Dynamic changes in the water distribution and key aroma compounds of roasted chicken during roasting.

The effects of roasting times (0, 2, 4, 6, 8, 10, 12, and 14 min) on the dynamic changes of the water distribution and key aroma compounds in roasted chicken during the electric roasting process were studied. In total, 36 volatile compounds were further determined by GC-MS and 11 compounds, including 1-octen-3-ol, 1-heptanol, hexanal, decanal, (E)-2-octenal, acetic acid hexyl ester, nonanal, 2-pentylfuran, heptanal, (E, E)-2,4-decadienal and octanal, were confirmed as key aroma compounds. The relaxation time of T22 and T23 was increased first and then decreased, while the M22 and M23 in roasted chicken were decreased and increased with increasing roasting time, respectively. The fluidity of the water in the chicken during the roasting process was decreased, and the water with a high degree of freedom migrated to the water with a low degree of freedom. In addition, the L*, a*, b*, M23 and all amino acids were positively correlated with all the key aroma compounds, while T22, M22 and moisture content were negatively correlated with all the key aroma compounds.

Electrochemical detection of enrofloxacin in meat using bimetallic organic framework-derived NiCo2O4@NiO.

A novel electrochemical aptasensor based on a bimetallic organic frame-derived carbide nanostructure of Co and Ni (NiCo2O4@NiO) was prepared for rapid and sensitive enrofloxacin (ENR) detection of sheep and pork liver meats. The composite was fabricated by solvothermal and direct pyrolysis methods and dropped onto a modified electrode to improve the electron transfer efficiency. Furthermore, different techniques such as scanning electron microscopy and X-ray photoelectron spectroscopy were used to characterize the morphology and structure of the materials. Electrochemical impedance spectroscopy and cyclic voltammetry were used to evaluate the performance of the electrochemical sensor. As a result, the electrochemical aptasensor based on NiCo2O4@NiO exhibited excellent sensing performances for ENR with an extremely low detection limit of 1.67 × 10-2 pg mL-1 and a broad linear range of 5 × 10-2 to 5 × 104 pg mL-1, as well as great selectivity, excellent reproducibility, high stability and applicability. In addition, the relative standard deviation for real samples was in the range of 93.83 to 100.09% and 94.95 to 100.01% for sheep and pork liver. The results showed that the composite can be expected to greatly facilitate ENR detection and practical applications in harmful food due to the advantages of simple fabrication, controllable, large-area uniformity, environmental friendliness, and trace detection.

A new sensor for the rapid electrochemical detection of ractopamine in meats with high sensitivity.

In this research, g-C3N4/Cu@CoO/NC, which contained graphitic phase carbon nitride (g-C3N4) with a binary nanostructure and Cu@CoO/NC with a bimetallic MOF precursor, was constructed by a low-temperature pyrolysis process. The g-C3N4/Cu@CoO/NC was characterised by several techniques, including X-ray diffraction, scanning electron microscope, transmission electron microscope and X-ray photoelectron spectroscopy. Further, it was used to prepare an electrochemical sensor for the detection of ractopamine (RAC) in meat samples. The sensor showed excellent electrochemical oxidation characteristics for RAC detection, with a wide linear range (0.005 µmol/L to 32.73 µmol/L) and low detection limit (1.53 nmol/L). Meanwhile, the reproducibility, stability and interference of the g-C3N4/Cu@CoO/NC/GCE sensor were found to be excellent. Besides, the g-C3N4/Cu@CoO/NC/GCE sensor was well-used for the detection of RAC in pork, pig liver and lamb samples with recovery rates ranging from 96.5 % to 102.2 %.

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

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.