The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.