Effect of five enological practices and of the general phenolic composition on fermentation-related aroma compounds in Mencia young red wines

The effects of five technological procedures and of the contents of total anthocyanins and condensed tan- nins on 19 fermentation-related aroma compounds of young red Mencia wines were studied. Multifactor ANOVA revealed that levels of those volatiles changed significantly over the length of storage in bottles and, to a lesser extent, due to other technological factors considered; total anthocyanins and condensed tannins also changed significantly as a result of the five practices assayed. Five aroma compounds pos- sessed an odour activity value >1 in all wines, and another four in some wines. Linear correlation among volatile compounds and general phenolic composition revealed that total anthocyanins were highly related to 14 different aroma compounds. Multifactor ANOVA, considering the content of total anthocy- anins as a sixth random factor, revealed that this parameter affected significantly the contents of ethyl lactate, ethyl isovalerate, 1-pentanol and ethyl octanoate. Thus, the aroma of young red Mencia wines may be affected by levels of total anthocyanins

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained