Rapid Salmonella Serovar Classification Using AI-Enabled Hyperspectral Microscopy with Enhanced Data Preprocessing and Multimodal Fusion

Salmonella serovar identification typically requires multiple enrichment steps using selective media, consuming considerable time and resources. This study presents a rapid, culture-independent method leveraging artificial intelligence (AI) to directly classify Salmonella serovars from high-dimensional hyperspectral microscopy data. Five serovars (Enteritidis, Infantis, Kentucky, Johannesburg, 4,[5],12:i:-) were analyzed from overnight cultures prepared on glass slides using only sterilized deionized water. Per serovar, 100 hyperspectral data cubes were collected across six biological replicates (n=500 total), yielding single-cell spectra from segmented cells and RGB composite images representing the full microscopy field. Data analysis involved two parallel branches followed by multimodal fusion. The spectral branch compared two data preprocessing approaches: manual feature selection and data-driven feature extraction via principal component analysis (PCA). Single-cell spectra were classified using k-nearest neighbors, support vector machine, random forest, and multilayer perceptron (MLP), evaluated by accuracy, precision, and recall. The image branch employed a convolutional neural network (CNN) to extract spatial features directly from microscopy images without relying on explicit morphological descriptors. PCA-derived spectral features outperformed manual selection, with MLP achieving 81.1% accuracy. Multimodal MLP-CNN fusion further improved accuracy (82.4%) while reducing overfitting. This study demonstrates that AI-enabled hyperspectral microscopy with multimodal fusion can streamline Salmonella serovar identification workflows.