Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

Abstract

Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of <i>Enterobacter</i> sp, <i>Acetobacter pasteurianus</i>, and <i>Lactobacillus paracasei</i> colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.