The performance of an electronic tongue can be optimized by varying the number and types of sensors in the array and by employing data-processing methods. Sensor selection is typically performed empirically, with sensors picked up either by analyzing their characteristics or through trial and error, which does not guarantee an optimized sensor array composition. This study focuses on developing a method for sensor selection for an electronic tongue using simulated sensor data and Lasso regularization. Simulated sensor responses were calculated using sensor parameters such as sensitivity and selectivity, which were determined in the individual analyte solutions. Sensor selection was carried out using Lasso regularization, which removes redundant or highly correlated variables without much loss of information. The objective of the optimization of the sensor array was twofold, aiming to minimize both quantification errors and the number of sensors in the array. The quantification of toxins belonging to one of the groups of marine toxins—paralytic shellfish toxins (PSTs)—using arrays of potentiometric chemical sensors was used as a case study. Eight PSTs corresponding to the toxin profiles in bivalves due to the two common toxin-producing phytoplankton species, G. catenatum (dcSTX, GTX5, GTX6, and C1+2) and A. minitum (STX, GTX2+3), as well as total sample toxicity, were included in the study. Experimental validation with mixed solutions of two groups of toxins confirmed the suitability of the proposed method of sensor array optimization with better performance obtained for the a priori optimized sensor arrays. The results indicate that the use of simulated sensor responses and Lasso regularization is a rapid and efficient method for the selection of an optimized sensor array.
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