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Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage
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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.

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Publication Date
Mon Jan 01 2018
Journal Name
Current Research In Microbiology And Biotechnology
Detection of some Physiological and Immunological parameters in Iraqi clinical and subclinical hypothyroid patients: Relationship study
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The present study was performed to evaluate the level of some risk factors (biochemical and immunological) in hypothyroid Iraqi patients considering the different thyroid functional states (hypothyroidism and subclinical hypothyroidism).The study includes 82 patients clinically diagnosed with hypothyroidism. Three study groups have been investigated: (47 clinical hypothyroid patients, 12 subclinical hypothyroid patients 23 healthy individuals) of different ages. This study, show that the proportion of females (83.3 %), (87.2%) in subclinical and clinical hypothyroidisim respectively higher than the proportion of males (16.7%),(12.8%) in subclinical and clinical hypothyrodism respectively of the total patients.The majority of subclinical hyp

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Advanced Pharmacy Education And Research
Comparative biosimilar quality studies between a rituximab product and MabThera
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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Sun Sep 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Development of PVT Correlation for Iraqi Crude Oils Using Artificial Neural Network
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Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Thu Mar 21 2019
Journal Name
J. Eng. Appl. Sci
Developing an Arabic handwritten recognition system by means of artificial neural network
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The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single l

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Publication Date
Mon Jan 02 2012
Journal Name
Journal Of The Faculty Of Medical Baghdad
Comparative study between ultrasound findings and intra-operative in non-traumatic abdominal pain
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Publication Date
Wed Oct 01 2025
Journal Name
Chinese Journal Of Analytical Chemistry
Synthesis, characterization, antioxidant and bioactivity assessment, and thermodynamic analysis of metal ion complexes using a novel azo dye
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Publication Date
Fri Mar 30 2018
Journal Name
International Journal Of Science And Research
The Effect of Age 0nLeptin Hormone and Some Biochemical Parameters in Patients Pre-Dialysis
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To evaluate impact the difference in stages ofage and related incidence of hemodialysis patients.Two hundred and fifty patients undergoing hemodialysis were collected from general hospital in Baghdad city /Iraq. The samples with renal failure before hemodialysis were divided into (138) male,( 112)female. The sera were separated from samples to physiological investigation. We found that renal failure was more predominant among the patients ages group ranging from (51-70) years old. The results shows A significant increase in the levels of urea, creatinine, in younger patients (≤ 30 years) when compared with older patients (>70 years). Furthermore a significant decrease in serum levels of total protein in patients in older patients (>7

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Publication Date
Sat Oct 01 2022
Journal Name
Journal Of Engineering
Parameters Affecting the Strength and Behavior of RC Dapped-End Beams: A Numerical Study
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The finite element method has been used in this paper to investigate the behavior of precast reinforced concrete dapped-ends beams (DEBs) numerically. A parametric investigation was performed on an experimental specimen tested by a previous researcher to show the effect of numerous parameters on the strength and behavior of RC dapped-end beams. Reinforcement details and steel arrangement, the influence of concrete compressive strength, the effect of inclined load, and the effect of support settlement on the strength of dapped-ends beams are examples of such parameters. The results revealed that the dapped-end reinforcement arrangement greatly affects the behavior of dapped end beam. The failure load decreases by 25% when

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