Abstract
SummaryGermination of Bacillus cereus spores followed by growth and replication of the vegetative cells in food can result in food poisoning and therefore significant economic and health impacts. This study explores a novel approach to detect and differentiate spores and germinated B. cereus cells using hyperspectral imaging (HSI) in combination with machine learning using three different germination triggers. HSI could successfully differentiate between dormant spores, germinated cells and structural controls (non‐spores). The spectral data in the visible‐near‐infrared range are sensitive to unique structural and chemical characteristics specific to spores, setting them apart from their vegetative counterparts and non‐biological controls. This non‐destructive and robust approach shows significant potential for detection and assessment of the physiological state (dormant or germinated). Therefore, HSI is a potential method for the detection of germination in B. cereus spores and merits further research and validation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Food Science & Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.