A 3D correlation spectroscopy framework for spectral feature decoupling: Enabling precise authentication and quantification of dairy cream fat adulteration.

BACKGROUND

Traditional Raman spectroscopy for dairy cream fat (DCF) authentication is often severely compromised by temperature perturbations, which alter crystalline orderliness and obscure low-concentration adulteration signals. To overcome this limitation, this study establishes a novel analytical framework integrating Raman spectroscopy, three-dimensional correlation spectroscopy (3D-COS), and a custom 3D-CNN architecture.

RESULTS

By constructing 3D spectral data cubes under dual perturbations (temperature and concentration), 3D-COS successfully decoupled physical thermal responses from chemical structural disruptions. Spectral analysis revealed that temperature modulates the triglyceride packing conformation, altering the vibrational uniformity at 960 cm-1, whereas adulterants explicitly disrupt the crystalline network, producing distinct variations at 960 cm-1 and 1655 cm-1. To thoroughly exploit these high-dimensional features, a 3D-CNN model was developed and rigorously evaluated using sample-level dataset partitioning to prevent data leakage. The proposed 3D-CNN effectively captured the spatial-concentration coupling features, outperforming PLSR, SVR, XGBoost, 1D-CNN, and 2D-CNN baselines. Ultimately, the framework achieved 100% qualitative classification accuracy and exceptional quantitative precision (R2 > 0.99) for adulterants.

SIGNIFICANCE: This study overcomes the analytical limitations caused by temperature fluctuations. By effectively decoding the highly discriminative coupled features across wavenumber space and concentration dimensions, the proposed 3D-COS and 3D-CNN framework provides a reliable, anti-interference, and mechanistically grounded technical solution for rapid dairy authenticity screening.