Quick Identification of Bacillus in the Solid-state Fermentation Based on Hyperspectral Imaging Technology
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Abstract:
Hyperspectral imaging technology combined with pattern recognition methods were used to rapidly identify the types of acid-producing Bacillus in the solid-state fermentation for the production of Zhenjiang balsamic vinegar. First, three species of Bacillus were screened as standard bacteria. After 12 h of growth, the standard bacterial colonies were used as study objects and images were collected using a hyperspectral imaging system. Next, a total of 120 average spectra of an area of interest (20 × 20) in a single colony were extracted and processed by standard normal variate transform. Principal component analysis (PCA) was used to select three images with a characteristic wavelength from each image, and four texture characteristic variables were extracted from each image with a characteristic wavelength based on a gray level co-occurrence matrix. Principal component analysis (PCA) was conducted on the characteristic variables of the spectra and image texture, and appropriate principle components were extracted to construct k-nearest neighbor and back propagation-artificial neural network identification models. Among them, the identification results for the spectral models were better than those of the image models, and the optimal result was obtained from the back propagation-artificial neural network spectral model, whose identification rates of calibration set and prediction set were 98.70% and 97.78%, respectively, and the number of principal component factors was five. The study shows that the internal characteristics of the bacterial colony are important for identifying the species of a colony, and hyperspectral image technology can be used for rapid and convenient bacterial identification.
