S.aureus-SERS-AI

Project Overview This repository contains the Surface-Enhanced Raman Spectroscopy (SERS) dataset and machine learning code used to classify Staphylococcus aureus isolates into MRSA, ERSA, and SSA. The dataset accompanies the manuscript: “Standardization is the Key to Reliable SERS-Based Machine Learning for Antimicrobial Resistance Classification. What This Project Does Provides a SERS dataset consisting of three CSV files (MRSA, ERSA, SSA), each containing five isolates as separate sheets. Includes preprocessing, feature selection, and machine learning scripts. Demonstrates the effect of 12 data partitioning strategies on model performance. Enables reproducible classification of AMR-related S. aureus isolates. Why This Project Is Useful Offers one of the few public SERS datasets with clearly defined clinical isolates. Helps researchers evaluate standardization issues in SERS-based machine learning. Provides ready-to-run ML pipelines for: Preprocessing spectra Applying Boruta, LASSO, and PCA Training RF, SVM, kNN, NNET, and XGB models Serves as a foundation for benchmarking, clinical AMR studies, or SERS–AI algorithm development. Dataset Description The dataset consists of ~15,000 SERS spectra collected from: 5 MRSA isolates 5 ERSA isolates 5 SSA isolates Each isolate contributes ~1,000 spectra. Spectral Range 400–1800 cm⁻¹ 818 Raman shift positions 785 nm excitation wavelength 1-second integration time File Format Each .csv file contains: Rows: individual spectra Columns: Raman intensities for each wavenumber