Encapsulation has great potential for preserving the flavour and health benefits of bioactive compounds. Hence, in the previous study, an attempt was made to encapsulate peppermint flavour in a gum arabic (GA) shell. To further understand the effect of a wide range of parameters, in the present study, artificial neural networks (ANNs) are developed. To predict the effect of various parameters on the encapsulation process, networks are developed with a back-propagation algorithm. Input parameters for the ANN are flavour concentration, GA concentration, spray dryer temperature and feed flow rate to the spray dryer. The encapsulation process is evaluated in terms of encapsulation efficiency, product yield, and particle size. To predict all outputs simultaneously, a combined model is developed. The results showed that the combined model has similar accuracy as that of the individual model and also helps to save on processing time. For the combined model, the best prediction performance is obtained with 5-4-3 ANN architecture exhibiting an R² value of 0.9991, and corresponding MSE values of 0.000 54, 0.000 63, and 0.000 61 for encapsulation efficiency, product yield, and particle size, respectively. This indicates that the developed ANN model is capable of predicting the encapsulation process. The interpolation and extrapolation ability of the developed network is also evaluated.

中文翻译：

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基于人工神经网络的薄荷味包封过程建模与超声方法

封装在保存生物活性化合物的风味和健康益处方面具有巨大潜力。因此，在之前的研究中，尝试将薄荷味封装在阿拉伯树胶 (GA) 外壳中。为了进一步了解各种参数的影响，在本研究中，开发了人工神经网络（ANN）。为了预测各种参数对封装过程的影响，使用反向传播算法开发了网络。人工神经网络的输入参数是风味浓度、GA 浓度、喷雾干燥器温度和喷雾干燥器的进料流速。包封过程根据包封效率、产品收率和粒径进行评估。为了同时预测所有输出，开发了一个组合模型。结果表明，组合模型与单个模型具有相似的准确性，并且还有助于节省处理时间。对于组合模型，最好的预测性能是通过 5-4-3 ANN 架构获得的，该架构展示了 R²值为 0.9991，相应的 MSE 值分别为 0.000 54、0.000 63 和 0.000 61，分别用于包封效率、产品收率和粒径。这表明开发的人工神经网络模型能够预测封装过程。还评估了已开发网络的插值和外推能力。