Classificação de água de coco processada e natural por meio de HCA, PCA e teores de íons metálicos determinados por ICP OES

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

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.