Potentiometric ETongue System for GeosminIsoborneol Presence Monitoring in Drinkable Water A potentiometric Etongue system based on lowselective polymeric membrane and chalcogenideglass electrodes is employed to monitor the tasteandodorcausing pollutants, geosmin (GE) and 2methylisoborneol (MIB), in drinkable water. The developed approach may permit a lowcost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ngL. The experiments demonstrate the success of the Etongue in combination with partial least squares (PLS) regression technique for the GEMIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ngL and at higher concentrations from 0.25 to 10 mgL by means of PLSdiscriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easytohandle tool for express evaluation of GEMIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users.

Electronic Tongue Recognition with Feature Specificity Enhancement As a kind of intelligent instrument, an electronic tongue (Etongue) realizes liquid analysis with an electrodesensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular Etongue 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 commonmode components (high correlated signals) in the sensorarray 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 Etongues have been adopted for the evaluation of the machinelearning models. One is collected by a designed Etongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machinelearning 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.