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A multilayer structure of microbial cells can result in multiple attenuations of electromagnetic waves, making the biological particles have a strong extinction ability. However, the influence of various morphologies on infrared band optical extinction performance of biomaterials is unclear. The combination of shape, dimension, and structure parameters is proposed to evaluate and enhance the optical extinction properties of artificial bioparticles. Combined with the preliminary work of our research group, four artificial biological particles were selected to simulate and calculate their extinction performance with different parameters theoretically based on the discrete-dipole approximation method. The results show the extinction properties of bioparticles with different shapes and dimensions in the 3.0 to 5.0 μm and 8.0 to 14.0 μm wavebands. It was found that the chain-shaped particles with more constituent spheres and a bending angle of 60 deg as well as the ellipsoid-shaped particles with an axis ratio of 1:2 exhibit better extinction properties in the above two bands. Among them, the regulation of extinction efficiency can reach ∼13.92 % and 18.16%.
The detection of biological spore activity is the basis for effective prevention and control of plant and animal diseases. However, the reduction of its activity level during storage is one of the major problems affecting the application. A rapid and accurate method to detect the activity of biological spores is of great value for exploration and research. In this paper, UV-Vis spectroscopy combined with a one-dimensional convolutional neural network (1D-CNN) is used for the discrimination of dead and viable biological spore. The spectrum of three biological spores were collected and preprocessed by the standard normal variate transformation (SNV).Unsupervised clustering of the sample set was performed using principal component analysis (PCA). The activity discrimination model of biological spores is constructed based on 1D-CNN. The experimental resultsshow that the model has a discriminative accuracy of 100%, which has the potential to replace the traditional methods of determining the dead and viable biological spore.
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