The surface roughness of wood is affected by the processing conditions and the material structure. So, optimization of operation parameters is very crucial to have minimum surface roughness. In this study, modeling and optimization of surface roughness (Ra) of Scotch pine (Pinus sylvestris) was investigated. Firstly, the samples were cut under different conditions 8 mm, 9 mm and 11mm depth of cut and 12 mm, 14 mm and 16 mm axial depth of cut) in computer numerical control (CNC) machine, and then surface roughness (Ra) values of samples were calculated. Then a prediction model of surface roughness was developed using artificial neural networks (ANN). Optimization process was carried out to reach minimum surface roughness of wood samples by the genetic algorithm (GA) method. MAPE value of the ANN model was found lower than 4,0 %. The optimum CNC operation parameters were 1874,5 rad/s, 3,0 m/min feed rate, 9,7 mm depth of cut and 12 mm for axial depth of cut for minimum surface roughness. As a result of study, surface roughness of Scotch pine wood can be modeled and optimized using integrated ANN and GA methods by saving time and cost.

Mushrooms are delicious, nutritious and consumed foods known as dietary and protein sources. Along with the rapid growth of the world population, the increasing tendency of people to alternate medicine has increased the consumption of mushrooms of which useful/medical features are revealed by scientific studies. As in every consumption behavior, consumption of mushrooms is also a result of preferences. The purpose of this study is to determine mushroom consumption preferences using the fuzzy analytical hierarchy process (AHP). First of all, it was created the analytic hierarchy process, which has the choice of mushrooms and (if any) sub-criteria. The generated AHP was converted into comparative matrices and replied to the experts. Received answers are transformed into fuzzy numbers and the importance levels of preferences are ranked according to their calculated weights.

Increasing environmental pressures on toxic chemical wood preservatives lead to the development of natural and environmentally friendly wood preservatives. In this study, using possibilities of lichen (Usnea filipendula) and leaves of mistletoe (Viscum album) as potential natural wood preservative were researched. Impregnation procedure was applied at four different concentration levels and with two different extraction methods (hot water and methanol). The concentration levels were arranged as 3%, 5%, 10%, 15% for hot water and as 3,75%; 6,25%; 12,5%; 18,75% for methanol. The treatment procedure has been applied according to the ASTM D-1413 (1988) standard test method. The fungal decay test has been done according to the EN 113 (1996) standard test method using a brown rot fungus, Coniophora puteana for both treated test and untreated control samples. The best results were obtained at the highest concentration level of the solutions. However, the weight losses in treated test specimen have not met the standard requirements. Nevertheless, it can be assumed that both natural extracts provide promising protection performance.

In this study, the possibilities of protecting the color of dried golden and pink mushrooms were investigated, and color parameters of dried mushrooms were modeled by artificial neural network (ANN). For this purpose, first, the golden oyster mushroom (Pleurotus citrinopileatus) and pink oyster mushroom (Pleurotus djamor) were cultivated. Then, pretreatments were applied using citric acid (CA) and potassium metabisulfite (KMS) with different rates (0.5%, 1.0%, and 1.5%) separately, excluding control group mushrooms. All mushrooms were dried for 330 minutes in a laboratory type oven at two different temperatures (40°C and 50°C) until completely dehydrated. Colorimetric values (L*, a*, and b*) were determined using Konica Minolta CM‐2600d spectrophotometer for 30 minute intervals during the drying process. The obtained data were modeled using the ANN technique. The results show that darkening of mushrooms increased as the drying temperature increased. CA and KMS showed better results for dried golden and pink mushrooms, respectively. Thanks to the pretreatment, the mushroom's original color was protected compared with control samples. All mean absolute percentage error values of models were determined, which were lower than 4.0%. It was concluded that ANN can be a good way to predict the color of dried golden and pink mushrooms (pretreated or not) with a high degree of accuracy.

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat-treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110 C, 140 C, 170 C, and 200 C) and durations (15,30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment.All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heattreated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability. K E Y W O R D Sartificial neural network, bamboo, colorimetric parameter, mass loss, multiple linear regressions

In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

No abstract

Many mushroom species have been used by people for different purposes, from past to present. Cultivated mushrooms may show different biological effects depending on the content of the substrate they grown on. The present study aimed to determine the total antioxidant status (TAS), total oxidant status (TOS) and oxidative stress index (OSI) of Pleurotus citrinopileatus Singer mushroom cultivated on five different substrates. The cultivated mushrooms were extracted with ethanol in a Soxhlet device. TAS, TOS and OSI of extracts were determined with Rel Assay kits. The highest TAS (3.125±0.038 mmol/L), TOS (10.786±0.313 µmol/L) and OSI (0.345±0.014) values were determined in the mushrooms grown on 90% beech sawdust+10% bran. The lowest TAS (2.316±0.042), TOS (1.246±0.044) and OSI (0.054±0.001) values were obtained from the mushrooms grown on 100% poplar sawdust.