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245	1	0	_aCorrelation between chemical composition and antimicrobial properties of essential oils against most common food pathogens and spoilers: In-vitro efficacy and predictive modelling
490	0		_vMicrobial Pathogenesis, 147, p. 04212, 2020
520	3		_aUsing disk diffusion assay and broth microdilution, we evaluated the antimicrobial activity of 38 commercially available essential oils (EOs)against 24 food pathogens and spoilers. These includingE. coliO157: H7 (3 types), Listeria (3 types), Bacillus (2 types),Salmonella enterica(2 types),Staphylococcus aureus(3 types), Clostridium tyrobutiricum,Pseudomonas aeruginosa, Brochotrix thermosphacta, Campylobacter jejuni, Carnobacterium divergens, Aspergillus (2 types), and Penicillium (4 types). Correlation between EOs' chemical composition and antimicrobial properties was studied using R software. Moreover, statistical models representing the relationship were generated using Design Expert®. The predictive models identified the chemical attributes of EOs that drive their antimicrobial properties while providing an understanding of their interactions. Thyme (Aldrich, Novotaste), cinnamon (Aliksir, BSA), garlic (Novotaste), Mexican garlic blend N A (Novotaste), and oregano (BSA)were the strongest antimicrobial. The most sensitive pathogens were P. solitum (MIC of 19.53 ppm)and L. monocytogenes (MIC of 39 ppm). The correlation analysis showed that phenols and aldehydes had the strongest positive effects on the antimicrobial properties followed by the sulfur containing compounds and the esters; while the effects of monoterpenes and ketones were negative. Different sensitivity of food pathogens to chemical families was observed. For instance, phenols and aldehydes exhibited a linear inhibitory effect onL. monocytogenes(LM1045, MIC), while sesquiterpene and ester showed a significant effect onS. aureus(ATCC 6538, MIC). The developed predictive models are expected to predict the antimicrobial properties based on the chemical families of essential oils.
650	1	4	_aMODELLING
650	1	4	_aPREDICTIVE MODELS BASED ON COMPOSITION
650	1	4	_aNATURAL ANTIMICROBIAL
650	1	4	_aFOODBORNE PATHOGENS
650	1	4	_aANTIMICROBIAL ACTIVITY AGAINST FOOD SPOILERS
700	1	2	_aBagheri, Leila
700	1	2	_aKhodaei, Nastaran
700	1	2	_aSalmieri, Stephane
700	1	2	_aKarboune, Salwa
700	1	2	_aLacroix, Monique
856	4	0	_uhttps://drive.google.com/file/d/1Um6qbtH4oJYj-2VI4j0oxidZnQnjLxub/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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