Introduction: Harvested blueberries can be processed into wine to extend their shelf life and increase their commercial value. In order to produce fruit wine, external sugar is often added prior to fermentation to increase the final alcohol content to a target of 8–12% (v/v) to meet consumer expectations.Method: we explore the effect of 8–14% (w/w) sucrose on the physicochemical properties of blueberry wine throughout the main fermentation process. We monitor changes of alcohol content, sugar, color, phenol, acidity, anthocyanin, and odor.Results and discussion: We notice that sucrose affects the fermentation process and physicochemical composition of the final blueberry wine by fermentation rate, fermentation color and protection of functional substances protection. Additional sucrose extends the total time of fermentation, and increases wine acidity. The color of the wine is also affected, with added sugar darkening and yellowing the final product. Interestingly, the sucrose has a protective effect on anthocyanin levels, although total anthocyanin levels are still substantially reduced following fermentation. Finally, the additional sugar increases accumulation of volatile odor components, particularly alcohols and esters, as measured by an electronic nose. We conclude that an addition of 12% sucrose produces wine with superior physicochemical properties of alcohol, anthocyanin loss and odor relative to other conditions tested and recommend this approach to commercial manufacturers.

• Low-salt pickling increased the content of SFA, PUFA and MUFA in salted eggs. • Low-salt process can achieve the same effect as traditional processes and is healthy alternative. • Phospholipids may be responsible for the unique quality of salted egg yolk.

The breaking capacity of rated current is one of the important indexes to evaluate the performance of circuit breakers, which is usually measured experimentally and cannot be analyzed in terms of the arcing characteristics of the opening process. Simulation methods based on the magnetohydrodynamic (MHD) model of the arc can be used to obtain the macroscopic motion of the arc within the interrupter and the interaction of the arc with the contacts, walls, and splitter plates. Therefore, this paper focuses on the arc interruption characteristics’ underrated current in low voltage circuit breakers by MHD simulation. A more accurate and effective field-circuit coupling MHD simulation model of low voltage circuit breaker products is developed in this paper. A nonlinear conductivity model of the sheath layer is considered to better simulate the near-pole voltage drop and bending processes after the arc has been cut by the splitter. The time-dependent magnetic field generated by the arc is considered in the calculation. Additionally, the real-time parameters of the external circuit are coupled to reflect the evolution of the arc characteristics under the action of the external circuit. The simulation results intuitively and clearly show the evolution of the arc during the breaking process. Through this, an arc extinguishing chamber can be designed to effectively regulate the arc interruption characteristics, thereby improving the breaking capacity of the circuit breaker. The accuracy and efficiency of the proposed simulation method is verified by experiments. This method can be extended to the performance analysis of AC/DC low voltage circuit breakers.

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with a probabilistic neural network (PNN) was applied to classify engineering structural metal samples (valve stem, welding material, and base metal). Additionally, utilizing data from the plasma emission spectrum generated by laser ablation of samples with different aging times, an aging time prediction model based on a firefly optimized probabilistic neural network (FA-PNN) was established, which can effectively evaluate the service performance of structural materials. The problem of insufficient features obtained by principal component analysis (PCA) for predicting the aging time of materials is addressed by the proposal of a time-frequency feature extraction method based on short-time Fourier transform (STFT). The classification accuracy (ACC) of time-frequency features and principal component features was compared under PNN. The results indicate that, in comparison to the PCA feature extraction approach, the time-frequency feature extraction method based on STFT demonstrates higher accuracy in predicting the time of aging materials. Then, the relationship between classification accuracy (ACC) and settings of PNN was discussed. The ACC of the PNN model for both the material classification test set and the aging time test set achieved 100% with Firefly (FA) optimization algorithms. This result was also compared with the ACC of ANN, KNN, PLS-DA, and SIMCA for the aging time test set (95%, 87.5%, 85%, and 62.5%, respectively). The experimental results demonstrated that the classification model using LIBS combined with FA-PNN could realize better classification accuracy.