基于电子舌的电子烟甜度评价模型研究

Research on e-cigarettes' sweetness evaluation models based on electronic tongue data

为了建立电子烟甜度定量分析的客观方法,利用电子舌对60个电子烟液样品进行测定得到60组数据,通过偏最小二乘、人工神经网络和支持向量机三种方法对电子舌测定数据与人工感官数据进行关联分析,建立了三个电子烟甜度评价模型。三个模型的比较结果显示,支持向量机法所建立的模型对未知电子烟样品甜度预测结果最为可靠,其中该模型的相关系数为0.96,预测结果的平均相对误差为7.30%,预测结果的均方根误差为0.61。由此可知,电子舌结合支持向量机法所建立的评价模型,可以实现对未知电子烟甜度的可靠预测。

The aim of this work is to establish objective methods to quantitatively analyze e-cigarettes＇ sweetness. Sixty samples of e-liquid were collected and measured by electronic tongue. Based on correlation analysis of electronic tongue data and artificial sensory data, three sweetness evaluation models were established by partial least squares, artificial neural network and support vector machine. Comparison results indicated that the support vector machine model was the most reliable for predicting sweetness of unknown e-cigarette samples. The correlation coefficient of the model was 0.96 with average relative error of predicted results of 7.30% and root mean square error of predicted results of 0.61. It was concluded that the evaluation model based on the combination of electronic tongue and the support vector machine can achieve reliable prediction of unknown e-cigarettes＇ sweetness.