Accurately determining citrus origin is essential for establishing and maintaining regional brands with distinctive qualities while safeguarding the rights and interests of both farmers and consumers. In this study, 2693 navel orange samples were collected from 13 major producing regions in China to establish a comprehensive near-infrared spectroscopy (NIRS) dataset. To address the challenge of citrus origin authentication, this study proposes a novel six-layer one-dimensional convolutional neural network (1D-CNN). The classification accuracy of this model reaches 96.16%. Compared with the support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three-layer 1D-CNNs with kernel sizes of 3 and 16, the accuracy of the proposed six-layer model is improved by 9.65%, 3.21%, 3.84%, and 1.98%, respectively. Furthermore, the dataset is augmented using a Wasserstein Generative Adversarial Network (WGAN) and Noise Addition. The results indicate that data augmentation can effectively improve the accuracy of various algorithm models. Among them, the 1D-CNN proposed in this study achieves the best performance on the Noise Addition-augmented dataset, with its accuracy, precision, recall, and F1-score reaching 98.39%, 0.9843, 0.9839, and 0.9840, respectively. Compared with the other four comparative models, the accuracy of this model is increased by 1.48%, 1.36%, 1.48%, and 2.85%, respectively. Finally, a visual analysis of the 1D-CNN’s feature-extraction process was conducted. The results demonstrate that the 1D-CNN can effectively extract discriminative NIR spectral features to accurately distinguish citrus from different origins and that data augmentation markedly improves model performance by increasing data diversity. This work provides a robust tool for citrus origin tracing and offers a new perspective for the origin authentication of other agricultural products.

This study involved developing a novel lemon flower-scented white tea (LT) through multiple aroma-imparting cycles, and taking an integrated approach to investigating its flavour formation mechanism. Sensory evaluation and electronic tongue analysis revealed that the LT exhibited more balanced taste characteristics, with significantly reduced bitterness and astringency, attributed to the decreased caffeine content and conversion of esterified catechins. Electronic nose and HS-SPME/GC-MS results confirmed that the LT had acquired a distinctive aroma characterised by floral and citrus notes, primarily originating from lemon flower volatiles such as methyl anthranilate and limonene. Multivariate statistical analysis identified 32 key differential compounds (variable importance in projection value > 1), with methyl anthranilate, β-ionone, and geraniol (relative odour activity value > 80) jointly forming the shared flavour base among teas. These findings demonstrate that lemon flower infusion can effectively enhance the sensory quality of white tea and provide theoretical support for the development of diverse floral teas.