Boosting living Bacillus spore identification: Kolmogorov–Arnold network-guided convolutional neural network combined with laser tweezers Raman spectroscopy

Abstract

As primary carriers of foodborne and zoonotic diseases, Bacillus spores can pose a serious threat to food microbiology and human disease. Thus, the precise identification of Bacillus spores is of great significance for ensuring food safety and human health. Herein, this study proposed a living Bacillus spore identification platform: an adaptive Kolmogorov-Arnold network (KAN)-guided convolutional neural network (CNN) configuration combined with laser tweezers Raman spectroscopy (LTRS). To address the small size of the original single-cell Raman spectral datasets, Gaussian noise-based spectra augmentation was employed to significantly enlarge and enrich it. When the adaptive KAN was introduced into the dense layer of CNN, the prediction accuracy of five Bacillus spore species was as high as 97.80% ± 1.79%. Moreover, the KAN-guided CNN configuration has strong robustness and generalization ability, providing a prediction accuracy of 96% for an independent spectral dataset. To figure out the classification contribution of each Raman band, a blocking individual Raman band method was proposed. The Raman band located at 1655 cm⁻¹, belonging to the amide I vibration of protein, was determined as the dominant contributor, surpassing two Raman bands belonging to Ca-DPA at 1576 cm⁻¹ and 1449 cm⁻¹. It can be foreseen that the KAN-guided CNN configuration combined with LTRS shows great promise for determining microbial identity, especially for unculturable microorganisms.