Effect of chitosan-Aloe vera coating on postharvest quality of blueberry (Vaccinium corymbosum) fruit

The present study was carried out to evaluate the effect of chitosan-based edible coatings with Aloe vera extract on the postharvest blueberry fruit quality during storage at 5 °C. Firstly, A. vera fractions (pulp and liquid) were extracted from leaves and evaluated in terms of antifungal and antioxidant capacities. The choice of the most adequate chitosan and A. vera fraction concentrations to be incorporated in coating formulation was made based on the wettability of the corresponding coating solutions. Coatings with 0.5% (w/v) chitosan + 0.5% (w/v) glycerol + 0.1% (w/v) Tween 80 + 0.5% (v/v) A. vera liquid fraction presented the best characteristics to uniformly coat blueberry surface. Physico-chemical (i.e., titratable acidity, pH, weight loss) and microbiological analyses of coated blueberries (non-inoculated or artificially inoculated with Botrytis cinerea) were performed during 25 d. Microbiological growth and water loss levels were approximately reduced by 50% and 42%, respectively, in coated blueberries after 25 d compared to uncoated blueberries. After 15 d, weight loss values were 6.2% and 3.7% for uncoated and chitosanA. vera coated blueberries, respectively. Uncoated fruits presented mold contamination after 2 d of storage (2.0 ± 0.32 log CFU g1), whilst fruits with chitosan-based coatings with A. vera presented mold contamination only after 9 d of storage (1.3 ± 0.35 log CFU g1). Overall, coatings developed in this study extend blueberries shelf-life for about 5 d, demonstrating for the first time that the combination of chitosan and A. vera liquid fraction as edible coating materials has great potential in expanding the shelf-life of fruits.

A novel hanging spherical drop system for the generation of cellular spheroids and high throughput combinatorial drug screening

We propose a novel hanging spherical drop system for anchoring arrays of droplets of cell suspension based on the use of biomimetic superhydrophobic flat substrates, with controlled positional adhesion and minimum contact with a solid substrate. By facing down the platform, it was possible to generate independent spheroid bodies in a high throughput manner, in order to mimic in vivo tumour models on the lab-on-chip scale. To validate this system for drug screening purposes, the toxicity of the anti-cancer drug doxorubicin in cell spheroids was tested and compared to cells in 2D culture. The advantages presented by this platform, such as feasibility of the system and the ability to control the size uniformity of the spheroid, emphasize its potential to be used as a new low cost toolbox for high-throughput drug screening and in cell or tissue engineering.

Electronic tongue: a versatile tool for mineral and fruit-flavored waters recognition

Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.