Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network

Abstract

Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modeling

“…Commonly used parameter optimization methods such as Grid search (GS) and Genetic Algorithm (GA) generally have shortcomings such as long time consumption, not considering previous parameter information when traversing the next parameter, and being prone to falling into local optima [21] . Bayesian optimization (BO) focuses on reducing evaluation costs, with fewer iterations and faster speed, taking into account previous parameter information [22] .…”

Origin traceability and adulteration detection of soybean using near infrared hyperspectral imaging

“…Moreover, orthogonal PLS‐DA model showed great potential to distinguish the red raspberry of three protected geographical indication regions (Wang et al., 2022). Multitask residual fully convolutional network under Bayesian optimized architecture was used to build the near four regions of Ningxia wolfberries model, and the accuracy was 96.43% (Cui et al., 2022). In general, NIR‐HSI combined with the multivariate analysis showed great performance in detection of the origin traceability of agricultural products.…”

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

“…Linoleic acid is characterized by its identical molecular groups and carbon chain structure in the red meat, and exhibits analogous composition to the components and muscle fiber structure of red meat. The development of a spectroscopic method for the detection of red meat coupled with the construction of a chemometrics analytical model holds significant prospective value for the advancement of fresh meat industry [16,17]. This study was focused on detecting linoleic acid content in mixed red meat from various cultivars, locations, and species and on constructing highly robust predictive models.…”