Abstract
A method based on a dual-type (transmission and fluorescence) hyperspectral microscopic image system was developed to identify species of intestinal fungi. Living fungi are difficult to identify via transmission spectra or fluorescence spectra alone. We propose an identification method based on both fluorescence and transmission spectra that employs a series of image processing methods. Three species of intestinal fungi were used to evaluate the method. The results demonstrate that the specificity of the model trained with dual-type spectra was 98.36%, whereas the specificities achieved by training with fluorescence spectra and transmission spectra alone were 94.04% and 92.88%, respectively.
Highlights
Intestinal fungi are important to human health, and disorders of intestinal fungi can cause many diseases
polymerase chain reaction (PCR) amplification and sequencing of 18S rDNA have been used to identify yeasts and molds [3]. 16S rRNA has been used as the raw material for gene-targeted PCR to identify probiotics [4], and this combined with SYBER Green I fluorophore [5] made the species of fungi easier to identify
We applied a method based on two types of hyperspectral images to pollen identification [24], and the results showed that this dual-type hyperspectral imaging technique can achieve higher specificity
Summary
Intestinal fungi are important to human health, and disorders of intestinal fungi can cause many diseases. Because living intestinal fungi entwine around each other in the culture medium and because their metabolic material contains abundant fluorescent materials that disturb the fluorescent signal from the fungi, they are difficult to identify via conventional single-type hyperspectral imaging technology. We applied a method based on two types of hyperspectral images (fluorescence and transmission spectra) to pollen identification [24], and the results showed that this dual-type hyperspectral imaging technique can achieve higher specificity. This dual-type hyperspectral imaging technique was applied to the identification of intestinal fungi for the first time, to the best of our knowledge. We built three classified models, one of these was trained using only the fluorescence spectral data, one was trained using only the transmission spectral data, and one was trained using both fluorescence and transmission spectral data
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