Nikola Major

Rapid honey characterization and botanical classification by an electronic tongue

In this paper a commercial electronic tongue (αAstree, Alpha M.O.S.) was applied for botanical classification and physicochemical characterization of honey samples. The electronic tongue was comprised of seven potentiometric sensors coupled with an Ag/AgCl reference electrode. Botanical classification was performed by PCA, CCA and ANN modeling on 12 samples of acacia, chestnut and honeydew honey. The physicochemical characterization of honey was obtained by ANN modeling and the parameters included were electrical conductivity, acidity, water content, invert sugar and total sugar. The initial reference values for the physicochemical parameters observed were determined by traditional methods. Botanical classification of honey samples obtained by ANN was 100% accurate while the highest correlation between observed and predicted values was obtained for electrical conductivity (0.999), followed by acidity (0.997), water content (0.994), invert sugar content (0.988) and total sugar content (0.979). All developed ANN models for rapid honey characterization and botanical classification performed excellently showing the potential of the electronic tongue as a tool in rapid honey analysis and characterization. The advantage of using such a technique is a simple sample preparation procedure, there are no chemicals involved and there are no additional costs except the initial measurements required for ANN model development.

Application of a potentiometric sensor array as a technique in sensory analysis

This paper reports on the application of a potentiometric sensor array used for monitoring changes in probiotic fermented milk during storage, classification of probiotic fermented milk according to flavor and to accurately predict the results from a human sensory panel. For that purpose the potentiometric electronic tongue consisting of seven sensors and an Ag/AgCl reference electrode was used. The samples of plain, strawberry, apple-pear and forest-fruit probiotic fermented milk were stored during 20 days on two different temperatures and monitored by the electronic tongue and the human sensory panel. Various pattern recognition techniques are adapted including multivariate data processing based on principal components analysis (PCA) for monitoring changes occurring in probiotic fermented milk, artificial neural networks (ANN) for the classification of probiotic fermented milk during storage, partial least square regression (PLS) and artificial neural networks (ANN) to estimate and predict the sensory panel evaluation results. The highest correct classification percentage (97%) was obtained for plain probiotic fermented milk and the lowest (87%) for apple-pear flavored probiotic fermented milk. The highest correlation between the sensor array and the human sensory panel was obtained for the forest-fruit flavored probiotic fermented milk both by using artificial neural networks (0.998) and partial least square regression (0.992). Results from these analyses demonstrate that the electronic tongue can be used to monitor changes in probiotic fermented milk during storage, to classify probiotic fermented milk according to flavor and to predict the sensory characteristics and their relationship to the quality of the probiotic fermented milk measured by consumer.

Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array.

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The alphaAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk. The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.