Recognition of Different Bacteria in Solid State Fermentation Based on Hyperspectral Image Segmentation
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Abstract:
In this work, the fast identification of different bacteria in solid-state fermentation based on hyperspectral image segmentation was investigated. The samples of Bacillus, Lactobacillus and Rhodospirillum were prepared by using potato glucose agar medium. The full-black reflective images were obtained on the lens through the black lens cover. The image was denoised and segmented to obtain the gradient and distribution peak value of hyperspectral image. The information of spectral data was extracted from the position of colonies in the mask image, and the spectral data of three bacteria were divided into correction set and test set, which were used to build and validate the model in turn. The pre-processing of original spectral data was completed by the standard normal variable change method. The principal component analysis method was used to reduce the dimension and distinguish different colonies. The identification model was established by partial least squares discriminant analysis. The principal components of hyperspectral image and hyperspectral response value of bacteria were analyzed, and the model was established and validated. The results showed that the ratio of total error to analysis error was 1.12 after pretreatment with standard normal variable change method, which was lower than the prescribed robustness parameter. The hyperspectral peaks of different bacterial colonies were different, and the reflectivity of light was quite different. When the culture time was 36 h, the three bacteria could be distinguished effectively. The model in this work was better than the other two models. The recognition rate of test set was 98.25%. In conclusion, this method had the advantages of low analysis error and high recognition rate.
