Noise-free microbial colony counting method based on hyperspectral features of agar plates

Abstract

A noise-free bacterial colony counting method identifying noise (i.e., sausage, bacon, and millet fragments) with similar colors or shapes to those of colonies was developed for food quality assessment. First, spectral features corresponding to colony cluster regions and background regions (agar medium and food fragments) were extracted after collection of hyperspectral images. A cluster-segmenting calibration model that could identify colony clusters and background regions was developed. Second, spectral features of colony centers and borders were extracted, and a colony-separating calibration model that could separate single colonies from clusters (multiple colonies contacting each other) was developed. Third, each pixel of an agar plate hyperspectral image was identified using established calibration models, enabling the colonies on the agar plate to be counted successfully (R² = 0.9998). The results demonstrated that the proposed method could identify the noises caused by food fragments with similar colors or shapes to those of colonies.

Keywords: Chemometrics; Colony counting; Hyperspectral imaging technology; Noise-free; Spectral feature.