ABSTRACT

Polysaccharide-based edible coating can be useful as a pretreatment for drying since it prevents the oxidation of nutritional compounds, thereby improving the quality of dried products. In this study, the effects of polysaccharide coating (xanthan and balangu seed gums) on the drying kinetics of apricot slices were investigated. In addition, genetic algorithm–artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used for prediction of drying time (DT) and moisture content (MC) of coated apricot slices in an infrared (IR) dryer. The GA-ANN and ANFIS were fed with two inputs of IR radiation intensity (150, 250, and 375 W) and the distance of slices from lamp surface (5, 7.5, and 10 cm) for prediction of average DT. Also, to predict the MC, these models were fed with three inputs of IR power, lamp distance, and treatment time. The developed GA-ANN, which included seven hidden neurons, predicted the DT of apricot slices with a correlation coefficient (r) of 0.970. Also, the GA-ANN model with nine neurons in one hidden layer predicts the MC with a high r-value (r = 0.999). The calculated r-values for prediction of DT and MC using the ANFIS-based subtractive clustering algorithm were 0.986 and 0.999, respectively, which showed a high correlation between predicted and experimental values. Sensitivity analysis results showed that IR intensity and treatment time were the most sensitive factors for prediction of DT and MC of coated apricot slices, respectively. Both GA-ANN and ANFIS models’ predictions agreed well with testing data sets, and they could be useful for understanding and controlling the factors affecting on drying kinetics of apricot slices in an IR dryer.

摘要

基于多糖的可食用涂层可以用作干燥的预处理，因为它可以防止营养化合物的氧化，从而提高干燥产品的质量。在这项研究中，研究了多糖包衣（黄原胶和巴兰固种子胶）对杏片干燥动力学的影响。此外，遗传算法-人工神经网络（GA-ANN）和自适应神经模糊推理系统（ANFIS）模型用于预测红外条件下涂层的杏片的干燥时间（DT）和水分含量（MC） ）烘干机。为GA-ANN和ANFIS提供了两个输入，分别是IR辐射强度（150、250和375 W）以及切片与灯表面的距离（5、7.5和10 cm），以预测平均DT。另外，要预测MC，这些模型需要输入IR功率，灯距，和治疗时间。发达的GA-ANN（包括七个隐藏的神经元）以相关系数预测了杏片的DT（r）为0.970。同样，在一个隐藏层中具有9个神经元的GA-ANN模型预测MC的r值较高（r = 0.999）。使用基于ANFIS的减法聚类算法计算的DT和MC预测的r值分别为0.986和0.999，这表明预测值和实验值之间具有高度相关性。敏感性分析结果表明，红外强度和处理时间分别是预测杏皮切片DT和MC的最敏感因素。GA-ANN和ANFIS模型的预测都与测试数据集非常吻合，它们对于理解和控制影响IR干燥机中杏片干燥动力学的因素很有用。