Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution.

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This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin-Ciocalteu (FC) method and UV absorbance polyphenol index (I(280)) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L(-1) gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).

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