Modification of Food-Contacting Surfaces by Plasma Polymerization Technique: Reducing the Biofouling of Microorganisms on Stainless Steel Surface

In this study, the prevention of the attachment of test microorganism Enterobacter sakazakii onto stainless steel (SS 316) surfaces by radio frequency (RF) plasma polymerization (PlzP) technique using several hydrophilic monomers as precursors was reported. Different plasma conditions (RF discharge power of 20–80 W with exposure time of 10 min) were employed during the modifications. PlzP-modified surfaces were characterized in detail by static contact angle measurements in order to state the change of surface hydrophilicity. The surface topology of unmodified and PlzP [ethylenediamine (EDA)]-modified SS 316 plates was characterized by atomic force microscopy. The attachment of the model microorganism on the SS 316 surface modified by plasma using EDA at 45 W and 10 min was reduced by 99.74% in comparison to the unmodified control surface. For equilibrium adsorption behavior, Freundlich and Langmuir models were attempted and model parameters for Freundlich (K _F and 1/n) and for Langmuir (a and b) were obtained. The values of the K _F and 1/n were 5.6 and 0.58 and 0.9 and 0.39, respectively; the values of a and b were 25 × 10⁴ and 1.82 × 10⁻⁸ and 0.3 × 10⁴ and 7.96 × 10^-8, for bare and PlzP-EDA-modified SS 316 surfaces, respectively. As a result, PlzP technique was found to be an alternative simple method to decrease the microbial attachment and create bacterial anti-fouling surfaces.     

Physiological changes of Escherichia coli O157:H7 and Staphylococcus aureus following exposure to high hydrostatic pressure

Morphological changes and membrane integrity of Escherichia coli O157:H7 and Staphylococcus aureus cells before and after high hydrostatic pressure (HHP) treatments (200–400 MPa) and time (1–5 min), at a constant temperature (40 °C), in peptone water were examined by using scanning electron microscopy (SEM) and fluorescent microscopy, respectively. SEM images showed that unpressurized cells exhibited a smooth surface appearance. E. coli O157:H7 cells exposed to pressure treatments first appeared larger, then with increasing pressure distorted with dimples and pinches. In case of S. aureus, the cells pressurized at low pressure levels did not show any significant change. The surface appearance became rough and cracked when the cells were exposed to higher pressure levels. Images of fluorescent microscopy showed that a small proportion of bacterial cells were not green fluorescent at lower pressure levels. The other part of the cell population was red fluorescent representing dead cells and the number of red fluorescent cells increased with increasing pressure. The cells with a yellowish color showed that varying levels of membrane damage occurred under HHP. The combinations of mild heat, antimicrobial substances and HHP treatment can be used to inactivate food borne pathogens of varying pressure resistance in the food industry for safe processing conditions. However, the resultant damaged cells at different levels should be taken into account during storage to prevent their recovery.     

Effect of shade and thermo-mechanical viscosity stimulation methods on the rheological properties of nanohybrid resin composite

The aim of the present study is to measure the rheological properties of nanohybrid resin composite of three shades in pre-polymerized phase using different thermomechanical stimulations. Nanohybrid composite (Kerr Herculite XRV Ultra) in enamel, dentin, and incisal shades was included. Rheological measurements were made with a rotational rheometer in dynamic oscillation mode using three methods: (a) Strain Sweep test explored a range of deformation γ₀ from 0.025 to 3% with a frequency ω = 1 Hz (temperature set at 25 and 65?°C), (b) Frequency Sweep test explored frequencies between 1 and 100 rad/s applying a deformation γ₀ = 0.5% (temperature set at 25; 45; 65?°C), and (c) Ramp Temperature test explored a heating phase from 25 to 75?°C then a cooling phase back to 25?°C applying a γ₀ = 0.5% and a ω = 10 rad/s. Data were analyzed using a three-way ANOVA and Tukey’s test (α = 0.05). Viscosity measurement (p < 0.05) and shade of the composites (p < 0.05) significantly affected the results. Viscosity turned out to be subordinate to strain amplitude, frequency, temperature, and axial force applied during each test. Enamel shade was the most viscous whereas dentin shade was 8% less viscous (p < 0.05). The incisal shade was significantly less viscous (70%) than enamel (p < 0.05). Pre-heating decreased viscosity of incisal shade (30%) above 50?°C but this value was 90 and 98%, respectively, for strain and frequency sweep test. Preheating had a side effect as in the cooling phase, viscosity increased from 66 to 450% exceeding the value recorded at the beginning of the test. Preheating was not effective to reduce viscosity, and may reveal some side effects. The composite tested might not be pre-heated above 45?°C.     

Effect of Physicochemical Surface Treatments on the Bond Strength and Adhesion of Porcelain Denture Teeth to Heat-Polymerized Acrylic Resin Denture Base Material

The aim of this study was to compare the effect of physicochemical surface treatments on the adhesive bond strength of porcelain denture teeth (PDT) to acrylic resin denture base material (PMMA). Totally, 100 PDT specimens, 50 with retentive palatal pins (+P) and 50 without pins (?P), were selected and assigned to 10 experimental groups (n = 10). Control groups CON-P and CON + P, did not receive any treatment. Groups SB + P and SB-P were sandblasted, groups SB/AE + P and SB/AE-P were sandblasted and acid-etched, groups TSC + P and TSC-P were tribochemically silica-coated, and groups FB + P and FB-P were covered with fibers. Cylindrical PMMA rods were polymerized onto treated palatal PDT surfaces. Force (N) was applied on palatal incisal edges of PDT specimens until debonding of PMMA. Obtained data were statistically analyzed with Kruskal-Wallis and Bonferroni corrected Mann Whitney U tests. The significance level was set at (p < .05). Mean force values of test groups ranged in descending order as follows: TSC+P (132.5 N ± 26.5), SB+P (113.5 N ± 47.5), SB/AE+P (112.2 N ± 26.1), CON+P (103.1 N ± 39.6), TSC-P (90.6 N ± 22.2), FB+P (77.7 N ± 18.3), SB/AE-P (47.6 N ± 10.5), SB-P (18.1 N ± 4.0), CON-P (4.6 N ± 5.4), and FB-P (0.0 N ± 0.0). No significant difference was found between groups with pins (+P) except group FB+P which displayed lower values than CON+P (p < .024), and TSC+P (p < 045). Groups (+P) showed significantly higher bond strength values than groups (?P) except for group TSC-P (p < .09 and p < 1). In groups without pins (P?), differences between groups were significant and ranged as follows: TSC-P>SB/AE-P (p < .0094), SB/AE-P > SB-P (p < .007), and SB-P > CON-P (p < .0013). Groups CON-P and FB-P did not show differences (p ≤ 1). Groups (+P) displayed higher bond strength values than groups (?P). SB-P, SB/AE-P, and TSC-P increased the adhesive bond between PDT and PMMA, respectively. Fiber coating negatively affected the bond.     

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R ²) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.     

K nearest neighbor reinforced expectation maximization method

K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded.