Artificial neural network modeling of engineering properties and microbial stability of watermelon juice preserved with sustainable natural additives
11 affiliations
- 1Department of Food and Nutrition, Faculty of Human Sciences and Design, King Abdulaziz University
- 2Department of Cyber Security, Southern Delta University
- 3Department of Agricultural Engineering, Southern Delta University
- 4Department of Mechanical Engineering, Southern Delta University
- 5Department of Food Science and Nutrition, College of Sciences, Taif University
- 6Department of Mechanical Engineering, Faculty of Engineering, Taif University
- 7Department of Biological Sciences, College of Science, University of Jeddah
- 8Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Umm Al-Qura University
- 9Department of Chemistry, College of Science, Taif University
- 10Research Center of Basic Sciences, Engineering and High Altitude, Taif University
- 11Department of Biology, College of Science and Arts, Najran University
Abstract
Food quality preservation remains a major challenge globally, especially for highly labile fruit juices. This study was conducted to predict the engineering and microbial stability of bio-fortified watermelon juice, using linear regression analysis and an artificial neural network (ANN). In this study, watermelon juice was treated with the synthetic additive sodium benzoate and natural (sustainable) additives, including clove seed extract, cinnamon extract, bay leaf extract, ginger rhizome extract, honey, and lime juice. The results revealed that the natural additives significantly improved, the engineering properties, antioxidant ability, and antimicrobial potential of the treated juice samples ( p ≤ 0.05), with some of the multi-component hybridized treatment formulations (particularly Treatment T13), performing better than the synthetic additive. Particularly, the negative control unit exhibited the poorest DPPH, TPC, TFC, FRAP, Vitamin C, and antimicrobial values, while the T13 sample recorded the maximum values of 91.30% DPPH, 191.86 mg GAE/100 mL juice, 77.31 mg QE/100 mL juice, 10.37 μmol Fe ²⁺ /mL, and 17.82 mg/100 mL, respectively. Also, the T13 sample recorded the lowest bacterial and fungal counts of 3.29 ×10 ⁴ and 2.17 ×10 ³ CFU/mL after 10 storage days, respectively. The results of the linear regression analysis, microbial inhibition efficiency, and index comparison further affirmed that the sustainable additives can substantially improve the antioxidant and antimicrobial mechanisms of the treatment. The linear regression modeling elucidated the associations between treatment and storage time and the evaluated quality attributes; conversely, the ANN approach acted as a supplementary nonlinear strategy for anticipating selected bioprocess and microbial behavior. Basically, the findings of this research have highlighted that, combined natural additives and computer programming, have potential to improve antioxidant effects, microbial stability, and targeted biophysical characteristics of watermelon juice. These findings suggest preservation capacity without demonstrating definitive clinical or epidemiological benefits.
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BibTeX
@article{allily2026artificial,
title = {{Artificial neural network modeling of engineering properties and microbial stability of watermelon juice preserved with sustainable natural additives}},
author = {Rowida Allily and Ekruyota Godwin and Alanood A. Alfaleh and Ebtihaj O. Alnasri and Maha Hijazi and Uguru Hilary and Edafiadhe Emozino and Nimah Alnemari and Rokayya Sami and Mahmoud Helal and Amera N. Alqahtani and Awatif Almehmadi and Ola Abu Ali and Sarah Alharthi and Ahmad F. Alhomodi},
journal = {Frontiers in Microbiology},
year = {2026},
month = oct,
doi = {10.3389/fmicb.2026.1977402},
url = {https://www.frontiersin.org/articles/10.3389/fmicb.2026.1977402/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial neural network modeling of engineering properties and microbial stability of watermelon juice preserved with sustainable natural additives
AU - Rowida Allily
AU - Ekruyota Godwin
AU - Alanood A. Alfaleh
AU - Ebtihaj O. Alnasri
AU - Maha Hijazi
AU - Uguru Hilary
AU - Edafiadhe Emozino
AU - Nimah Alnemari
AU - Rokayya Sami
AU - Mahmoud Helal
AU - Amera N. Alqahtani
AU - Awatif Almehmadi
AU - Ola Abu Ali
AU - Sarah Alharthi
AU - Ahmad F. Alhomodi
PY - 2026
DA - 2026/10/09
JO - Frontiers in Microbiology
DO - 10.3389/fmicb.2026.1977402
UR - https://www.frontiersin.org/articles/10.3389/fmicb.2026.1977402/full
AB - Food quality preservation remains a major challenge globally, especially for highly labile fruit juices. This study was conducted to predict the engineering and microbial stability of bio-fortified watermelon juice, using linear regression analysis and an artificial neural network (ANN). In this study, watermelon juice was treated with the synthetic additive sodium benzoate and natural (sustainable) additives, including clove seed extract, cinnamon extract, bay leaf extract, ginger rhizome extract, honey, and lime juice. The results revealed that the natural additives significantly improved, the engineering properties, antioxidant ability, and antimicrobial potential of the treated juice samples ( p ≤ 0.05), with some of the multi-component hybridized treatment formulations (particularly Treatment T13), performing better than the synthetic additive. Particularly, the negative control unit exhibited the poorest DPPH, TPC, TFC, FRAP, Vitamin C, and antimicrobial values, while the T13 sample recorded the maximum values of 91.30% DPPH, 191.86 mg GAE/100 mL juice, 77.31 mg QE/100 mL juice, 10.37 μmol Fe ²⁺ /mL, and 17.82 mg/100 mL, respectively. Also, the T13 sample recorded the lowest bacterial and fungal counts of 3.29 ×10 ⁴ and 2.17 ×10 ³ CFU/mL after 10 storage days, respectively. The results of the linear regression analysis, microbial inhibition efficiency, and index comparison further affirmed that the sustainable additives can substantially improve the antioxidant and antimicrobial mechanisms of the treatment. The linear regression modeling elucidated the associations between treatment and storage time and the evaluated quality attributes; conversely, the ANN approach acted as a supplementary nonlinear strategy for anticipating selected bioprocess and microbial behavior. Basically, the findings of this research have highlighted that, combined natural additives and computer programming, have potential to improve antioxidant effects, microbial stability, and targeted biophysical characteristics of watermelon juice. These findings suggest preservation capacity without demonstrating definitive clinical or epidemiological benefits.
SN - 1664-302X
ER -