Storage At Low Temperature Differentially Affects The Colour And Carotenoid Composition Of Two Cultivars Of Banana.

Different storage conditions can induce changes in the colour and carotenoid profiles and levels in some fruits. The goal of this work was to evaluate the influence of low temperature storage on the colour and carotenoid synthesis in two banana cultivars: Prata and Nanicão. For this purpose, the carotenoids from the banana pulp were determined by HPLC-DAD-MS/MS, and the colour of the banana skin was determined by a colorimeter method. Ten carotenoids were identified, of which the major carotenoids were all-trans-lutein, all-trans-α-carotene and all-trans-β-carotene in both cultivars. The effect of the low temperatures was subjected to linear regression analysis. In cv. Prata, all-trans-α-carotene and all-trans-β-carotene were significantly affected by low temperature (p<0.01), with negative estimated values (β coefficients) indicating that during cold storage conditions, the concentrations of these carotenoids tended to decrease. In cv. Nanicão, no carotenoid was significantly affected by cold storage (p>0.05). The accumulation of carotenoids in this group may be because the metabolic pathways using these carotenoids were affected by storage at low temperatures. The colour of the fruits was not negatively affected by the low temperatures (p>0.05).

Influência da temperatura do ar de secagem no calor latente de vaporização de água em feijão macassar (Vigna unguiculata (L.) Walp.), variedade sempre-verde

In order to determine the energy needed to artificially dry an agricultural product the latent heat of vaporization of moisture in the product, H, must be known. Generally, the expressions for H reported in the literature are of the form H = h(T)f(M), where h(T) is the latent heat of vaporization of free water, and f(M) is a function of the equilibrium moisture content, M, which is a simplification. In this article, a more general expression for the latent heat of vaporization, namely H = g(M,T), is used to determine H for cowpea, always-green variety. For this purpose, a computer program was developed which automatically fits about 500 functions, with one or two independent variables, imbedded in its library to experimental data. The program uses nonlinear regression, and classifies the best functions according to the least reduced chi-squared. A set of executed statistical tests shows that the generalized expression for H used in this work produces better results of H for cowpea than other equations found in literature.