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Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
Published on: July 24, 2021
Bayesian optimization of natural antifungal blends reveals metabolomic remodeling in Aspergillus niger
Carla Pino1, Matthew Sujanto2, Camila Covarrubias3
1Scientific Research Sub-Unit, Research and Development Department, The Not Company, 3550 Quilín Ave., Macul, Santiago, Chile.
Abstract:
The demand for clean-label preservation has heightened interest in natural antifungal alternatives for bakery products. Here, Bayesian optimization and untargeted metabolomics were combined to design and evaluate food-grade botanical blends against the spoilage fungus Aspergillus niger. Individual extracts showed limited in vitro activity, with inhibition values from 2% to 23%. However, the optimization workflow identified multicomponent blends with improved performance: Blend 1 and Blend 2 reached 34% and 27% in vitro inhibition, respectively. Blend 2 showed the strongest matrix-level effect in pound cake, with conidial loads more than 100-fold lower than the control at 18 days post-inoculation (dpi) and visible growth ratios that decreased approximately 5-fold at 7 dpi. Untargeted liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry revealed shared and treatment-specific remodeling of the fungal metabolome. These results indicate that antifungal performance was better explained by blend composition than by the apparent activity of individual extracts.
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