Analysis of Milk Using a Portable Potentiometric Electronic Tongue Based on Five Polymeric Membrane Sensors.

A portable potentiometric electronic tongue (PE-tongue) was developed and applied to evaluate the quality of milk with different fat content (skimmed, semi-skimmed, and whole) and with different nutritional content (classic, calcium-enriched, lactose-free, folic acid-enriched, and enriched in sterols of vegetal origin). The system consisted of a simplified array of five sensors based on PVC membranes, coupled to a data logger. The five sensors were selected from a larger set of 20 sensors by applying the genetic algorithm (GA) to the responses to compounds usually found in milk including salts (KCl, CaCl2, and NaCl), sugars (lactose, glucose, and galactose), and organic acids (citric acid and lactic acid). Principal component analysis (PCA) and support vector machine (SVM) results indicated that the PE-tongue consisting of a five-electrode array could successfully discriminate and classify milk samples according to their nutritional content. The PE-tongue provided similar discrimination capability to that of a more complex system formed by a 20-sensor array. SVM regression models were used to predict the physicochemical parameters classically used in milk quality control (acidity, density, %proteins, %lactose, and %fat). The prediction results were excellent and similar to those obtained with a much more complex array consisting of 20 sensors. Moreover, the SVM method confirmed that spoilage of unsealed milk could be correctly identified with the simplified system and the increase in acidity could be accurately predicted. The results obtained demonstrate the possibility of using the simplified PE-tongue to predict milk quality and provide information on the chemical composition of milk using a simple and portable system.

Analysis of musts and wines by means of a bio-electronic tongue based on tyrosinase and glucose oxidase using polypyrrole/gold nanoparticles as the electron mediator.

A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process.

Analysis of organic acids and phenols of interest in the wine industry using Langmuir-Blodgett films based on functionalized nanoparticles.

A chemically modified electrode consisting of Langmuir-Blodgett (LB) films of n-dodecanethiol functionalized gold nanoparticles (SDODAuNP-LB), was investigated as a voltammetric sensor of organic and phenolic acids of interest in the wine industry. The nanostructured films demonstrated interfacial properties being able to detect the main organic acids present in grapes and wines (tartaric, malic, lactic and citric). Compared to a bare ITO electrode, the modified electrodes exhibited a shift of the reduction potential in the less positive direction and a marked enhancement in the current response. Moreover, the increased electrocatalytic properties made it possible to distinguish between the different dissociable protons of polyprotic acids. The SDODAuNP-LB sensor was also able to provide enhanced responses toward aqueous solutions of phenolic acids commonly found in wines (caffeic and gallic acids). The presence of nanoparticles increased drastically the sensitivity toward organic acids and phenolic compounds. Limits of detection as low as 10(-6) mol L(-1) were achieved. Efficient catalytic activity was also observed in mixtures of phenolic acid/tartaric in the range of pHs typically found in wines. In such mixtures, the electrode was able to provide simultaneous information about the acid and the phenol concentrations with a complete absence of interferences. The excellent sensing properties shown by these sensors could be attributed to the electrocatalytic properties of the nanoparticles combined with the high surface to volume ratio and homogeneity provided by the LB technique used for the immobilization. Moreover, the LB technique also provided an accurate method to immobilize the gold nanoparticles giving rise to stable and reproducible sensors showing repeatability lower than 2% and reproducibility lower than 4% for all the compounds analyzed.