As a technology with the essence of unsteady heat transfer ability, the stir-fry process could improve the flavor of food significantly. Basically, the effects of different cooking time and temperature on the flavor characterization of Agaricus bisporus in novel ready-to-eat dishes (RTE dishes) were investigated in the present study, aiming to provide data basis for the development and production of edible mushroom RTE dishes.

The volatile flavors of A. bisporus under different conditions were evaluated by electronic nose firstly, followed by gas chromatography-ion mobility spectrometry (GC-IMS) and free fatty acid content determination. Then, the non-volatile flavor analyzation through electronic tongue, soluble sugar (alcohol), free amino acids, flavor nucleotides and organic acids detection, as well as the evaluation of equivalent freshness concentration (EUC) were carried out. At last, the sensory quality analysis was utilized to estimate the differences in shape, color, flavor, organization and taste of A. bisporus in novel RTE dishes under different conditions, from which the quality of different products was evaluated based on consumers' subjective perception.

The whole stir-fry process could be divided into three stages, mainly including 2 min, 4-8 min and 10 min based on the cooking time or 160-170℃, 180-190℃ and 200℃ based on the cooking temperature. The contents of 1-octene-3-ol and 3-octanol were considered the characteristic odor components of A. bisporus, which increased with the extension of cooking time. On the other side, the contents of 1-octene-3-ol and 3-octanol reached the maximum value at 180℃ with the increasing of cooking temperature. From the perspective of umami, the time of 4 min was the optimal time for the formation of non-volatile flavor of A. bisporus in RTE dishes. As for the cooking temperature, umami and sweet taste reached the highest peak at 180℃ with the increase of the cooking temperature. Moreover, the contents of soluble sugar (alcohol) and organic acids in the RTE dishes were decreased and increased, respectively, along with the cooking time extension. However, with the increasing of cooking temperature, the contents of soluble sugar (alcohol) increased first and then decreased, while the contents of organic acids displayed an increasing trend. In addition, it was found that different cooking conditions exhibited little effects on nucleotide, but longer cooking time or higher cooking temperature would reduce the contents of free amino acids in the RTE dishes. Finally, in accordance with the analysis of EUC and sensory evaluation, the umami and sensory evaluation of A. bisporus under the cooking time of 4 min and cooking temperature of 180℃ presented the highest score, indicating that the products prepared under this stir-fry conditions were mostly accepted by consumers.

In order to maintain the maximum freshness and sweetness, as well as the most suitable flavor of A. bisporus in the RTE dishes, the final stir-fry process conditions was intended to be stir-frying at 180℃ for 4 min.

Food 3D printing technology, a promising technology in the field of food, can be affected by multiple factors and thus has problems, such as difficulty in determining printing parameters and poor ability of predicting printing accuracy. This paper aimed to seek out an effective modeling method to optimize 3D printing parameters of Pleurotus eryngii powder and to determine the optimal conditions for 3D printing.

Pleurotus eryngii powder and locust bean gum were adopted as 3D printing ink. Then, based on single-factor experiments, the central composite experimental design was performed to study the influence of four key process parameters - nozzle diameter, printing height, nozzle movement speed and fill density - on the accuracy of 3D printing. In order to optimize 3D printing parameters of Pleurotus eryngii powder, response surface methodology (RSM) and artificial neural network and genetic algorithm (ANN-GA) were employed to achieve different effects.

The determination coefficient (R²), root mean square error (RMSE), relative error (RE), and optimal value of prediction (VOP) of RSM model were 0.8817, 0.2314, 72.73%, and 0.148, respectively; the R², RMSE, RE, and optimal VOP of ANN-GA model were 0.9389, 0.2269, 33.85%, and 0.215, respectively. The ANN-GA model obtained higher R², lower RMSE and RE, and was better fitting ability, and higher optimal VOP than RSM model, so ANN-GA model possessed better prediction ability. Compared with RSM, ANN-GA was more suitable for optimization of 3D printing parameters of Pleurotus eryngii powder. The optimal process parameters of 3D printing obtained by ANN-GA, with Pleurotus eryngii as printing ink, included nozzle diameter 1.2 mm, printing height 1.1 mm, nozzle movement speed 24 mm·s^-1, and fill density 84%. Experimental verification suggested that the deviation of printed samples by ANN-GA was 0.325, which was superior to the actual printing deviation 0.550 by RSM.

ANN-GA was effective in determining the optimal process parameters of 3D printing and accurate in predicting the accuracy of food 3D printing products. Therefore, ANN-GA could serve as an effective and convenient method for optimizing personalized 3D printing parameters of agricultural products and food.