Evaluation of anti-biofilm activity of Lactobacillus rhamnosus GG and Nisin on the expression of aap, ica-A and ica-D as biofilm-associated genes of Staphylococcus epidermidis.

Background and Objectives: In the present study, the anti-biofilm activity of Lactobacillus rhamnosus GG and Nisin was investigated on biofilm-forming abilities of Staphylococcus epidermidis strains and the expression of the biofilm-associated genes. Materials and Methods: In this study, the standard strain of L. rhamnosus GG (ATCC 53103) and Nisin were used to assess their anti-microbial and anti-biofilm effects on S. epidermidis (RP62A). Results: The MIC and MBC analysis showed that Nisin at 256 µg/mL and 512 µg/mL, and L. rhamnosus GG at 1×107 CFU/mL and 1×108 CFU/mL have anti-microbial activity compared to the negative control respectively. L. rhamnosus GG bacteria and Nisin inhibited the biofilm formation of S. epidermidis based on optical density of at 570 nm (P <0.001). The relative mRNA expression of aap, icaA, and icaD genes was significantly reduced compared to the negative control after treating S. epidermidis with sub-MIC of Nisin (0.44, 0.25 and 0.6 fold, respectively) (P>0.05). In addition, the relative expression of aap and icaA genes, but not icaD (P>0.05), was significantly lower than the negative control (0.62 and 0.7 fold, respectively) (P>0.05), after exposure to the sub MIC of L. rhamnosus GG. Conclusion: Nisin and L. rhamnosus GG exhibit potent activity against biofilm-forming abilities of S. epidermidis and these agents could be utilized as an anti-biofilm agents against S. epidermidis infections.

Detection of ripeness grades of berries using an electronic nose.

The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e-nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e-nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10-11-5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.

Optimization on drying conditions of a solar electrohydrodynamic drying system based on desirability concept.

The purpose of this article was to present a new drying method for agricultural products. Electrohydrodynamic (EHD) has been applied for drying of agricultural materials due to several advantages such as energy saving, low cost equipment, low drying temperatures, and superior material quality. To evaluate this method, an EHD dryer based on solar (photovoltaic) energy was designed and fabricated. Moreover, the optimum condition for the EHD drying of kiwi fruit was studied by applying the Box-Behnken design of response surface methodology. The desirability function was applied for optimization in case of single objective and multiobjective functions. By using the multiobjective optimization method, maximum desirability value of 0.865 was obtained based on the following: applied voltage of 15 kV, field strength of 5.2 kV cm(-1), without forced air stream, and finally a combination of 17 discharge electrodes (needles). The results indicated that increasing the applied voltage from 6 to 15 kV, moisture ratio (MR) decreased, though energy efficiency and energy consumption were increasing. On the other hand, field strength of 5.2 kV cm(-1) was the optimal point in terms of MR.

Modeling of electrohydrodynamic drying process using response surface methodology.

Energy consumption index is one of the most important criteria for judging about new, and emerging drying technologies. One of such novel and promising alternative of drying process is called electrohydrodynamic (EHD) drying. In this work, a solar energy was used to maintain required energy of EHD drying process. Moreover, response surface methodology (RSM) was used to build a predictive model in order to investigate the combined effects of independent variables such as applied voltage, field strength, number of discharge electrode (needle), and air velocity on moisture ratio, energy efficiency, and energy consumption as responses of EHD drying process. Three-levels and four-factor Box-Behnken design was employed to evaluate the effects of independent variables on system responses. A stepwise approach was followed to build up a model that can map the entire response surface. The interior relationships between parameters were well defined by RSM.