
Scopus (2)
Scopus Crossref
Publication Date
Fri Dec 06 2019
Journal Name
Ssociation Of Arab Universities Journal Of Engineering Sciences
Application of Artificial Neural Network and GeographicalInformation System Models to Predict and Evaluate the Quality ofDiyala River Water, Iraq
...Show More Authors

This research discusses application Artificial Neural Network (ANN) and Geographical InformationSystem (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameterswere used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium,Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity.These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala provincewhile it is poor to very polluted at the south of Baghdad City. The selected parameters wer

... Show More
Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Intelligent Systems And Applications In Engineering
Artificial Intelligence Based Statistical Process Control for Monitoring and Quality Control of Water Resources: A Complete Digital Solution
...Show More Authors

Scopus (3)
Scopus
Publication Date
Sun Sep 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
The role of competitive intelligence and reverse engineering to achieve competitive advantage
...Show More Authors

Abstract

In light of the great technological development and the emergence of globalization has increased global competition, where it became competitive exercise pressure on all sectors. In light of this companies mast enviorment depend on the means that keeps them on the competitive position through access to information about competitors in order to help them to draw a strategy that will achieve a competitive edge either through excellence or reduce the costs of their products and this means intelligence competitive and reverse engineering that help to gain information on competitors analyze and put of the decision-maker From this point formed the idea of ​​research in the statement of the role of

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sun May 01 2022
Journal Name
International Journal Of Multiphase Flow
Application of artificial neural network to predict slug liquid holdup
...Show More Authors

Publication Date
Wed Jun 11 2003
Journal Name
Iraqi Journal Of Laser
Modeling of Plasma Affected Thermal Profile in Solids During Laser Materials Processing
...Show More Authors

A new scheme of plasma-mediated thermal coupling has been implemented which yields the temporal distributions of the thermal flux which reaches the metal surface, from which the spatial and temporal temperature profiles can be calculated. The model has shown that the temperature of evaporating surface is determined by the balance between the absorbed power and the rate of energy loss due to evaporation. When the laser power intensity range is 107 to108 W/cm2 the temperature of vapor could increase beyond the critical temperature of plasma ignition, i.e. plasma will be ignited above the metal surface. The plasma density has been analyzed at different values of vapor temperature and pressure using Boltzmann’s code for calculation of elec

... Show More
View Publication Preview PDF
Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects
...Show More Authors

Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was

... Show More
View Publication Preview PDF
Crossref (1)
Crossref