研究三种精油在抗菌和抗氧化应用中的协同作用:使用混合物设计,人工神经网络和计算机方法的综合框架

IF 5.9 1区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY Food Bioscience Pub Date : 2025-05-01 Epub Date: 2025-03-05 DOI:10.1016/j.fbio.2025.106250
Sara Lebrazi , Kawtar Fikri-Benbrahim , Mohamed El fadili , Mohamed Jeddi , Abderrazak Aboulghazi , Zineb Benziane Ouaritini , Chaimae Moufakkir , Yassine Kharbach , Hicham Harhar , Abdelhakim Bouyahya , Chaimae Najjari , Mouhcine Fadil
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引用次数: 0

摘要

精油因其抗菌和抗氧化的特性,在食品领域作为防腐剂得到了广泛的应用。此外,精油的混合物可能比单一精油具有更强的抗菌和抗氧化作用。在此基础上,本研究的目的是强调从摩洛哥枝花(C. mixtus)、桃金娘(M. communis)和意大利蜡菊亚种中提取的混合精油(EOs)的联合抗菌和抗氧化作用。意大利属植物;气相色谱-质谱联用(GC-MS)和气相色谱-火焰离子化检测(GC-FID)分析结果表明,混合葡萄树的主要成分为α-蒎烯(19.7%)、圣托里纳醇(11.53%)和德曼烯D (10.59%); communis的主要成分为1,8-桉树脑(30.88%)、α-蒎烯(25.65%)、乙酸桃金桃烯基(14.69%)和柠檬烯(10.99%),而H. italicum EO的主要成分为乙酸橙基(16.27%)、α-蒎烯(13.64%)、β-亚麻烯(8.79%)和γ-姜黄烯(7.7%)。混合菌对大肠杆菌(E. coli)和金黄色葡萄球菌(S. aureus)的抗氧化作用最强,而意大利菌(H. italicum)的抗氧化作用最强。随后,采用混合设计(MDM)和人工神经网络(ann)相结合的方法对EOs的配方进行了改进。预测模型结果表明,混合菌、communis和H. italicum的三元混合物对大肠杆菌和金黄色葡萄球菌的抑制作用均有影响,而混合菌和H. italicum的二元混合物表现出最高的抗氧化活性。统计分析表明,两种优化方法均能准确预测生物活性。然而,人工神经网络模型的准确性优于MDM。
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Investigating the synergistic action of three essential oils for antibacterial and antioxidant applications: A comprehensive framework using mixture design, artificial neural networks, and in silico methods
Essential oils (OEs), recognized for their antibacterial and antioxidant characteristics, have gained extensive application in the food sector as preservative agents. Moreover, mixtures of OEs may have more potent antibacterial and antioxidant effects than a single essential oil. Expanding on this foundation, the objective of this work was to highlight the combined antibacterial and antioxidant action of blending essential oils (EOs) derived from Moroccan Cladanthus mixtus (C. mixtus), Myrtus communis (M. communis), and Helichrysum italicum subsp. Italicum (H. italicum). Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Flame Ionization Detection (GC-FID) analyses have revealed that C. mixtus EO was marked by α-pinene (19.7%), santolina alcohol (11.53%) and germacrene D (10.59%), M. communis was characterized by 1,8-cineole (30.88%), α-pinene (25.65%), myrtenyl acetate (14.69%) and limonene (10.99%), while neryl acetate (16.27%), α-pinene (13.64 %), β-selinene (8.79%), γ-curcumene (7,7%) were major compounds in H. italicum EO. C. mixtus showed the strongest efficacy against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), while H. italicum demonstrated the strongest antioxidant effect. Subsequently, the methodology of mixture design (MDM) combined with artificial neural networks (ANNs) were used to improve the formulations of the EOs. The results obtained from the predictive modeling revealed that the inhibition of E. coli and S. aureus was impacted by the ternary mixtures of C. mixtus, M. communis, and H. italicum, whereas the binary mixture of C. mixtus and H. italicum displayed the highest antioxidant activity. Statistical analysis indicated that both optimization approaches accurately predicted the biological activities. However, the accuracy of the ANNs models outperformed that of MDM.
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来源期刊
Food Bioscience
Food Bioscience Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
6.40
自引率
5.80%
发文量
671
审稿时长
27 days
期刊介绍: Food Bioscience is a peer-reviewed journal that aims to provide a forum for recent developments in the field of bio-related food research. The journal focuses on both fundamental and applied research worldwide, with special attention to ethnic and cultural aspects of food bioresearch.
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