Advanced Spore Identification: Single-Cell Raman Spectroscopy Combined with Self-Attention Mechanism-Guided Deep Learning.

(Nb) has been considered a dangerous pathogen, which can spread rapidly through free spores. Nowadays, pebrine disease caused by Nb spores is a serious threat to silkworms, causing huge economic losses in both the silk industry and agriculture every year. Thus, how to accurately identify living Nb spores at a single-cell level is greatly demanded. In this work, we proposed a novel approach to accurately and conveniently identify Nb spores using single-cell Raman spectroscopy and a self-attention mechanism (SAM)-guided convolutional neural network (CNN) framework. With the assistance of SAM and data augmentation methods, an optimal CNN model can not only efficiently extract spectral feature information but also construct potential relationships of global spectral features. Compared with the case without both SAM and data augmentation, the average prediction accuracy of Nb spores from nine different larvae can be significantly developed by almost 18%, from original 83.93 ± 4.88% to 99.27 ± 0.25%. To visualize the individual classification weight, a local feature extraction strategy named blocking individual Raman bands was proposed. According to the relative weight, these four Raman bands located at 1658, 1458, 1127, and 849 cm, mainly contribute to the high prediction accuracy of 99.27 ± 0.25%. It is worth noting that these Raman bands were also highlighted by the weight curve of SAM, indicating that the four Raman bands proposed by our optimal CNN model are reliable. Our findings clearly show that single-cell Raman spectroscopy combined with SAM-mediated CNN configuration has great potential in performing early diagnosis of Nb spores and monitoring pebrine disease.