Multi-Scale No-Pooling Network for Geographical Origin Identification of Boletus via SERS

Frequent poisoning incidents associated with Boletus mushroom highlight the urgent need for rapid trace detection and precise geographical traceability to ensure public safety. However, traditional spectral analysis faces severe bottlenecks due to the strong interference of complex biological matrices and the highly overlapped spectral characteristics among different origins. To overcome these challenges, this study proposes a collaborative strategy combining surface-enhanced Raman spectroscopy (SERS) technology and a deep learning framework. Using gold nanorods (Au NRs) as the SERS enhancement substrate, the ultra-sensitive detection of the Boletus toxin protein bolesatine peptide was achieved, and key molecular fingerprint information such as tryptophan (733 cm-1) and tyrosine (634 cm-1) could be effectively detected from complex matrices. Subsequently, a multi-scale no-pooling network (MNNet) was constructed, innovatively adopting a dual-stream parallel architecture, which can simultaneously encode microscopic fingerprint details and macroscopic spectral contour trends. The model directly introduced the "no-pooling" mechanism, discarding the downsampling operation in traditional convolutional networks, thereby strictly preserving the spectral-index-to-wavenumber correspondence of Raman shifts and effectively avoiding the loss of key chemical structure information. Under the current internal evaluation setting, the MNNet-SVM framework achieved an accuracy of 97.69% in classifying Boletus samples from four geographical origins and outperformed the tested one-dimensional comparison frameworks. SHAP interpretation and spectral-window masking further indicated that the classification relied on distributed spectral information rather than exclusively on the dominant bolesatine-associated bands. This study successfully established a promising SERS-assisted multivariate analytical strategy for the detection of toxic components and origin identification in complex public safety systems.