An artificial tongue composed of four sensors made from ultrathin films deposited onto gold interdigitated electrodes has been able to distinguish easily the four basic tastes (salty, sour, sweet, and bitter), in addition to detecting inorganic contaminants in ultrapure water and identifying different brands of coconut water. Some tastants were detected below the human threshold values, for example, 5 mM of NaCl or sucrose. Suppression of quinine by sucrose was also detected. The high sensitivity may be partially attributed to the ultrathin nature of the films as the sensors were produced with Langmuir−Blodgett films of the 16-mer polyaniline oligomer, polypyrrole, and a ruthenium complex and with self-assembled films of an azobenzene-containing polymer. The sensor response was evaluated with ac measurements taken at various frequencies, with the admittance being treated theoretically with an equivalent circuit representing the sensor immersed in a polyelectrolyte solution.

Novelty detection has been presented in the literature as one-class problem. In this case, new examples are classified as either belonging to the target class or not. The examples not explained by the model are detected as belonging to a class named novelty. However, novelty detection is much more general, especially in data streams scenarios, where the number of classes might be unknown before learning and new classes can appear any time. In this case, the novelty concept is composed by different classes. This work presents a new algorithm to address novelty detection in data streams multi-class problems, the MINAS algorithm. Moreover, we also present a new experimental methodology to evaluate novelty detection methods in multi-class problems. The data used in the experiments include artificial and real data sets. Experimental results show that MINAS is able to discover novelties in multi-class problems.

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