Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

The use of biomimetic measurement systems as electronic tongues and noses has considerably increased in the last years. This paper presents the design of a low-cost electronic tongue system that includes a software application that runs on a personal computer and electronic equipment based on a 16-b microcontroller. The designed system is able to implement voltammetry and impedance spectroscopy measurements with different-electrode configurations. The data obtained from the electrochemical measurements can be used to build statistical models able to assess physicochemical properties of the samples. The designed system has been applied to the detection and quantification of trinitrotoluene (TNT), which is one of the most common explosive materials. Pulse voltammetry measurements were carried out on TNT samples with different concentration levels. The principal component analysis of the obtained results shows that the electronic tongue is able to detect TNT in acetonitrile samples. Prediction models were built with partial least squares regression, and a good correlation was observed between the pulse voltammetry measurements and the TNT concentration levels. In this experience, a new voltammetric data compression algorithm based on polynomial approximations has been tested with good results. The electronic tongue has also been applied to the prediction of water quality parameters in wastewater and to the evaluation of different-pulse array designs for pulse voltammetry experiences.