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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/516

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Autor: search.filters.author.Bona, EvandroTem arquivos: Sim

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    Use of experimental design and simplex optimization algorithm in the encapsulation of roasted coffee oil
    (Embrapa Café, 2015) Freiberger, Eliza Brito; Kaufmann, Karine Cristine; Bona, Evandro; Araújo, Pedro Henrique Hermes de; Sayer, Claudia; Leimann, Fernanda Vitória; Gonçalves, Odinei Hess
    Nanoencapsulation is a promising approach to protect the volatile compounds in natural lipid mixtures like roasted coffee oil . In this work, n anocapsules were obtained by the miniemulsification - solvent evaporation technique using poly (L - lactic acid) (PLLA) and poly( hydroxybutyrate - co - hydroxyvalerate) (PHBV) as encapsulant polymers. The total amount of effectively encapsulated oil was evaluated using a combination of full factorial experimental design and the simplex optimization algorithm and the following independen t factors were evaluated: encapsulant polymer , dispersion mechanism, polymer:oil mass ratio and surfactant. The total oil content (oil recovery) was significantly influenced (p < 0.05) by two - way and three - way interactions confirming that a complex dependence between the factors took place. If PLLA was the encapsulant polymer , then sonication yield ed the highest oil recovery. For PHBV as encapsulant, high shear homogenization (Ultraturrax) led to the highest oil recovery. In both cases, polymer: oil ratio and surfactant must be adjusted accordingly.
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    Arabica coffee classification using near infrared spectroscopy and two-stage models
    (Embrapa Café, 2015) Marquetti, Izabele; Link, Jade Varaschim; Lemes, André Luis Guimarães; Scholz, Maria Brígida dos Santos; Valderrama, Patrícia; Bona, Evandro
    Coffee quality depends on the environment al conditions of the growing area. Factors such as climate, soil type and altitude, associated with agricultural practices, directly influence the chemical composition of the coffee beans. This study developed two - stage models to determine the geographic and genotypic origin of the grain. For the first stage, the partial least squares with discriminant analysis (PLS - DA) and principal component analysis (PCA) models were tested. Then, two artificial neural network (ANN) non - linear models, i.e. multilayer perceptron (MLP) and the radial - basis function (RBF), were evaluated as the second stage. Samples from four genotypes, cultivated in four different cities within Parana State in Brazil, were analyzed using near infrared spectroscopy (NIRS) in the 1100 to 2498 nm range. Three preprocessing techniques were tested on the spectra, i.e. multiplicative scatter correction (MSC); the Savitzky - Golay second - derivative and both combined. The best models were obtained with the spectra treated using MSC plus the second - derivative, with PLS - DA as first stage followed by the RBF network. For geographic and genotypic classification the sensitivity and specificity values of 100% were obtained for the training and test sets. The NIRS spectra presented better class separation when compared with the FTIR spectra used in a previous work. These results demonstrate that NIRS spectra, allied with the right pattern recognition techniques, can be used as a quick and efficient technique to distinguish green coffee samples both geographically and genotypically.