
Using a custom-built 785 nm Raman instrument, a recent study identified 14 pesticides and employed multivariate and machine learning techniques—particularly Random Forests (RF)—to automate classification. Readers will learn practical steps in spectral acquisition, spectral comparison across wavelengths, data preprocessing, and implementing machine learning models for real-world chemical monitoring (1).




























