Sensory and chemical profile of a phenolic extract from olive mill waste waters in plant-base food with varied macro-composition.

Phenols from olive mill waste water (OMWW) represent valuable functional ingredients. The negative impact on sensory quality limits their use in functional food formulations. Chemical interactions phenols/biopolymers and their consequences on bioactivity in plant-base foods have been widely investigated, but no studies to date have explored the variation of bitterness, astringency and pungency induced by OMWW phenols as a function of the food composition. The aim of the paper was to profile the sensory and chemical properties of phenols from OMWW in plant-base foods varied in their macro-composition. Four phenol concentrations were selected (0.44, 1.00, 2.25, 5.06 g/kg) to induce significant variations of bitterness, sourness, astringency and pungency in three plant-base food: proteins/neutral pH - bean purée (BP), starch/neutral pH - potato purée (PP), fiber/low pH - tomato juice (TJ). The macro-composition affected the amount of the phenols recovered from functionalized food. The highest recovery was from TJ and the lowest from BP. Two groups of 29 and 27 subjects, trained to general Labelled Magnitude Scale and target sensations, participated in the evaluation of psychophysical curves of OMWW phenols and of functionalized plant-base foods, respectively. Target sensations were affected by the food macro-composition. Bitterness increased with phenol concentration in all foods. Astringency and sourness slightly increased with concentration, reaching the weak-moderate intensity at the highest phenol concentration in PP and TJ only. Pungency was suppressed in BP and perceived at weak-moderate intensity in PP and TJ sample at the highest phenol concentration. Proteins/neutral pH plant-food (BP) resulted more appropriate to counteract the impact of added phenol on negative sensory properties thus allowing to optimize the balance between health and sensory properties.

Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC-MS analysis of volatile compounds.

An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with artificial neural network (ANN) method, to classify Pecorino cheeses according to their ripening time and manufacturing techniques. For this purpose different pre-treatments of electronic nose signals have been tested. In particular, four different features extraction algorithms were compared with a principal component analysis (PCA) using to reduce the dimensionality of data set (data consisted of 900 data points per sensor). All the ANN models (with different pre-treatment data) have different capability to predict the Pecorino cheeses categories. In particular, PCA show better results (classification performance: 100%; RMSE: 0.024) in comparison with other pre-treatment systems.

Evaluation of the antioxidant capacity of individual phenolic compounds in virgin olive oil.

Virgin olive oil has a high resistance to oxidative deterioration due to its tryacylglycerol composition low in polyunsaturated fatty acids and due to the presence of a group of phenolic antioxidants composed mainly of polyphenols and tocopherols. We isolated several phenolic compounds of extra virgin olive oil (phenyl-ethyl alcohols, lignans, and secoiridoids) by semipreparative high-performance liquid chromatography (HPLC) and identified them using ultraviolet, atmospheric pressure chemical ionization, and electrospray ionization MS detection. The purity of these extracts was confirmed by analytical HPLC using two different gradients. Finally, the antioxidant capacity of the isolated compounds was evaluated by measuring the radical scavenging effect on 1,1-diphenyl-2-picrylhydrazyl radical, by accelerated oxidation in a lipid model system (OSI, oxidative stability instrument), and by an electrochemical method.

Calculation of the molar absorptivity of polyphenols by using liquid chromatography with diode array detection: the case of carnosic acid.

Antioxidant activity of vegetable extracts is related to the nature and the amount of active components, mainly polyphenols; therefore, a correct quantification of these molecules should be required to define their concentration in such kind of vegetable extracts. A fast and accurate method to calculate molar absorption coefficients (epsilon), by using HPLC, has been tested on standard polyphenols and caffeine, and should be widely adapted for standardless quantitative analysis. Molar absorptivity (epsilon) of carnosic acid (CA) was determined from 200 to 300 nm, by the proposed method and those values were compared to tert-butyl-hydroxytoluene (BHT) ones for further comparative quantification.