Recent advances in plant-based compounds for mitigation of mycotoxin contamination in food products: current status,challenges and perspectives

There are numerous strategies to control the growth of mycotoxigenic fungi and reduce mycotoxins in food, including physical, chemical and biological treatments. However, consumers prefer organic food and natural inhibitors because they are biodegradable and safe for human and animal health. This review summarises the current advances in plant-based compounds to mitigate contamination of food products by mycotoxigenic fungi and their toxins. In addition, a clear understanding of the roles of plant-based extracts in food products, their mechanisms of action and challenges and perspectives in mycotoxin degradation are presented. Essential oils and plant-based extracts are complex mixtures of major and minor chemical compounds with antimicrobial and antioxidant properties. In general, the mechanisms of antifungal activity of plant-based compounds are attributed to the reduction of the ergosterol content, disruption of cell membrane integrity, enhancing of membrane ion leakage and permeability, disruption to the organisation of mitochondrial structure, interference in enzymatic reactions of cell wall synthesis, disturbance of oxidative balance, inhibition of carbohydrate metabolism, suppression of mycotoxin biosynthetic genes and alterations in the molecular structure of mycotoxins.     

Experimental validation of a deep neural network—Sparse representation classification ensemble method

In the current study, a new pattern recognition‐based damage detection technique is developed using the frequency response function of the structure. Principal component analysis is employed as an authoritative feature extraction method for dimensional reduction of the measured frequency response function data and constructing distinct feature patterns. Subsequently, as a novel approach, an ensemble of 2 powerful classifiers containing deep neural networks and couple sparse coding classification is utilized for damage prediction of the structure because there is no individual optimal classifier for all the problems. Verification of the proposed method is evaluated by an aluminum beam experimental setup besides a numerical 3D finite element model of a truss bridge. Damage detection results elucidate that the ensemble method decisions are much more accurate compared with the individual classifier decision. The proposed ensemble method verifies to be a novel, robust, and powerful damage detection process.     

Application of adaptive neuro-fuzzy inference system in prediction of hydrate formation temperature

A look at the number of publications in the last decades on the prediction of hydrate forming conditions for various gas mixtures obviously indicates the importance of this field from scientific and industrial viewpoints. Yet, the correlations presented in the literature are not accurate enough and also some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design. In this study adaptive neuro-fuzzy inference systems were used to produce a nonlinear model to predict the hydrate formation temperature. The model was trained using 303 input–output patterns collected from reliable sources. The adaptive neuro-fuzzy inference system model enables the user to accurately predict hydrate formation conditions under varying system conditions (i.e., temperature, pressure, and gas composition), without having to do costly experimental measurements. Also, statistical error analysis is used to evaluate the performance and the accuracy of the adaptive neuro-fuzzy inference system for estimating natural gas hydrate formation to guide designers and operators in selecting the best system conditions for their particular applications. It is shown that the results of predictions are in acceptable agreement with experimental data indicating the capability of the adaptive neuro-fuzzy inference system for predicting hydrate formation conditions of natural gases.     

Investigation of the antibacterial activity and efflux pump inhibitory effect of co-loaded piperine and gentamicin nanoliposomes in methicillin-resistant Staphylococcus aureus

Objective: Antibiotic resistance has stimulated the research for developing novel strategies that can prevent bacterial growth. Methicillin-resistant Staphylococcus aureus (MRSA), regarded as one of the most serious antibiotic-resistant bacteria which has been conventionally recognized as a nosocomial pathogen.

Materials and methods: Nanoliposomal formulations of piperine and gentamicin were prepared by dehydration-rehydration (DRV) method and characterized for size, zeta potential and encapsulation efficiency. Antibactericidal activities of liposomal and free forms were evaluated against MRSA ATCC 43300 by the determination of minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and fractional inhibitory concentration index (FICI). The time-kill studies were carried out to evaluate the potency of antibacterial agents. The effect of piperine on bacterial efflux pumps was also investigated.

Results: MIC values of gentamicin and piperine were 32 and 100?µg/mL, respectively. Synergetic effects were observed by the combination of gentamicin and piperine and FICI was determined to be 0.5. Following incorporation of gentamicin into liposomal gentamicin and liposomal combination, the MIC values were reduced 16- and 32-fold, respectively. MBC values of gentamicin reduced 4 and 8 times following incorporation into gentamicin and combination liposomes, respectively. In comparison with vancomycin, liposomal combination was more effective in bacterial inhibition and killing. Liposomal combination was the most effective preparations in time-kill study. Our findings indicated that liposomal piperine was able to inhibit the efflux pump sufficiently.

Conclusion: The results of this study revealed that liposomal combination is a powerful nano-antibacterial agent to eradicate MRSA infection. This dual-loaded formulation was an effective approach for eradication of MRSA.     

Processing of Parboiled Wheat Noodles Fortified with Pulsed Ultrasound Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L. var. Malas) Peel Extract

The main objective of this study was to improve the antioxidant properties of parboiled wheat noodles (salted and yellow alkaline noodles) by adding different concentrations of pomegranate peel extract (PPE) into the noodle formulations (0, 0.75, and 1.50%) in order to produce the acceptable new product. The total phenolic contents and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity varied from 4.07 to 8.61 mg GAE/g and from 23.20 to 95.16%, respectively. The white salted noodle fortified with 1.50% PPE had the highest antioxidant activity among all prepared noodles. The fortified noodles showed significant (p ≤ 0.05) differences in terms of color and textural properties as compared to the control noodle. The fortified white salted noodles had a light brown color with a soft texture, whereas the fortified yellow alkaline noodles were substantially darker and harder than the white salted noodles. The fortified white noodles had a shorter optimum cooking time than the yellow noodles. The addition of PPE to the formulation of both noodles led to a reduction of pH. The current study revealed that the fortified noodles had more homogeneous and compact microstructure than the control noodle. There was no significant difference (p > 0.05) between the overall acceptability of both fortified noodles.     

On the Mutual Effect of Viscoplasticity and Interfacial Damage Progression in Interfacial Fracture of Lead-Free Solder Joints

The main goal of this paper is to shed light on the effect of strain rate and viscoplastic deformation of bulk solder on the interfacial failure of lead-free solder joints. For this purpose, interfacial damage evolution and mode I fracture behavior of the joint were evaluated experimentally by performing stable fracture tests at different strain rates employing an optimized tapered double cantilever beam (TDCB) design. The viscoplastic behavior of the solder was characterized in shear, and the constitutive parameters related to the Anand model were determined. A rate-independent cohesive zone damage model was identified to best simulate the interfacial damage progression in the TDCB tests by developing a three-dimensional (3D) finite-element (FE) model and considering the viscoplastic response of the bulk solder. The influence of strain rate on the load capability and failure mode of the joint was clarified by analyzing the experimental and simulation results. It was shown how, at the lower strain rates, the normal stress generated at the interface is limited by the significant creep relaxation developed in the bulk solder and thus is not sufficiently high to initiate interfacial damage, whereas at higher rates, a large amount of the external energy is dissipated into interfacial damage development.     

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.