Inhibitory effects of a novel antimicrobial peptide from kefir against Escherichia coli

Antimicrobial peptide F1, a novel antimicrobial peptide from Tibetan kefir, have shown strong antimicrobial activity against several bacteria and fungi. We identified the amino acid sequence and studied the antimicrobial mechanism of peptide F1 against Escherichia coli. Our results showed that antimicrobial peptide F1 contained 18 amino acids (Thr-DAP-Asn-Thr-PEA-His-Pro-Asn-Thr-His-Leu-Ile- PEA-CySH-Val-Asn-PEA-Tau), which increased the outer and inner membrane permeability of E. coli, and the leakage of the cytoplasmic β-galactosidase and potassium ions was detected in the process. Morphologies of E. coli were observed by confocal laser scanning microscopy and transmission electron microscopy, which visually showed that antimicrobial peptide F1 could penetrate and accumulate into cell causing disruption of cell membrane functions. Furthermore, we elucidated the DNA binding ability of antimicrobial peptide F1 by agarose gel retardation and atomic force microscopy. Our findings indicated that antimicrobial peptide F1 has multiple targets in the killing of E. coli.     

Improving the antimicrobial activity of d-limonene using a novel organogel-based nanoemulsion

The purpose of this research was to develop a novel antimicrobial delivery system by encapsulating d-limonene into an organogel-based nanoemulsion and investigating its antimicrobial activity. The d-limonene organogel-based nanoemulsion was prepared by high pressure homogenization method. The surfactant concentration had a major impact on the droplets' formation and distribution. At the optimal condition (10% w/w Tween 80, 100 Mpa, and 10 Cycles) the smallest droplet size (d ≈ 36 nm) could be obtained, which has shown a narrow structure and good stability. Results from the antimicrobial activity have shown the encapsulation of d-limonene (4% w/w) into the organogel-based nanoemulsion contributed to the increase of its antimicrobial activity. In addition, the mechanism of d-limonene organogel-based nanoemulsion against the tested microorganisms was studied by the electronic microscope observation and the cell constituent release. This research would have an important implication for the design of more efficient antimicrobial systems for food preservation and production.     

An antimicrobial peptide screened from casein hydrolyzate by Saccharomyces cerevisiae cell membrane affinity method

A Saccharomyces cerevisiae cell membrane affinity screening method was developed to separate targeted antimicrobial peptides from the pepsin hydrolyzate of bovine casein. S. cerevisiae cell membranes were first immobilized on the surface of the silica gel to construct an affinity binding medium. A membrane-binding fraction was successfully screened by comparing the RP-HPLC fingerprint chromatograms of the hydrolyzate before and after adsorption with the adsorption medium. The amino acid sequence of the peptide was identified as LRLKKYKVPQL with the use of a matrix-assisted laser desorption/ionisation quadrupole time-of-flight tandem mass spectrometer. The sequence corresponded to amino acid residues 99–109 of bovine α_S1-casein. The results indicated that it is feasible to target screen antimicrobial peptides from protein hydrolyzate using S. cerevisiae cell membranes. The influences of thermal treatment, pH, ions, and enzymes on the activity of the purified peptide were also determined. The activity of the peptide was relatively thermally stable and was pH dependent. It retained more than 90% of its activity in the presence of 15% Na⁺, K⁺ and pepsin. Trypsin, proteinase K, divalent cation Mg²⁺ and Ca²⁺ reduced the activity to different extents. The peptide also showed antibacterial effectiveness in fresh pear juice. These observations provide further information on the application of protein-derived antimicrobial peptides in food systems.     

Membrane disruption and DNA binding of Staphylococcus aureus cell induced by a novel antimicrobial peptide produced by Lactobacillus paracasei subsp. tolerans FX-6

Previously, we have isolated a novel bacteriocin, peptide F1 from Tibetan Kefir, and demonstrated its superior antimicrobial activity. However, its antimicrobial mechanism is still undefined. This study was aimed to elucidate the antimicrobial mechanism of peptide F1 against Staphylococcus aureus. The antimicrobial effects of peptide F1 were characterized by the following methods: chemical assay to quantify cytoplasmic β-galactosidase leakage, atomic absorption spectrometry to measure the released potassium ions, transmission electron microscopy to visualize the cellular morphological changes, and electrophoresis analysis and atomic force microscopy together to exam the DNA binding activity. Our results revealed that peptide F1 exerted its bactericidal effects by damaging bacterial cell membranes and by binding to the genomic DNA in the cytoplasm, which both led to rapid cell death.     

Antilisterial activity and stability of nanovesicle-encapsulated antimicrobial peptide P34 in milk

Bacillus sp. P34, a strain isolated from aquatic environments of Brazilian Amazon basin, produces a bacteriocin-like substance (BLS) which was encapsulated in nanovesicles prepared from partially purified soy lecithin. The efficiency of free and encapsulated BLS P34 to control the development of L. monocytogenes and maintenance of antimicrobial activity was assessed over time in milk. The antimicrobial activity of free and encapsulated BLS P34 decreased approximately 50% after 4 days of storage (<4 °C) in skim and whole milk. After this period there was not significant loss of activity up to 21 days. The viable counts of Listeria monocytogenes in skim and whole milk containing 3200 AU/ml of free or encapsulated BLS P34 were always lower than those observed in controls without bacteriocin at both 30 °C and 7 °C. At 1600 AU/ml concentration, free and encapsulated BLS P34 were inhibitory to L. monocytogenes in skim milk, when compared with the control at 7 days. Nanovesicle-encapsulated and free BLS P34 shows potential use as biopreservative for application in milk-derived products.     

A novel antimicrobial substance produced by Lactobacillus rhamnous LS8

Superbugs or multi-drug resistant pathogens are not only clinical issue but also highlighted in food safety. However, development of new antibiotics and safe preservatives is declining. A novel workflow was used to purify antimicrobial substance produced by Lactobacillus rhamnosus LS-8, in which ethyl acetate extraction, polypeptide column and HPLC were involved in the purification process. Subsequently, a novel antimicrobial substance was purified and identified as 4,6-dimethyl-1,2,5-triazepane-3,7-dione (C₆H₁₁N₃O₂) by mass spectrum, ¹H NMR and ¹³C NMR analysis. Fortunately, it has significant inhibitory effect on both gram-positive and gram negative strains including multi-drug resistance pathogens. Also it had good heat stability and resistant to proteolytic enzymes, however, it was only active in acidic condition. The results of scanning electron microscopy (SEM) and transmission electron microscope (TEM) showed that LS-8-25 destroyed the ultrastructure of S. aureus and E. coli. The discovery is adding a new variety of antimicrobial substance produced by lactic acid bacteria.     

Operationalising a performance objective with a microbiological criterion using a risk-based approach

In this concept paper, we have developed three case scenarios to illustrate how one can derive a Microbiological Criterion (MC) from a Performance Objective (PO) or from a Food Safety Objective (FSO). In the first scenario, we show how one can derive an MC from a PO that is set as a numerical limit to the concentration of a pathogen. In the second scenario, it is shown how one can derive an MC from a PO that is set as the limit to the prevalence or proportion of a microorganism. In the final scenario, we show how to derive an MC from an FSO for a product supporting growth of the target pathogen between the PO and FSO. These case scenarios present guidance on how to derive an MC from risk-based metrics and very explicitly detail all the steps to be taken and assumptions/decisions that need to be made. In all three cases, MCs could successfully be established, but to do so required specific data, assumptions and decisions, as appropriate.     

Utilization of RBF-ANN as a novel approach for estimation of asphaltene inhibition efficiency

One of the problematic concerns in petroleum industries is the deposition of heavy fractions of crude oil such as asphaltene fraction during production and transportation. The utilization of inhibitors is known as a relative low cost and effective method for asphaltene inhibition. In this study, Radial basis function artificial neural network (RBF-ANN) was applied to predict asphaltene precipitation reduction in terms of structure and concentration of inhibitor and oil properties. In order to training and testing of RBF-ANN the required data are extracted from reliable sources. The predicted asphaltene precipitation reduction values were compared with the actual data statistically and graphically. The coefficients of determination for training and testing phases of RBF-ANN were determined as 0.995906 and 0.994853 respectively. These evaluations showed that the RBF-ANN as a predictive tool has great capacity to estimate effect of asphaltene inhibitors on reduction of asphaltene precipitation.     

Applying ANFIS-PSO algorithm as a novel accurate approach for prediction of gas density

The accurate estimations of processes in gas engineering need a high degree of accuracy in calculations of gas properties. One of these properties is gas density which is straightly affected by pressure and temperature. In the present work, the Adaptive neuro fuzzy inference system (ANFIS) algorithm joined with Particle Swarm Optimization (PSO) to estimate gas density in terms of pressure, temperature, molecular weight, critical pressure and critical temperature of gas. In order to training and testing of ANFIS-PSO algorithm a total number of 1240 experimental data were extracted from the literature. The statistical parameters, Root mean square error (RMSE), coefficient of determination (R²) and average absolute relative deviation (AARD) were determined for overall process as 0.14, 1 and 0.039 respectively. The determined statistical parameters and graphical comparisons expressed that predicting mode is a robust and accurate model for prediction of gas density. Also the predicting model was compared with three correlations and obtained results showed the better performance of the proposed model respect to the others.     

Fundamental study of a novel membrane filtration cleanup method for pesticide analysis in agricultural products

The recovery and purification characteristics of membrane filtration for pesticide analysis of agricultural products were investigated. Eight different types of membranes classified by their molecular weight cut off (MWCO) and material were used. The results showed that the recovery and purification characteristics varied according to the eluting solvent used, as well as the membrane's MWCO and material. The recovery increased as the MWCO increased, and the purification increased when the eluting solvent contained water. A GK membrane with acetonitrile-water (1:1, v/v) was the most effective membrane filtration method among those tested. The pesticide analysis of spinach using the GK filtration method indicated that this method results in better purification than the modified QuEChERS method.     

Deep gas cleaning of highly permeating impurities using a membrane module with a feed tank

Deep gas cleaning of highly permeating impurities using a membrane module with a feed tank has been considered. Two modes of the process has been studied, one with a constant degree of separation in the membrane module and the other with a constant concentration of impurities in the stream directed to the receiver tank. The degree of removal and the time of the process have been calculated. It has been shown that the second mode is somewhat more cost-effective because of the lack of mixing of streams with different concentrations and the absence of the lost work of separation.     

Applying of LSSVM approach as a novel tool for accurate prediction of asphaltene inhibition efficiency

Asphaltene precipitation is one of critical problems for petroleum industries. There are different methods for inhibition of asphaltene precipitation. One of the common and effective methods for inhibition of asphaltene precipitation is utilizing asphaltene inhibitors. In this work, Least squares support vector machine (LSSVM) algorithm was coupled with simplex optimizer to create a novel and accurate tool for estimation of effect of inhibitors on asphaltene precipitation as function of concentration and structure of inhibitors and crude oil properties. To this end a total number of 75 measured data was extracted from the literature for training and testing of predicting model. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) of total data for prediction algorithm were determined as 1.1479, 0.99406 and 0.61039. According to these parameters and graphical comparisons the LSSVM algorithm has potential to predict asphaltene precipitation in high degree of accuracy.     

Application of novel ANFIS-PSO approach to predict asphaltene precipitation

Asphaltene precipitation is known as one of the challenging problems in petroleum industries which have significant effects on production such as formation damage and wellbore plugging. To solve this problem, calculation of precipitated asphaltene becomes highlighted so in the present study a novel approach is proposed based on ANFIS algorithm to estimate precipitated asphaltene in terms of dilution ration, carbon number of precipitants and temperature. The particle swarm optimization (PSO) method is applied to optimize ANFIS algorithm parameters. The proposed model was evaluated based on statistical parameters and the calculated R², AARD and RMSE for the total data are 0.90309, 9.4908 and 7.9468. They showed the predicting algorithm performed in acceptable manner so a high accurate and simple tool was proposed to predict the precipitated asphaltene as function of Dilution ration, temperature and carbon number of precipitants.     

Prediction of dynamic viscosity of n-alkanes at high pressures using a rigorous approach

An adaptive neuro-fuzzy interference system has been developed for estimating the dynamic viscosity of n-alkanes in a wide range of operating conditions. In this study, for the first time, a simple predictive model is proposed for viscosity prediction of n-pentane, n-octane, n-nonane, n-decane and dodecane at various pressures and temperatures, especially at high pressures, without needing to measurement or estimation of density. This tool predicts the dynamic viscosity of the n-alkanes as function of pressure, temperature and n-alkanes' molecular weight. The obtained results of the model were in an excellent agreement with experimental data with an acceptable coefficient of determination of 0.999 for both training and testing datasets. Moreover, the validity of the proposed model for viscosity trends prediction at various conditions was demonstrated and it showed a very good match with actual data. This model is simple to use and can be of massive evaluation for better understanding the behavior of fluids under reservoir conditions.     

Water reuse in a shrimp processing line: Safety considerations using a HACCP approach

The large consumption of water involved in food manufacturing is prompting food processors to optimise the use. Treating and reusing or recycling water within the food plant results in substantial reduction of water use and wastewater production and discharge. If implemented, water reuse should be integrated into existing HACCP programs, and HACCP plans specifically addressed to the actual reuse should be elaborated. This paper evaluates the microbiological safety issues associated with water recycling during the production of shrimps (Pandalus borealis) in brine, and it indicates how the hazards may be effectively controlled using a HACCP approach. Following these procedures, process water recovered from peeling during shrimp processing and treated by means of reverse osmosis could be recycled within the same food unit operation.     

Rapid and specific sensing of tetracycline in food using a novel upconversion aptasensor

Rare earth-doped upconversion nanoparticles (UCNPs) have a promising potential in biodetection due to their unique frequency upconverting capability and high detection sensitivity. This study developed a novel and sensitive aptamer-based UCNPs (NaY_0.48Gd_0.3F₄:Yb_0.2, Ho_0.02) biosensor for sensing tetracycline (TET) in foodstuffs. The aptamer-magnetic nanoparticles (aptamer-MNPs) and the complementary DNA-UCNPs (cDNA-UCNPs) conjugates were prepared and used as the capture and signal probes, respectively. They formed the complex of MNPs-aptamer-cDNA-UCNPs with complete binding. With TET addition, the aptamer preferentially bound with TET and caused the dissociation of some cDNA; and the liberation of some cDNA-UCNPs. This led to a decreased fluorescent signal on the surface of MNPs. Under the optimal conditions, a wide linear detection range from 0.01 to 100 ng/mL was achieved, with a limit of detection of 0.0062 ng/mL, for detection of TET. The proposed method was successfully applied to measure TET in contaminated samples. Results showed that the proposed UCNPs biosensor offers an efficient, specific, and simple approach for the detection of TET in food, and has high potential for food safety and quality control.     

一种新型生物油酯磺酸盐驱油剂的制备及性能

用发烟浓硫酸对油酯进行磺化,水解、中和后制成了磺酸盐驱油剂。最佳的反应条件为:油酯与发烟浓硫酸的体积比为7:1;反应温度为40℃;反应时间为4h。对产物用红外光谱和核磁共振1H谱进行的结构分析结果显示,油酯的不饱和双键发生了磺化反应,生成了饱和的羟基磺酸盐。对产物的界面活性进行的测定结果表明,在不加碱及其他助剂的情况下,该驱油剂能大幅度降低胜利油田采油一区、采油二区的油水界面张力至超低值10^-3mN/m,最低值达到7×10^-4mN/m。该驱油剂具有较好的抗盐和抗Ca²⁺、Mg²⁺能力,在总盐度为(2~130)×10³mg/L范围内和Ca²⁺、Mg²⁺总浓度低于500mg/L时都具有较高的界面活性。     

Emulsification of Indian heavy crude oil using a novel surfactant for pipeline transportation

The most economical way to overcome flow assurance problems associated with transportation of heavy crude oil through offshore pipelines is by emulsifying it with water in the presence of a suitable surfactant.In this research,a novel surfactant,tri-triethanolamine monosunflower ester,was synthesized in the laboratory by extracting fatty acids present in sunflower(Helianthus annuus)oil.Synthesized surfactant was used to prepare oil-in-water emulsions of a heavy crude oil from the western oil field of India.After emulsification,a dramatic decrease in pour point as well as viscosity was observed.All the prepared emulsions were found to be flowing even at 1°C.The emulsion developed with 60%oil content and 2wt%surfactant showed a decrease in viscosity of 96%.The stability of the emulsion was investigated at different temperatures,and it was found to be highly stable.The effectiveness of surfactant in emulsifying the heavy oil in water was investigated by measuring the equilibrium interfacial tension(IFT)between the crude oil(diluted)and the aqueous phase along with zeta potential of emulsions.2wt%surfactant decreased IFT by almost nine times that of no surfactant.These results suggested that the synthesized surfactant may be used to prepare a stable oil-in-water emulsion for its transportation through offshore pipelines efficiently.     

Application of Fuzzy C-means algorithm as a novel approach to forecast gas density in different conditions

One of the dominant parameters in accurate calculation and forecasting processes gas industries is accurate estimation of gas properties. The gas density is known as an effective parameter in gas processes calculations which affected by pressure and temperature. In the present paper, the Fuzzy c-means (FCM) algorithm is utilized as a novel predictive tool to estimate gas density as function of molecular weight, critical pressure and critical temperature of gas, pressure and temperature. In the purpose of training and testing of proposed FCM algorithm, a total number of 1240 measured data were gathered from reliable sources. The outputs of model and experimental data comparisons showed the great agreement between them such that the coefficients of determination for training and testing datasets were determined as 0.9982 and 0.9903 respectively. According to the obtained results from the graphical and statistical comparisons it can be concluded that the FCM algorithm has great ability and enough accuracy in prediction of gas density.