Steady shear (η) and complex viscosities (η*) are important rheological properties of honeys. In this study, the effects of temperature and total soluble solids (TSS) concentration on these properties in 40 different Brazilian honeys were investigated. All honeys showed liquid-like behaviour at the temperatures and mechanical spectra tested and, except for the orange blossom and multisoutheast samples, respected the Cox-Merz rule. It was found that η varied between 147.3 Pa s and 0.35 Pa s, and η* between 151.33 Pa s and 0.42 Pa s, at 10°C and 60°C, respectively. Four experimental models (Arrhenius, Williams-Landel-Ferry (WLF), Vogel-Tamman-Fulcher (VTF), and power law) were used to evaluate the effect of temperature on η and η*. The Arrhenius model was the most appropriate for estimation of η of all honeys and η* of some. WLF was the most appropriate for predicting η* of the orange blossom, multisoutheast, and multi-southern honeys. Increase in temperature and decrease in TSS concentration lowered these values. The effect of TSS concentration on η and η* at different temperatures could be appropriately described by the power law and exponential models. Simplified models were proposed to determine the η and η* from the combined effect of both factors, which presented R 2 of 0.9540 and 0.9334, and RMSE of 8.00 and 10.44, respectively. Thus, all models obtained in this study provide important tools towards the proper industrialisation of honeys and honey-based products.

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The rheological and thermal properties of honeys have a practical importance for beekeepers and industries, as their determination allows their proper processing and quality control. However, few studies have been carried out in this area with Brazilian honeys. Therefore, physicochemical, rheological and thermal properties of 40 Brazilian honeys of different floral origins were determined in this study. All the physicochemical properties indicated that the analyzed honey samples met the criteria for honeys established by Codex Alimentarius. The honeys showed non‐Newtonian behavior from 10C to 15C and Newtonian behavior from 20C to 30C with the influence of temperature on viscosity and on the viscous modulus (G″) described by the Arrhenius models. Oscillatory rheological data revealed a liquid‐like behavior (G″> elastic modulus (G′)) of the honeys. The glass transition temperature (Tg) of the honeys varied between −46.7C and −43.7C. By the principal component analysis (PCA), the first component explained 70.0% of the variation of the data, forming two groups described by moisture content, aw, viscosity (10C to 30C), G″ and Tg. This analysis confirms the important influence of the moisture content and of aw in the rheological and thermal properties of honeys and provide useful information for the industrialization of honey and honey‐based products. Practical Application Determining the honey rheological and thermal properties allows its proper processing and quality control, preventing the waste of economic resources. Conclusions for beekeepers and industries are: the honey physicochemical properties are in conformity with the Brazilian legislation and the Codex Alimentarius, which could be used by consumers and the food industry; rheological and thermal properties are inversely correlated with the moisture content and with aw, and could be used to distinguish Brazilian honeys; Brazilian honeys behave as diluted materials, and their viscosity is temperature‐ and floral origin‐dependent. Determining their viscosity helps in the performance, sizing and selection of equipment such as pumps, mixers, filters, centrifuges and heat exchangers; temperature fluctuations induce physical and chemical alterations in the honey, and temperature control during its processing and storage is important for maintaining its quality. All of these parameters provide useful information for the development of new products, optimization of industrial processes and control of quality and authenticity of honeys.

The relationships between physico-chemical and rheological properties are considered complex nonlinear systems. Thus, the artificial neural network (ANN) and regression models were used for the rheological characterization of Brazilian honeys, based on low-cost measurements of water content and temperature. The steady shear viscosity (η) performed well when measured in the test phase in a 2-12-1 neuron multilayer perceptron (MLP) ANN (model 1) with a root mean square error (RMSE) and correlation coefficient (r) equal to 0.0430 and 0.9681, respectively. The parameter loss modulus (G″), storage modulus (G′) and complex viscosity (η*) were predicted in the temperature sweep test by small amplitude oscillatory shear (SAOS) measurements during heating and cooling, and the MLP ANNs with architectures of 2-9-3 (model 2) and 2-3-3 (model 3) showed RMSE values equal to 0.0261 and 0.0387 in the test phase, respectively. For all the determined parameters, non-linear exponential models showed similar results to models 1, 2 and 3. An ANN with 3-9-3 architecture (model 4) showed RMSE and r for G′ equal to 0.0158 and 0.7301, for G″ equal to 0.0176 and 0.9581, and for η* equal to 0.0407 and 0.9647, respectively, in the test phase for date of the frequency sweep test obtained by SAOS. These results were far superior to those obtained by second-order multiple linear models. The acquisition of all models is an important application for the processing of honey and honey-based products, since these properties are essential in engineering calculations and quality control of products.

Purpose The purpose of this paper is to evaluate the classification ability of pork quality by cluster analysis in relation to reference criteria proposed in the literature. Verify if clusters were theoretically significant with major pork quality categories. Verify if classificatory parameter values of quality attributes determined “a posteriori” may be used for following categorization. Design/methodology/approach In total, 60 pork loins were classified into pale, soft and exudative, reddish-pink, soft and exudative, RFN and dark, firm and dry by reference criteria and hierarchical cluster analyses were performed to identify groups of samples with different attributes, based on only pH45min and on pHu, L* and drip loss. Findings Cluster analysis divided total samples into different (p<0.05) smaller groups. Two groups were formed based on only pH45min and five groups were formed based on pHu, L* and drip loss. By these five groups, L* of 44 and 52 distinguished between dark, reddish-pink and pale meat colors and drip loss of 2 and 6 percent distinguished between dry, non-exudative and exudative meats. Cluster analyses identify pork groups with different attributes and the proposed parameters can be used to distinguish between groups theoretically similar to major pork quality categories. Originality/value To decide the best destination to pork carcass and to reduce economic losses, the correctly classify of the pork quality is decisive. This study proves that cluster analysis is able to classify pork into groups with significantly different quality attributes, which are significant with major pork quality categories, without unclassified samples.

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A jabuticaba possui fibras, vitaminas C e do complexo B, sendo uma fruta rica em sais minerais como, cálcio, fósforo e ferro, com potente ação antioxidante. Estudos de uma técnica de processamento de alimentos denominada estruturação da polpa de frutas tem sido realizados obtendo-se resultados promissores. Essa técnica é uma boa alternativa para garantir as características microbiológicas, nutricionais e sensoriais mais próximas do produto in natura, possibilitando redução dos desperdícios de grandes safras e proporcionando o desenvolvimento de produtos inovadores, convenientes e com alto valor agregado. Neste trabalho foi realizado o estudo de estruturados da polpa concentrada de jabuticaba pelo uso da técnica de shelf-life em diferentes temperaturas (5, 25 e 35 °C), no período de 120 dias. Durante o acompanhamento da vida útil foram realizadas análises físico-químicas (pH e acidez titulável), microbiológicas (coliformes totais, fungos filamentosos e leveduras) e sensoriais (teste de aceitação e intenção de compra) dos estruturados, nos dias 0, 15, 40, 60, 80, 100 e 120, a fim de identificar as características que mais afetaram a qualidade dos produtos. As análises de pH e acidez dos estruturados durante os 120 dias apresentaram-se com médias iguais a (3,86 ± 0,07) e (1,02 ± 0,03) g ácido cítrico/100 g de amostra, respectivamente. Nas temperaturas de 5 e 25 °C os estruturados mantiveram-se adequados para o consumo durante os 120 dias. Os dados cinéticos, Ea (14,33 kcal/mol) e Q10 (1,86), estabelecidos no teste acelerado, permitiram estimar o limite da vida útil em 107 dias para o produto armazenado a 25 °C e 405 dias para o produto armazenado a 5 °C, com a formulação contendo glicose (F1). Entretanto, na temperatura de 35 °C, os estruturados foram considerados impróprios para o consumo, acima de 83 dias, devido a alterações nas características sensoriais, principalmente no sabor. Dessa forma, a elaboração de estruturados de frutas possibilitou a obtenção de um produto com maior vida útil, praticidade e características sensoriais próximas da fruta in natura, podendo ser utilizados na produção de caldas, sorvetes, confeitarias, geleias e iogurtes bicamadas.

This research was performed to ascertain the most suitable Artificial Neural Network (ANN) model to quantify the degree of fraud in powdered milk through the addition of powdered whey via regular standard physicochemical analyses. In this study, an evaluation was done on 103 samples with different quantities of added whey powder to whole milk powder. Using Fourier Transform Infrared Spectroscopy the fat, cryoscopy, total solids, defatted dry extract, lactose, protein and casein were analyzed. The hyperbolic tangent transformation function was used with 45 topologies, and the Holdback and K-fold validation methods were tested. In the Holdback method, 75% of the database was employed for training, while 25% was used for validation. In the K-fold method, the database was categorized into five equal sized subsets, which alternated between training and validation. Of the two methods, the K-fold method was proven to have superior efficiency. Next, analysis was done on three models of multilayer perceptron networks with feedforward architecture. In Model 1, the input layer contained all the physicochemical analyses conducted, in model 2 the casein analysis was excluded, and in model 3 the routine analyses performed for dairy products was done (fat, defatted dry extract, cryoscopy and total solids). From Model 3 an ANN was derived which could satisfactorily predict fraud calculated from using the routine and standard analyses for dairy products, containing 64 nodes in the hidden layer, with R2 of 0.9935 and RMSE of 0.5779 for training, and R2 of 0.9964 and RMSE of 0.4358 for validation.