High oat content may be efficiently processed by optimizing the extruder conditions (temperature, screw speed, and profile), improving the nutritional properties of the final product. © 2017 Society of Chemical Industry.

In this study, cellulose nanocrystals with cellulose crystal structure I (CNCs I) and with coexisting cellulose crystalline structures I and II (CNCs I-II) were isolated from pine cellulose using acid hydrolysis with H 2 SO 4 . It was possible to obtain CNCs with different morphologies, crystallinities and crystalline structures adjusting only the reaction conditions. The thermal stability is directly related to the crystalline structure and the crystallinity. It was observed that CNCs composed mainly of CNC II have an initial degradation temperature higher than the CNCs I when comparing samples with similar crystallinity. The kinetic results allow us to conclude that activation energy (E a ) not only depends on the crystalline structure and crystallinity of the CNCs but may also be related to the presence of sulfate groups. Understanding the influence of crystallinity and crystalline structure on the thermal stability of CNCs can direct the studies of future applications for these materials.

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Food texture is one of the most widely measured quality attributes during processing and consumption, being measured by instrumental and sensory means. The aims of this study were to measure the textural parameters of the crumb of 14 whole-wheat bread loaves made with whole-wheat flour and fat replacer using instrumental methods and a sensory trained panel, and to determine the relationship between instrumental and sensory assessments. The data of instrumental and sensory texture were individually subjected to analysis of variance and correlated using principal component analysis. The analyses showed that for both (instrumental and sensory texture), the less firm, more elastic and more cohesive bread loaves have <60% whole-wheat flour, regardless of the content of fat replacer. The hardness attribute, measured with a texturometer, was consistent with the results of other published works and with our sensory evaluation as the matrix showed correlation coefficients with high values.

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The effects of β-glucans (βG) in beef emulsions with carrageenan and starch were evaluated using an optimal mixture modeling system. The best mathematical models to describe the cooking loss, color, and textural profile analysis (TPA) were selected and optimized. The cubic models were better to describe the cooking loss, color, and TPA parameters, with the exception of springiness. Emulsions with greater levels of βG and starch had less cooking loss (<1%), intermediate L* (>54 and <62), and greater hardness, cohesiveness and springiness values. Subsequently, during the optimization phase, the use of carrageenan was eliminated. The optimized emulsion contained 3.13 ± 0.11% βG, which could cover the intake daily of βG recommendations. However, the hardness of the optimized emulsion was greater (60,224 ± 1025 N) than expected. The optimized emulsion had a homogeneous structure and normal thermal behavior by DSC and allowed for the manufacture of products with high amounts of βG and desired functional attributes.

Maize drying is an important process, especially for storage and conservation. For this study, the experimental stage was carried out using a forced convection dryer with air heated at different temperature conditions (306.05–441.85 K) and flow (0.13–0.256 m3/hr), totalizing 15 drying curves. Then the performances of the classic drying kinetics methodology and the approach proposed in this paper, in which the increase in moisture content of the product with time was represented combining exponential models and neural networks based on wavelets, were compared. Good performance was obtained in predictions using the proposed approach. One of the main differentials of the methodology adopted was the obtainment of a model that has a global predictive capacity, within the range of tested operating conditions, which can be used in predicting drying curves for different operating conditions. Practical applications The drying process is also one of the most widely used methods for preserving food, and has the advantage of reducing the costs of storage and transport because of the low volume and weight of the end product. During the last years, this topic has attracted a broad industrial interest, resulting in many research studies investigating the drying process. Usually, with regard to the classic approach for modeling of the drying process, the kinetics of drying curves obtained in different operating conditions is affected separately, that is, the parameters are estimated independently, resulting in different regression problems. With the classical approach, in general, it is not possible to obtain a comprehensive prediction model with regards to operating conditions. We have proposed an alternative modeling method. Aiming to obtain a modeling tool with an overall predictive ability, an approach for drying kinetics prediction that combines exponential models and neural networks was proposed. The proposed modeling method was able to predict drying curves for different operating conditions.