School of Microelectronics and Communication Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing 400044, China.
Sensors (Basel). 2020 Jan 31;20(3):772. doi: 10.3390/s20030772.
As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.
作为一种智能仪器，电子舌（E-tongue）通过电极传感器阵列和某些机器学习方法实现液体分析。大振幅脉冲伏安法（LAPV）是一种常规的 E-tongue 类型，它更喜欢在短时间内以高采样频率采集大量响应数据。因此，对于机器学习方法来说，需要一种快速有效的特征提取方法。考虑到传感器阵列响应中的大量共模分量（高相关信号）会降低机器学习模型的识别性能，我们提出了一种替代的特征提取方法，称为特征特异性增强（FSE），用于增强特征特异性和降低特征维度。所提出的 FSE 方法通过消除配对传感器响应中的共模信号来突出特异性信号。同时，利用径向基函数将原始特征投影到非线性空间中。此外，由于其快速速度和出色的灵活性，我们选择核极限学习机（KELM）作为识别部分。我们采用来自 LAPV E-tongue 的两个数据集来评估机器学习模型。一个是由设计的 E-tongue 用于饮料识别收集的，另一个是公共基准。为了进行性能比较，我们引入了几种由不同特征提取和识别方法组合而成的机器学习模型。实验结果表明，所提出的 FSE 与 KELM 相结合在准确性、时间消耗和内存成本方面明显优于其他模型。此外，还证明了所提出的模型的参数敏感性较低。
