Determination of copper at wide range concentrations using instrumental features of high-resolution continuum source flame atomic absorption spectrometry

This work describes a method to determine Cu at wide range concentrations in a single run without need of further dilutions employing high-resolution continuum source flame atomic absorption spectrometry. Different atomic lines for Cu at 324.754 nm, 327.396 nm, 222.570 nm, 249.215 nm and 224.426 nm were evaluated and main figures of merit established. Absorbance measurements at 324.754 nm, 249.215 nm and 224.426 nm allows the determination of Cu in the 0.07 - 5.0 mg L-1, 5.0 - 100 mg L-1 and 100 - 800 mg L-1 concentration intervals respectively with linear correlation coefficients better than 0.998. Limits of detection were 21 µg L-1, 310 µg L-1 and 1400 µg L-1 for 324.754 nm, 249.215 nm and 224.426 nm, respectively and relative standard deviations (n = 12) were £ 2.7%. The proposed method was applied to water samples spiked with Cu and the results were in agreement at a 95% of confidence level (paired t-test) with those obtained by line-source flame atomic absorption spectrometry.

Análise de consistência de iogurte: correlação entre medida sensorial e instrumental

O efeito das variáveis independentes: soro desmineralizado em pó (0,0; 1,5 e 3,0 %), cultura láctica (1,0; 2,0 e 3,0 %) e temperatura de tratamento da mistura (85; 90 e 95°C) foi estudado nas respostas consistência sensorial e instrumental do iogurte. A metodologia de superfície de resposta foi usada para efetuar tratamentos estatísticos dos experimentos e desenvolver modelos matemáticos da relação entre as variáveis e as respostas. A consistência instrumental e sensorial apresentaram resultados semelhantes e correlacionáveis entre si, r = 0,65 (p < 0,01), ambas foram maiores quando aumentou-se a temperatura da mistura e diminuiu-se o teor de soro desmineralizado em pó.

Desenvolvimento do perfil sensorial e avaliação sensorial/instrumental de suco de maracujá

As mais importantes frutas tropicais na categoria de sucos são maracujá e manga, especialmente desejadas por sua impressão aromática e intensa. O aroma e o sabor são resultados da presença de numerosos constituintes que se encontram em concentrações variáveis, extremamente sensíveis às mudanças durante o tratamento térmico na produção de suco. Há a necessidade do desenvolvimento dos perfis sensorial e de cor, que podem ser alterados durante o processamento do suco. O objetivo deste trabalho foi desenvolver esses perfis e avaliar os sucos de maracujá entamborado: despolpado, pasteurizado, entamborado e congelado); pronto para beber (formulado com açúcar e água); e pasteurizado. Treze atributos compuseram o perfil sensorial. O experimento de Análise Descritiva Quantitativa foi delineado com 2 fatores, sucos e provadores. Os resultados foram também submetidos a uma análise de componentes principais. Houve diferença significativa entre os três sucos nos atributos sensoriais, exceto para aromas de maracujá e doce. O aroma artificial, e os aromas e os sabores de cozido e fermentado aumentaram significativamente no suco processado. Os dois primeiros componentes principais concentraram 57% da variação nos atributos sensoriais. Os parâmetros de cor, à exceção do turbidez, apresentaram variações significativas entre os sucos. O suco entamborado foi o mais escuro. A intensidade de vermelho nos sucos entamborado e formulado foi similar, menos acentuada no pasteurizado, enquanto a intensidade de amarelo foi mais acentuada no formulado, decrescendo nos pasteurizado e entamborado.

Textura instrumental de queijo petit-suisse potencialmente probiótico: influência de diferentes combinações de gomas

O efeito de diferentes combinações de gomas sobre os parâmetros de textura instrumental de queijo tipo petit-suisse probiótico foi avaliado. Queijos petit-suisse foram produzidos utilizando-se massa-base de queijo quark preparada com o starter Streptococcus thermophilus e os probióticos Lactobacillus acidophilus e Bifidobacterium longum. Três formulações de queijo petit-suisse foram preparadas, a partir de massa-base de queijo quark adicionada de 0,75% do produto final da mistura de gomas - xantana (X), carragena (C), guar (G), pectina (P): F1 = 2,5X:2,5C:5G; F2 = 2X:3C:5P; F3 = 5C:5G. Os parâmetros avaliados após 1, 7, 14 e 21 dias de armazenamento do produto a 4±1ºC incluíram a contagem de probióticos, a textura instrumental, o pH e a umidade. As populações de probióticos mantiveram-se acima de 6,40 log UFC/g para L. acidophilus e de 7,30 log UFC/g para B. longum. Houve diferença significativa entre as formulações (p<0,05) para todos os parâmetros de textura, exceto a firmeza de F1. O pH e a umidade apresentaram comportamento semelhante para as três formulações. Dentre as formulações estudadas, F1 foi considerada a melhor, por sua firmeza mais estável durante o período de armazenamento.

Instrumental color and sensory acceptance of soy-based emulsions: a response surface approach

Response Surface Methodology (RSM) was applied to evaluate the chromatic features and sensory acceptance of emulsions that combine Soy Protein (SP) and red Guava Juice (GJ). The parameters analyzed were: instrumental color based on the coordinates a* (redness), b* (yellowness), L* (lightness), C* (chromaticity), h* (hue angle), visual color, acceptance, and appearance. The analyses of the results showed that GJ was responsible for the high measured values of red color, hue angle, chromaticity, acceptance, and visual color, whereas SP was the variable that increased the yellowness intensity of the assays. The redness (R²adj = 74.86%, p < 0.01) and hue angle (R²adj = 80.96%, p < 0.01) were related to the independent variables by linear models, while the sensory data (color and acceptance) could not be modeled due to a high variability. The models of yellowness, lightness, and chromaticity did not present lack of fit but presented adjusted determination coefficients bellow 70%. Notwithstanding, the linear correlations between sensory and instrumental data were not significant (p > 0.05) and low Pearson coefficients were obtained. The results showed that RSM is a useful tool to develop soy-based emulsions and model some chromatic features of guava-based emulsions through RSM.

Effect of whey protein concentrate on texture of fat-free desserts: sensory and instrumental measurements

It is important to understand how changes in the product formulation can modify its characteristics. Thus, the objective of this study was to investigate the effect of whey protein concentrate (WPC) on the texture of fat-free dairy desserts. The correlation between instrumental and sensory measurements was also investigated. Four formulations were prepared with different WPC concentrations (0, 1.5, 3.0, and 4.5 wt. (%)) and were evaluated using the texture profile analysis (TPA) and rheology. Thickness was evaluated by nine trained panelists. Formulations containing WPC showed higher firmness, elasticity, chewiness, and gumminess and clearly differed from the control as indicated by principal component analysis (PCA). Flow behavior was characterized as time-dependent and pseudoplastic. Formulation with 4.5% WPC at 10 °C showed the highest thixotropic behavior. Experimental data were fitted to Herschel-Bulkley model. The addition of WPC contributed to the texture of the fat-free dairy dessert. The yield stress, apparent viscosity, and perceived thickness in the dairy desserts increased with WPC concentration. The presence of WPC promotes the formation of a stronger gel structure as a result of protein-protein interactions. The correlation between instrumental parameters and thickness provided practical results for food industries.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.