Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants Discriminação de diferentes estados de plantios de café pós-colheita, por meio da técnica de mineração de dados e perfis espectrais

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.<br>Este trabalho teve o objetivo de identificar diferentes condições na cultura do café, após a colheita, utilizando mineração de dados e curvas espectrais obtidas do sensor Hyperion/EO1.A imagem Hyperion, com resolução espacial de 30 m,foi obtida em 28 de agosto de 2008, fim de safra do café na área de estudo.Como pré-processamentos, foram realizadas a correção atmosférica através do algoritmo Flaash e a correção do sinal/ruído pelo algoritmo MNF (Minimum Noise Fraction Transform). Posteriormente, foram geradas curvas espectrais (38) de 150 comprimentos de onda, que foram analisadas através do algoritmo EM (Expectation-Maximization), considerando simulações de 2; 3; 4 e 5 clusters. Para verificar a igualdade das médias dos comprimentos de onda dos clusters, foi utilizado o teste t, com 5% de significância. Os resultados mostraram que foi possível separar os cafés em cinco diferentes grupos, segundo sua condição pós-colheita, possibilitando melhorar ações futuras de intervenção

Optimization of the immobilization process of &#946;-galatosidade by combined entrapment-cross-linking and the kinetics of lactose hydrolysis

The immobilization of Aspergillus oryzae &#946;-galactosidase was achieved by entrapment in sodium alginate and gelatin and cross-linking with glutaraldehyde. The optimal concentrations of the aforementioned variables in the immobilization process were determined using an orthogonal central composite design with an orthogonal axial value of 1.35313. The concentrations of alginate, gelatin and glutaraldehyde that provided the greatest enzymatic activity were 6.60%, 4.05% and 3.64% (w/v), respectively. The stability of the immobilized enzyme under the optimal conditions was evaluated through daily activity assays. After 25 uses, a 20% decrease in the enzymatic activity was observed, indicating that the immobilization process could be used to produce a stable biocatalyst. This study investigates the influence of lactose and product concentrations on kinetic reaction hydrolysis. The concentration ranges for the studied variables were 10 to 56 g/L for lactose and 0 to 11.5 g/L for glucose and galactose. Only galactose presented a competitive inhibitory effect