Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.

This paper presents imaging of copper slag solidification processes using electromagnetic tomography (EMT). It describes key components of the measurement system including a sensor that can operate in the harsh temperature environments. An integrated process tomography system is detailed that includes the main processing unit, i.e. a field programmable gate array and other front end circuitries. It is capable of delivering a wide excitation signal frequency ranging from 0.1 Hz to 500 kHz and a fast speed of 131 frames s⁻¹ with a typical signal-to-noise ratio of 66 dB–95 dB. Measurement trials were conducted on a molten converter slag solidification process where the copper slag changes from the molten state to solidification state during the cool-down for more than an hour. The phenomenon in which the disorderly distributed metal gradually forms solid and permeable object was observed, which can indicate the status of the process. This is the first report of observing such a process using an EMT system. Moreover location-based convergence analysis has been carried out in the imaging space and useful new insights have been gained for the copper production process, which would be difficult to obtain otherwise. Verification and calibration using x-ray diffraction and scanning electron microscope indicate the viability of the EMT-based measurement method.

A new index, the estimate of the error variance, which can be used to quantify the evolution of the flow patterns when multiphase components or tracers are difficultly distinguishable, was proposed. The homogeneity degree of the luminance space distribution behind the viewing windows in the direct contact boiling heat transfer process was explored. With image analysis and a linear statistical model, the F-test of the statistical analysis was used to test whether the light was uniform and a non-linear method was used to determine the direction and position of a fixed source light. The experimental results showed that the inflection point of the new index was approximately equal to the mixing time. The new index has been popularized and applied to a multiphase macro mixing process by top blowing in a stirred tank. Moreover, a general quantifying model was introduced for demonstrating the relationship between the flow patterns of the bubble swarms and heat transfer. The results can be applied to investigate other mixing processes that are very difficult to recognize the target.