Reverse AIEE/ACQ Engineering in a Carbon Dot-Quantum Dot Ratiometric Nanosensor: Machine-Learning-Driven Quantitative and Recognition Detection of Biogenic Amines for Food Freshness Monitoring.

Abstract

Simultaneous profiling of biogenic amines, including spermine, putrescine, cadaverine, histamine, and tyramine, is crucial for assessing food freshness, tracking spoilage progression, and preventing associated foodborne illnesses and adverse health effects. Herein, we report a chemically unmodified dual-emissive nanosensor constructed by noncovalent integration of green carbon dots (G-CDs) and near-infrared CdTe quantum dots (QD700). The sensing mechanism arises from competitive modulation of excited-state relaxation pathways via multiple noncovalent interactions (electrostatic attraction, multivalent hydrogen bonding, and van der Waals forces): Spe induces aggregation-induced emission enhancement (AIEE) in G-CDs while concurrently triggering aggregation-caused quenching (ACQ)-fluorescence resonance energy transfer (FRET) in QD700, resulting in ratiometric fluorescence quenching with a distinct redshift. The system achieves an ultralow detection limit of 0.0097 μM and responds within 2 s. Unique response fingerprints toward five biogenic amines (Spe, Cad, Put, His, Tyr), coupled with a custom DETR with Improved Matching (DEIM), enable high-accuracy synchronous discrimination. Integrated into a paper-based smartphone platform, the method allows visual shrimp freshness assessment (red = fresh, green = spoiled) with excellent correlation to the standard TVB-N assay (R2 = 0.95). This work establishes a "single-probe, specific-detection, multidiscriminant" paradigm for on-site, dynamic freshness monitoring.