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Summary: Germination 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Growth of three Clostridium species in cheese monitored with HSI. • PLS and SVM models showed similar prediction performance. • The HSI map revealed the diffusion process of the metabolic products in cheese. • HSI is a fast and reliable technique to monitor late blowing defect in cheese. This study developed a novel method for monitoring cheese contamination with Clostridium spores non-invasively using hyperspectral imaging (HSI). The ability of HSI to quantify Clostridium metabolites was investigated with control cheese and cheese manufactured with milk contaminated with Clostridium tyrobutyricum , Clostridium butyricum and Clostridium sporogenes. Microbial count, HSI and SPME-GC-MS data were obtained over 10 weeks of storage. The developed method using HSI successfully quantified butyric acid (R2 = 0.91, RPD = 3.38) a major compound of C lostridium metabolism in cheese. This study creates a new venue to monitor the spatial and temporal development of late blowing defect (LBD) in cheese using fast and non-invasive measurement. [ABSTRACT FROM AUTHOR]

Background Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopic technique which uses a focused pulsed laser beam to generate plasma from the material. The plasma contains atoms, ions and free electrons which emit electromagnetic radiation as the plasma cools down. The emitted light is resolved by a spectrometer to form a spectrum. Recently, LIBS has become an emerging analytical technique for characterisation and identification of materials; its multi-elemental analysis, fast response, remote sensing, little to no sample preparation, low running cost and ease of use make LIBS a promising technique for the food sector. Scope and approach The present article reviews the feasibility of LIBS for food analysis. It presents recent progress and applications of LIBS as an efficient and reagent-free, at-line tool capable of replacing traditional time-consuming analytical methods for assessing the quality and composition of food products. An overview of LIBS fundamentals, instrumentation and statistical data analysis is also provided. Key findings and conclusions Although LIBS technology shows many advantages, challenges remain in terms of sample preparation, matrix effects, spectral pre-processing, model calibration and instrument development. [ABSTRACT FROM AUTHOR]