Drivers of coffee liking: Effect of physicochemical characteristics and aromatic profile on consumers' acceptability of mono-origin and mono-variety coffees.

Few studies have investigated at the same time how physicochemical, volatile, and sensory characteristics affect coffee liking. The aim of this study is to evaluate the influence of geographical origin and variety on physicochemical parameters and volatile compounds composition of mono-origin and mono-variety coffees. Check-all-that-apply (CATA) method was used with the aim of identifying the drivers of coffee liking. Moisture content, bulk density, solubility index, color parameters, and phenols and caffeine content were useful parameters for discriminating Robusta from Arabica variety, but not samples from different origins. The hierarchical cluster and principal component analyses performed on the headspace compositions showed a quite sharp ability to group the samples based on their variety. Based on CATA results, roasted attribute, related to the presence of pyrazines, was considered a positive driver of coffee liking unlike grassy and acidic attributes (associated to the presence of acids and aldehydes, respectively). Findings from this study can be a useful tool for coffee manufacturers for a winning market strategy, helping them in the choice of the most suitable raw materials and process conditions in order to produce a well-balanced beverage by enhancing all the possible positive drivers of acceptability and reducing the negative ones.

Assessment of brewing attitude of unmalted cereals and pseudocereals for gluten free beer production.

In this study, 40% of unmalted gluten free (GF) grains (sorghum, millet, buckwheat, quinoa and amaranth) was used in brewing process, in gelatinized and ungelatinized form, in order to produce GF beer and to extend current knowledge about their suitability as brewing adjuncts. Partial replacement of barley malt with GF grains led to a significant decrease of extract (°P) and alcohol (%v/v) content compared to control beer (p < 0.05), except for quinoa beer (QB). Results from Principal Component Analysis (PCA) highlighted a satisfactory classification of experimental beers according to the two different forms of GF grains (gelatinized and ungelatinized). However, beers brewed with ungelatinized grains (mainly sorghum and quinoa) showed acceptable technological and sensory properties, thus suggesting that the pre-gelatinization step could be bypassed with a view to more environmentally and economically sustainable time-saving process. In addition, all beer samples showed a gluten content higher than 20 ppm.

Optimization Model of Phenolics Encapsulation Conditions for Biofortification in Fatty Acids of Animal Food Products.

The nutritional quality of animal products is strongly related to their fatty acid content and composition. Nowadays, attention is paid to the possibility of producing healthier foods of animal origin by intervening in animal feed. In this field, the use of condensed tannins as dietary supplements in animal nutrition is becoming popular due to their wide range of biological effects related, among others, to their ability to modulate the rumen biohydrogenation and biofortify, through the improvement of the fatty acids profile, the derivate food products. Unfortunately, tannins are characterized by strong astringency and low bioavailability. These disadvantages could be overcome through the microencapsulation in protective matrices. With this in mind, the optimal conditions for microencapsulation of a polyphenolic extract rich in condensed tannins by spray drying using a blend of maltodextrin (MD) and gum Arabic (GA) as shell material were investigated. For this purpose, after the extract characterization, through spectrophotometer assays and ultra-high-performance liquid chromatography-quadrupole time-of-flight (UHPLC-QTOF) mass spectrometry, a central composite design (CCD) was employed to investigate the combined effects of core:shell and MD:GA ratio on the microencapsulation process. The results obtained were used to develop second-order polynomial regression models on different responses, namely encapsulation yield, encapsulation efficiency, loading capacity, and tannin content. The formulation characterized by a core:shell ratio of 1.5:5 and MD:GA ratio of 4:6 was selected as the optimized one with a loading capacity of 17.67%, encapsulation efficiency of 76.58%, encapsulation yield of 35.69%, and tannin concentration of 14.46 g/100 g. Moreover, in vitro release under varying pH of the optimized formulation was carried out with results that could improve the use of microencapsulated condensed tannins in animal nutrition for the biofortification of derivates.

Prediction of the antioxidant activity of extra virgin olive oils produced in the Mediterranean area.

A chemical characterisation was conducted on 75 commercial extra virgin olive oils (EVOO) produced in the years 2011-2012 in Southern Italy from five different olive monovarieties (Coratina, Leccino, Maiatica, Ogliarola del Vulture and Ogliarola del Bradano). The possibility of estimating the antioxidant activity of EVOO by using a chemical index as predictor of this property was considered. In order to build up and validate an antioxidant activity predictive model, the relationship between the antioxidant activity and the chosen chemical parameters was systematically investigated. The results indicated that oil antioxidant activity, measured as IC50, could be satisfactorily predicted, for olive oils from the considered region, by using a simple index, such as the K225 value of oil samples, which represents a spectrophotometric index of the compounds responsible for oil bitterness measured at 225 nm.

Extra virgin olive oil bitterness evaluation by sensory and chemical analyses.

An experimental investigation was performed on blend extra virgin olive oils (EVOOs) from different cultivars and EVOO from different olive monovarieties (Coratina, Leccino, Maiatica, Ogliarola) with the aim to evaluate the possibility of estimating the perceived bitterness intensity by using chemical indices, such as the total phenol content and the compounds responsible for oil bitterness measured spectrophotometrically at 225 nm (K225 value), as bitterness predictors in different EVOO. Therefore, a bitterness predictive model, based on the relationship between the perceived bitterness intensity of the selected stimuli and the chosen chemicals parameters has been built and validated. The results indicated that the oil bitterness intensity could be satisfactorily predicted by using the K225 values of oil samples.