An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.

Complex fluid–solid systems generally exist in process engineering. The cognition of complex flow systems depends on numerical and experimental methods. The computational fluid dynamics–discrete phase method simulation based on coarsening technology has potential application prospects in industrial-scale equipment. This review outlines the computational fluid dynamics–discrete phase method and its application in several typical types of process engineering. In the process research, more attention is paid to the dense condition and multiphase flow. Furthermore, the CFD-DPM and its extension method for comprehensive hydrodynamics modeling are introduced. Subsequently, the current challenges and future trends of the computational fluid dynamics–discrete phase method are proposed.

Soil penetration resistance (SPR) is an important indicator for soil strength which not only affects the growth of crop roots and crop yield but also is crucial in the design and selection of agricultural machinery. The determination of SPR in the laboratory is complex and time-consuming, while measuring SPR on-site shows high uncertainty at different times and locations due to soil heterogeneity. Therefore, this paper investigated the impact of soil parameters on SPR for paddy soils in the plastic state and then established a simple regression model to predict SPR using easy-to-obtain soil physical properties, including clay content, water content and density. Using the combined approaches of central composition rotatable design (CCRD) with response surface methodology (RSM), SPR of 20 soil samples from five paddy fields were measured in the laboratory. The results from the experiments showed that the contribution rate of each single factor to SPR from high to low was soil density, clay content and water content. Statistical analysis for the established equation suggested that the p-value for goodness of fit was significant (p < 0.001) and the p-value for lack of fit was insignificant (p > 0.05); meanwhile, the coefficient of determination (R²) was 0.95, indicating that the model was effective in predicting the SPR. Subsequently, the performance of the regression model was validated by comparing the estimated SPR with in situ field measurements, which showed high accuracy, with percent errors within 10%. Our study successfully proposed a method to estimate SPR using easy-to-measure soil properties that could be obtained from sensors in the soil or field investigations, including soil clay content, water content and wet bulk density.

A taste sensing system was used to determine the marked ages and flavours of rice wines. The taste sensing system consisted of five different artificial lipid–polymer membrane electrodes that were highly sensitive to the five basic tastes (umami, astringency, bitterness, sourness and saltiness). This taste sensing system can be used to show the quality and intensity of tastes in samples and detects these tastes in a manner similar to that of the human gustatory system. Three types of rice wine with the same flavour, but different marked ages, and three types of rice wine with same marked age, but different flavours were analysed. A “taste map” analysis was performed to determine taste intensity based on which the tastes could be quantitatively analysed without chemometric methods. The differences in flavour among the rice wines were clearly shown. The responses (including the change in membrane potential caused by adsorption) obtained by the electrodes were analysed using principal components analysis and discriminant function analysis for classification and partial least-squares regression and a support vector machine for forecasting. Discriminant function analysis performed better than principal components analysis in classifying the rice wines with different marked ages and had similar results to principal components analysis in classifying rice wines with different flavours. The support vector machine based on the leave-one-out cross-validation was more stable than partial least-squares regression and the support vector machine based on the ten-fold cross-validation in predicting the marked ages and flavours of different types of rice wine; the prediction correlations were R² = 0.9568 and R² = 9620, respectively.