Application of Electronic Tongue Combined With Convolutional Autoencoder and a Self-Adaptive Attention Mechanism to Identify the Botanical Origins of Honey

Honey manufacturers and traders have suffered economic damage due to the spurious labeling of honey botanical sources. The traditional detection methods for distinguishing honey origins have inherent performance disadvantages, such as costly equipment, complicated operations, and need more time. This study proposes a novel method for identifying the origin of honey by applying an electronic tongue (ET) and deep learning algorithm. First, a self-developed ET device is utilized to collect unique fingerprints of honey samples from different botanical origins. Due to the high dimensionality and complex nonlinearity of the collected signal, an unsupervised convolutional autoencoder (CAE) is developed to reduce the dimension of the ET signals and achieve effective feature extraction. Moreover, in order to avoid the gradient disappearance and suppress redundant information, the self-adaptive attention mechanism and residual learning are introduced in the CAE model to improve the feature extraction ability of the model. The proposed model is further used to measure actual honey samples and contrast with multiple machine learning and deep learning models. The experimental results show that the proposed model demonstrates better classification capabilities for honey from different botanical sources, and the accuracy, precision, F1, and area under the curve (AUC) score reach 99.25%, 99.14%, 0.9919, and 0.9957, respectively. Compared with the other detection methods, the proposed method exhibited advantages in terms of convenience and accuracy of detection, which has great application prospects in the field of on-site measurements.