Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood

One of the biggest challenges in machining processes of wood is to detect the optimum values of process parameters for reducing the final production cost. In the present study, the effects of various process parameters on surface roughness and power consumption in abrasive machining process of wood using experimental data collected from the literature were modeled by artificial neural networks (ANNs). The results have indicated that accurate prediction of the experimental data by neural network models was achieved with the mean absolute percentage error (MAPE) less than 2.51 % for power consumption and 2.65 % for surface roughness in the testing phase. Besides, the values of determination coefficient (R²) were found as 0.994 and 0.985 in the prediction of surface roughness and power consumption by the ANN modeling, respectively. Based on the results, it can be said that by means of the proposed models the surface roughness and power consumption can easily be predicted with very high degrees of accuracy in abrasive machining process of wood. Consequently, the present study can effectively be applied to the wood industry to reduce the time, energy consumption and high experimental costs because it eliminates the need for a large number of experiments.     

Aflatoxins, patulin and ergosterol contents of dried figs in Turkey.

Dried figs of three different categories, palatable, fluorescent, and cull, were investigated for their contents of aflatoxins (B(1), B(2), G(1) and G(2)), patulin, and ergosterol. Samples were obtained from four fig processing plants located in a major fig producing area in the Aegean Region in Turkey. Affinity column clean-up methods were employed for aflatoxins. All aflatoxins, patulin, and ergosterol were determined using high performance liquid chromatography. Palatable figs contaminated with trace amounts of aflatoxins, patulin, and ergosterol, so they posed no risk for the consumer when national and/or international regulatory limits were considered. Fluorescent figs were contaminated with high (117.9-471.9 ppb) aflatoxin levels and cull figs with high patulin (39.3-151.6 ppb) and ergosterol (4.5-18 ppm) levels. The total aflatoxins content was significantly correlated with the patulin content (r(2) = 0.813, p < 0.002) and the ergosterol content (r(2) = 0.920, p < 0.002) only in fluorescent figs. However there was no significant correlation between patulin and ergosterol contents of fluorescent figs. Furthermore, there were no significant correlations between the contents of any two of the three substances in cull figs. This is the first report on the presence of patulin and its co-occurrence with aflatoxin in dried figs.     

K and Ca content of fresh green tea, black tea, and tea residue determined by X-ray fluorescence analysis

Summary X-Ray fluoresence (XRF) can be successfully used for the qualitative and quantitative elemental analysis of various agricultural products. Its simplicity, high throughput and the possibility of automation make it useful for screening large numbers of samples. The K and Ca content of 138 samples of fresh green tea, black tea and black tea residues were determined by applying the XRF system. Such a method of mineral analysis of food products is not very common. Tea from different teagrowing areas of Turkey, green tea of different shooting periods, black tea processed at different tea plants and tea residues from these black tea were analysed. The K content of green tea, processed black tea and tea residues after brewing were found to have ranges of 19,049–26,254 mg/kg, 21,904–26,883 mg/kg and 9,468–13,778 mg/kg, respectively. In the same samples the Ca content was determined as 3,580–4,799 mg/kg, 3,370–4,823 mg/kg, and 3,743–5,733 mg/kg, respectively. These findings were compared with the results of atomic emission techniques and it was concluded that the XRF system could be effectively used for quantitative analysis of the K and Ca content of tea samples.

---

Die Bestimmung des K- und Ca-Gehaltes von frischem Tee, schwarzem Tee und Teerückstand durch die X-Ray-Fluoreszenz-Technik

Zusammenfassung Mit der X-Ray Fluoreszenztechnik können die qualitativen und quantitativen Bestimmungen der Mineralstoffe in verschiedenen Produkten erfolgreich durchgeführt werden. Mit dieser Methode wurde der Ca- und K-Gehalt von 138 Proben untersucht, und zwar frischer Tee, schwarzer Tee und Teerückstand. Diese Methode ist in der Lebensmitteltechnologie, insbesondere bei der Mineralstoffbestimmung nicht üblich. Die Teeproben stammten aus verschiedenen Teeanbaugebieten der Türkei. In dieser Arbeit wurden die Wirkung der Sproßperiode, der Verarbeitungstechnik und des Anbaugebietes auf den Ca- und K-Gehalt der Teeproben untersucht. Der durchschnittliche K-Gehalt betrug: in frischem Tee 19049–26254 mg/kg, in schwarzem Tee 21904–26883 mg/kg, in Teerückstand 9468–13778 mg/kg; der Ca-Gehalt 3580–4799 mg/kg, 3370–4823 mg/kg und 3473–5733 mg/kg.

     

Reduction and characterizations of iron particles: influence of reduction parameters

Iron particles have some applications as electromagnetic devices in magnetic recording and data storage technology due to their small sizes and high data storage capacity. The devices can be advanced by improving the properties of existing materials according to the production parameters. Thus, the influences of reduction parameters on the properties of iron particles were studied. The iron particles were reduced from superparamagnetic iron oxide nanoparticles by altering reduction parameters under hydrogen atmosphere at high (400 °C) temperature. The structural analysis of the films was carried out using the X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM) techniques. The XRD data revealed that the crystal textures changed for the particles reduced at each parameter. And, the crystal structure turns from the cubic spinel structure of magnetite and body centered cubic (bcc) structure of iron to the bcc iron as the reduction time increases from 15 to 240 min. Then, the similar structure change can be seen for the samples reduced at increasing hydrogen flow rates. The HRTEM studies revealed that the surface morphology of the films strongly depend on the flow rate. Finally, magnetite peaks weaken and then disappear as the precursor mass decreases to the lowest value. The average crystallite sizes were found to be consistent with changing crystal structure. Furthermore, the magnetic characteristics studied by a vibrating sample magnetometer were observed to be affected by the parameters. Besides, magnetic differences may arise from the variation of crystal structure and crystal sizes caused by individual reduction parameters of reduction time, hydrogen flow rate and precursor mass.     

An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process

Volatile organic compounds refer to a large class of carbon-based chemicals capable of evaporating easily into the air at room temperature. Formaldehyde is one of the best known volatile organic compounds, and long-term exposure to formaldehyde emission from wood-based building products in indoor air may cause many adverse health effects. This paper presents an implementation of artificial neural networks for modeling the formaldehyde emission from particleboard as a wood-based product based on wood-glue moisture content, density of board and pressing temperature, with the experimental data collected from Petinarakis and Kavvouras (Wood Res 51(1):31–40, 2006). With the constructed model, formaldehyde emission of particleboard could be predicted successfully, and the intermediate formaldehyde emission values not obtained from experimental investigation could be predicted for different combinations of manufacturing parameters. The results proved that the artificial neural network is a promising technique in predicting the formaldehyde emission from particleboard. In this regard, the findings of this study will help the manufacturing industries in obtaining the intermediate values of the formaldehyde emission without performing further experimental activity. The model thus may save time, reduce the consumption of experimental materials and design costs.     

Basic chemical characteristics of fresh, non-packed and vacuum-packed sheep-tail and tail-fat stored frozen for different periods

Fatty tails were obtained after the slaughter of Morkaraman sheep, which are a common breed of fatty-tailed sheep in Turkey. Fat obtained from sheep tails, (called tail-fat) is used either in fresh condition or after frozen storage. This fat is commonly used as a food source in many countries in which fat-tail sheep breeds are raised; but there has been no detailed research on fatty tails or the tail-fat. In this work some quality criteria such as water, fat and nitrogen content, 2-thiobarbituric acid and peroxide numbers, free fatty acids, melting point, iodine and saponification numbers were studied in sheep tails and sheep tail-fat, both in the fresh condition and after storage at -18 ± 1°C for up to 60 days. The effect of vacuum packaging versus storage in netting bags (no-protection, non-packed), was also determined. The fat content of the tails was around 94%. Thus, the tail is a concentrated fat source. Some quality criteria of the tail and the tail-fat were determined and are given in the text. Overall results indicated that both the tail and the tail-fat can be effectively used in various types of food and food processing, especially after performing some technological and refining processing. If the fatty tail has to be stored, it should be vacuum packed and stored frozen. At -18°C, vacuum-packed tail samples could be stored around 45 days, but in the netting bags, this storage period should not exceed 30 days.     

Of critical importance: Toward a quantitative probabilistic risk assessment framework for critical infrastructure

Critical comments can be made to the current risk assessment framework for critical infrastructure used in The Netherlands: the Dutch National Risk Assessment (DNRA). The DNRA uses a qualitative approach to risk assessment, based on, for example, ordinal risk scoring and the risk matrix. Even though qualitative risk assessment methods are internationally popular, there is no scientific evidence that a qualitative approach to risk assessment actually works. In the case of the DNRA, the main points of criticisms relate to the usage of a subjective concept of risk, dependency on subjective risk experts, the use of risk matrices and the absence of decision rules. To combat these criticisms, this article proposes a quantitative probabilistic risk assessment framework for critical infrastructure based upon four design principles, that a methodologically justified risk assessment method for critical infrastructure should meet. The proposal made for the quantitative risk assessment framework uses a Bayesian approach, a standardized measure for negligible risk in the form of a yearly mortality probability of 10⁻⁶, and Disability Adjusted Life Years to quantify human life years for social cost–benefit analysis. Finally, the proposed quantitative method is demonstrated in a case study.