Non-destructive and quantitative estimation of microbial growth rates on food surfaces using microscopic image analysis.

Microbial growth is a major cause of food spoilage, food waste and foodborne disease, making reliable monitoring of microbial dynamics on food surfaces critical for ensuring food quality and safety. In this study, an efficient and quantitative image-based estimation of microbial growth rates was achieved using the "bounded area". The bounded area was defined as the area including both the cells and the surrounding annular dark region, observed by microscopic analysis of microbial colonies on a membrane filter placed on a food sample. We propose a mathematical model that enables the estimation of individual colony growth rates from the temporal expansion of bounded areas and reconstruction of population-level growth rates by integrating these individual measurements. The method accurately captured the temperature-dependent growth behavior of Leuconostoc mesenteroides, with population-level growth rates closely matching those obtained by the conventional plate count method. The method was validated with a complex food matrix using a ham model, where growth rates from the image-based method were consistent with those from the plate count method despite the structural complexity of the food matrix. This approach provides a rapid, quantitative, and non-destructive method for assessing absolute microbial growth rates on food surfaces, supporting applications in food quality control and accurate shelf life assessment of processed foods.