Gluten contamination in manufactured gluten-free foods in Turkey

ABSTRACT

Gluten contamination in manufactured gluten-free foods (mGFFs) is a major health, well-being and economic issue worldwide for both mandatory and voluntary GFF-consumers. Although scarce, a number of surveys have shown that up to 21.5 % of mGFFs in circulation in the market are contaminated with gluten. However, at the present time there is no published work reporting gluten contamination in mGFFs produced in Turkey. In this paper miscellaneous mGFFs produced in Turkey were analysed for gluten concentration (G) to fill this knowledge gap, and to compare the situation in Turkey with worldwide efforts on this issue. A total of 200 mGFFs from 8 product categories (snack, pasta, bread, cookie, cracker, farina, traditional and others), and manufactured using 7 main ingredients (cereal mixture, buckwheat, corn, rice, locust bean, potato, and others) were analysed. A significant portion of mGFFs (17.5 %) were contaminated with gluten and therefore unacceptable as being GFF. The results point to buckwheat as the main cause of this contamination. If buckwheat is excluded, the ratio of unacceptable mGFFs dramatically decreases to 6.3 % and probably to 1.8 %, which are comparable figures to those reported for other countries. Almost all countries are subjected to the same regulations on GFFs, and the problem of gluten contamination could readily be solved to a great extent if pre-market measures are mandated. Enforcing mGFF-producers to screen their raw materials and final products to detect the presence of gluten, and preventing the release of contaminated mGFFs into the market would be a practical measure in favour of all stakeholders involved in GFF-consumption.     

Influence of fat content and emulsifier type on the rheological properties of cake batter

The effects of fat content and emulsifier type on the rheological properties of cake batter have been investigated by using a parallel-plate rheometer. The apparent viscosity of cake batter with five different fat concentrations (0, 12.5, 25, 37.5, and 50%) and two types of emulsifier, namely Purawave and Lecigran, was studied as a function of the shear rate. In addition, the time dependency of different cake formulations was investigated. It was found that cake batter with different fat concentrations and emulsifier types exhibited shear thinning and time-independent behavior. Experimental data provided a good fit for the power law model. The increase in fat content and addition of emulsifier caused a decrease in the apparent viscosity. The flow behavior index was not found to be dependent on the composition of cake batter.     

Effectiveness of chitosan‐based coating in improving shelf‐life of eggs

Despite eggs having a natural packaging—shell—they are perishable and can lose their quality during storage. Chitosan‐based coatings were applied to shell eggs to examine potential effects on egg quality properties (weight loss, Haugh unit, yolk index) during 4 weeks of storage. Mineral amounts in yolks were also evaluated after 4 weeks of storage. Three chitosan‐based coatings produced with organic acids (acetic‐(C‐AA), lactic‐(C‐LA), and propionic (C‐PA)) were evaluated on shelf‐life enhancements of fresh egg quality. All chitosan‐coated eggs showed greater interior quality than the non‐coated eggs. The coatings significantly maintained weight loss compared to the control specimen (4.96%). Lower weight loss (3.45% for C‐PA, 3.53% for C‐LA) was observed in the coated eggs. Eggshell chitosan coat containing lactic and propionic acids maintained higher Haugh unit and yolk index than eggs coated with acetic acid. Uncoated (UC) eggs changed from grade ‘A’ to ‘B’ after 1 week of storage. Chitosan‐based coating containing lactic and propionic acids maintained eggs in grade ‘A’ for 4 weeks. Haugh unit showed that C‐LA and C‐PA effectively maintained eggs at grade ‘A’ quality for at least 3 weeks more than control and 1 week more than C‐AA. Results also indicated that the chitosan coating maintained mineral amounts at nutritional values (especially calcium, iron and magnesium concentration) in yolks after 4 weeks storage. Copyright © 2006 Society of Chemical Industry     

Polyurethaneurea–silica nanocomposites: Preparation and investigation of the structure–property behavior

Nanocomposites consisting of thermoplastic polyurethane–urea (TPU) and silica nanoparticles of various size and filler loadings were prepared by solution blending and extensively characterized by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), thermal analysis, tensile tests, and nanoindentation. TPU copolymer was based on a cycloaliphatic diisocyanate and poly(tetramethylene oxide) (PTMO-2000) soft segments and had urea hard segment content of 20% by weight. TPU/silica nanocomposites using silica particles of different size (29, 74 and 215 nm) and at different loadings (1, 5, 10, 20 and 40 wt. %) were prepared and characterized. Solution blending using isopropyl alcohol resulted in even distribution of silica nanoparticles in the polyurethane–urea matrix. FTIR spectroscopy indicated strong interactions between silica particles and polyether segments. Incorporation of silica nanoparticles of smaller size led to higher modulus and tensile strength of the nanocomposites, and elastomeric properties were retained. Increased filler content of up to about 20 wt. % resulted in materials with higher elastic moduli and tensile strength while the glass transition temperature remained the same. The fracture toughness increased relative to neat TPU regardless of the silica particle size. Improvements in tensile properties of the nanocomposites, particularly at intermediate silica loading levels and smaller particle size, are attributed to the interactions between the surface of silica nanoparticles and ether linkages of the polyether segments of the copolymers.     

rGO/CuO/PEDOT nanocomposite formation,its characterisation and electrochemical performances for supercapacitors

ABSTRACT

Supercapacitor properties of rGO, CuO, PEDOT and rGO/CuO at [rGO]_o/[CuO]_o?=?1:1; 1:1.5; 1:2 and rGO/CuO/PEDOT nanocomposite at [rGO]_o/[CuO]_o/[EDOT]_o?=?1:1:1; 1:1:3; 1:1:5 were investigated using chemical reduction of GO and in-situ polymerisation process. SEM-EDX, HRTEM, BET surface area analysis confirm the nanocomposite formations. Nanocomposite materials are also analysed through FTIR-ATR, Raman, TGA-DTA, GCD, CV and EIS. The highest specific capacitance of C _sp?=?156.7 F/g at 2?mV/s is determined as rGO/CuO/PEDOT at [rGO]_o/[CuO]_o/[EDOT]_o?=?1:1:5. In addition, two-electrode supercapacitor device for rGO/CuO/PEDOT at [rGO]_o/[CuO]_o/[EDOT]_o?=?1:1:5 are found to provide a maximum specific energy (E?=?14.15 Wh/kg at 20?mA) and specific power (P?=?24730 W/kg at 50?mA), electrical serial resistance (ESR?=?13.33 Ω) with good capacity retention after 3000 cycles. An equivalent circuit model of LR1(CR2)(QR3) is proposed to interpret the EIS data. The supercapacitor performance of the rGO/CuO/PEDOT nanocomposite electrode indicates the synergistic effect of hybrid supercapacitors.     

Multiplicative neuron model artificial neural network based on Gaussian activation function

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.     

A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting

Neural Processing Letters - Financial time series forecasting has been becoming one of the most attractive topics in so many aspects owing to its broad implementation areas and substantial impact....     

Biosorption characteristics of Bacillus sp. ATS-2 immobilized in silica gel for removal of Pb(II)

The bacterial strain Bacillus sp. ATS-2 isolated from Pb(II) polluted soil was immobilized with a silica matrix and Pb(II) biosorption properties of immobilized biosorbent were examined. Optimum biosorption conditions were investigated in the fixed bed column with the variation in the parameters of pH, bed length, flow rate and influent concentration. The Pb(II) biosorption equilibrium was attained within 60 min and the maximum biosorption yield for silica gel immobilized Bacillus sp. ATS-2 was determined as 91.73% at pH 4.0. The higher biosorption yields were observed at flow rates of 60 and 180 ml h(-1). The optimum bed length for the column was found as 10 cm. Data obtained from batch studies were evaluated by Freundlich, Langmuir and Dubinin-Radushkevich (D-R) isotherm models. The maximum monolayer capacity of Bacillus sp. ATS-2 for Pb(II) was 2.36 x 10(-5) mol g(-1). The involvement of the functional groups on the surface of immobilized cells in biosorption process was also evaluated by FTIR spectral analysis.     

Estimation of Dielectric Properties of Cakes Based on Porosity, Moisture Content, and Formulations Using Statistical Methods and Artificial Neural Networks

Dielectric constant (DC) and dielectric loss factor (DLF) are the two principal parameters that determine the coupling and distribution of electromagnetic energy during radiofrequency and microwave processing. In this study, chemometric methods [classical least square (CLS), principle component regression (PCR), partial least square (PLS), and artificial neural networks (ANN)] were investigated for estimation of DC and DLF values of cakes by using porosity, moisture content and main formulation components, fat content, emulsifier type (Purawave™, Lecigran™), and fat replacer type (maltodextrin, Simplesse). Chemometric methods were calibrated firstly using training data set, and then they were tested using test data set to determine estimation capability of the method. Although statistical methods (CLS, PCR and PLS) were not successful for estimation of DC and DLF values, ANN estimated the dielectric properties accurately (R ², 0.940 for DC and 0.953 for DLF). The variation of DC and DLF of the cakes when the porosity value, moisture content, and formulation components were changed were also visualized using the data predicted by trained network.