Fingerprint spectrum construction for Chinese wolfberry (CW) origin tracing and key element prediction via...

Chinese wolfberry (CW), an essential traditional functional food in China, faces challenges like market confusion about its origin and counterfeiting. A multimodal workflow integrating computer vision and inductively coupled plasma mass spectrometry (ICP-MS) was applied to 120 CW batches from four regions. Chromaticity, visible-light response, Gabor texture, and elemental data were combined to construct a fingerprint for origin discrimination and elemental prediction. Multivariate statistical analysis identified As, Zn, Fe, Energy, yellow-light response, CIE Z, and b^⁎ as key variables with variable importance in projection (VIP) scores greater than 1. After VIP screening, convolutional neural network (CNN) achieved 100.0% training and held-out test accuracy, with a five-fold cross-validation balanced accuracy of 0.988 ± 0.018 and a test Macro-F1 of 1.000; processing time decreased by 31.5%. Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) yielded test R² values of 0.967-0.977 and RPDp values of 5.52-6.59 for As, Zn, and Fe, outperforming conventional regressors. These findings support multimodal CW evaluation, while broader validation will further improve model robustness.