ABSTRACT
We formerly developed an automatic colony count system based on the time-lapse shadow image analysis (TSIA). Here this system has been upgraded and applied to practical rapid decision. A microbial sample was spread on/in an agar plate with 90 mm in diameter as homogeneously as possible. We could obtain the results with several strains that most of colonies appeared within a limited time span. Consequently the number of colonies reached a steady level (Nstdy) and then unchanged until the end of long culture time to give the confirmed value (Nconf). The equivalence of Nstdy and Nconf as well as the difference of times for Nstdy and Nconf determinations were statistically significant at p < 0.001. Nstdy meets the requirement of practical routines treating a large number of plates. The difference of Nstdy and Nconf, if any, may be elucidated by means of retrievable big data. Therefore Nconf is valid for official documentation.
Subject(s)
Colony Count, Microbial/methods , Food Contamination/analysis , Food Microbiology/methods , Imaging, Three-Dimensional/methods , Time-Lapse Imaging/methods , Aspergillus/growth & development , Bacillus/growth & development , Candida albicans/growth & development , Escherichia coli/growth & development , Models, TheoreticalABSTRACT
Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance.
