Background:
Bacterial cellulose (BC) is a versatile biomaterial with numerous applications,
and the identification of bacterial strains that produce it is of great importance. This study explores
the effectiveness of a Stacked Autoencoder (SAE)-based deep learning method for the classification
of bacterial cellulose-producing bacteria.
Objective:
The primary objective of this research is to assess the potential of SAE-based classification
models in accurately identifying and classifying bacterial cellulose-producing bacteria, with a
particular focus on strain GZ-01.
objective:
The primary objective of this research is to assess the potential of SAE-based classification models in accurately identifying and classifying bacterial cellulose-producing bacteria, with a particular focus on strain GZ-01.
Methods:
Strain GZ-01 was isolated and subjected to a comprehensive characterization process,
including morphological observations, physiological and biochemical analysis, and 16S rDNA sequencing.
These methods were employed to determine the identity of strain GZ-01, ultimately recognized
as Acetobacter Okinawa. The study compares the performance of SAE-based classification
models to traditional methods like Principal Component Analysis (PCA).
Results:
The SAE-based classifier exhibits outstanding performance, achieving an impressive accuracy
of 94.9% in the recognition and classification of bacterial cellulose-producing bacteria. This
approach surpasses the efficacy of conventional PCA in handling the complexities of this classification
task.
Conclusion:
The findings from this research highlight the immense potential of utilizing nanotechnology-
driven data analysis methods, such as Stacked Autoencoders, in the realm of bacterial cellulose
research. These advanced techniques offer a promising avenue for enhancing the efficiency and
accuracy of bacterial cellulose-producing bacteria classification, which has significant implications
for various applications in biotechnology and materials science.