FlourSpec: Cost-Effective Spectral Analysis for Trace-Level Detection of Flour Adulteration

Flour adulteration poses significant health risks and economic losses for consumers, yet current detection methods are often impeded by high costs, limited sensitivity, and impractical laboratory requirements, leaving end users vulnerable. The difficulty is exacerbated by the need for extremely low detection limits, often at parts-per-million (ppm) levels, to identify adulterants. To address these challenges, we introduce FlourSpec, a low-cost and user-friendly system designed for on-site detection of flour adulteration at the ppm level. FlourSpec employs a spectral reconstruction algorithm to extract pertinent information from coarse-grained spectral data collected using a low-cost spectrometer. Recognizing that existing spectral reconstruction algorithms often struggle with significant errors that obscure trace adulterant information, we develop a novel end-to-end architecture that balances spectral fidelity and classification performance while minimizing the propagation of reconstruction errors. Additionally, we incorporate a hybrid attention mechanism to capture harmonic correlations within the spectra, effectively suppressing cross-band reconstruction errors. A supervised contrastive learning module is also incorporated to enhance the discriminability of trace features. Experimental evaluations demonstrate that FlourSpec achieves 97.44% accuracy in detecting multiple adulteration types and 93.12% accuracy in identifying various BPO concentrations. Notably, it is only 0.63% lower than expensive solutions and 18.74% higher than baseline systems at the same price, highlighting its effectiveness for trace-level detection.