Classification and quantification of sodium metabisulfite in goji berry powder: Applications of hyperspectral technology and transformer-based hybrid models.

Sodium metabisulfite (Na2S2O5) is widely used as an antioxidant and preservative in food products, but excessive residues pose health risks and are strictly regulated. Rapid and non-destructive detection methods are therefore essential, particularly for goji berry powder where sulfite addition is common during processing. In this study, near-infrared hyperspectral imaging (HSI, 900-1700 nm) was employed in combination with advanced deep learning models to classify and quantify sodium metabisulfite concentrations. A total of 360 samples (nine concentration levels including control, 40 replicates each) were prepared, with 70 % allocated for training and 30 % for testing. For classification, a hybrid ResLocalformer model that integrates local attention and residual paths within a Transformer framework achieved an accuracy of 97.22 %. For regression, the Resformer model, combining Transformer's global attention with dense layers, yielded an R2 of 0.9945 and RMSE of 0.0343. Spectral preprocessing using first-derivative Gaussian smoothing (1DER-GS) significantly enhanced feature quality, improving overall model performance by more than 40 %. Compared with conventional approaches such as CNN and LSTM, the proposed models demonstrated superior robustness and predictive accuracy. These results indicate that HSI combined with transformer-based hybrid models provides an effective and non-destructive approach for monitoring sodium metabisulfite in goji berry powder, with strong potential for extension to the detection of other food additives and matrices.