Introduction: “Eletronic tongue†is a device commonly used in the analysis of tastants, heavy metal ions, fruit juice, wines and also in the development of biosensors [1-3]. Briefly, the e-tongue is constituted by sensing units formed by ultrathin films of distinct materials deposited on gold interdigitated electrodes, which are immersed in liquid samples, followed by impedance spectroscopy measurements [1]. The e-tongue sensor is based on the global selectivity concept, i.e., the materials forming the sensing units are not selective to any substance in the samples, therefore, it allows the grouping of information into distinct patterns of response, enabling the distinction of complex liquid systems [1].
Aim: Our aim was to use e-tongue system for the assessment the homeopathic medicine Belladonna at different degrees of dilution, in attempt to differentiate highly diluted systems.
Methods: Ultrathin films forming the sensing units were prepared by the layer-by-layer technique [4], using conventional polyelectrolytes such as poly(sodium styene sulfonate) (PSS) and poly(allylamine) hydrochloride (PAH), chitosan and poly(3,4-ethylenedioxythiophene) (PEDOT). Homeopathic medicines (Belladonna 1cH, 6cH, 12cH and 30cH) were prepared by dilution and agitation according to Hahnemann´s method [5], using ethanol at 30% (w/w) as vehicle. Experimental data acquisition was conducted by blind tests measurements involving Belladonna samples and the vehicle used in the dilutions. Five independent and consecutive measurements were taken for each solution at 1 kHz, which were further analysed by Principal Component Analysis (PCA), a statistical method largely employed to reduce the dimensionality of the original data without losing information in the correlation of the samples [3].
Results: Figure 1 shows that the five independent measurements are grouped quite closed each other for each solution analysed, with a clear distinction of them. Therefore, it was noticed a change in the observed pattern measured at different days, indicating a reduced reproducibility, although the groups of data could still be identified.
Discussion: PCA is a powerful tool highly employed to extract relevant information in the correlation of data analysis of e-tongue systems. PCA plots showed a good statistical correlation of the systems (PC1 + PC2 ³ 90%), with the solutions being straightforwardly distinguished each other and also from the vehicle used.
Conclusion: Despite the differences of data obtained along distinct days of analysis, the e-tongue could detect differences among the samples tested, even considering the highly diluted cases studied.
Statistical analysis and characterization of Landsat 8 satellite images of forest wildfires regions
