Kinetics of Adhesion Staphylococcus aureus on Glass in the Presence of Sodium Lauryl Sulfate

Bacterial contamination of surfaces is a natural and spontaneous process that often results in the formation of biofilms. The extracellular matrix of biofilm is mostly composed of proteins, polysaccharides, and extracellular DNA and is responsible for the strong persistent ability of biofilm in the food industry. Despite cleaning and disinfection processes, persistent bacteria cause a major problem in food processing environments. Synthetic surfactants, mainly anionic surface-active agent, are commonly used as detergents, foaming agents, wetting agents, emulsifiers, and dispersants. Their tendency to adsorb to surfaces and interfaces and modify their surface tension, is considered among their main properties. They also have the ability to attach to bioactive macromolecules such as proteins, peptides, and DNA causing cell membrane damage. In order to estimate the adhesion kinetic and proliferation of pathogenic bacteria Staphylococcus aureus, the surface of glass was coated with anionic surfactant Sodium Lauryl Sulfate (SLS). Moreover, SLS was added in suspension with the culture medium. The physicochemical properties of the material were calculated using the contact angle measurement method and bacterial hydrophobicity using the microbial adhesion to hydrocarbons (MATH) test. The obtained results showed that the number of adhering cells increased gradually as a function of time. However, changing the surface properties of the glass and S. aureus has affected the rate of adherent cells with time as well as their organization. SLS inhibited the attachment of cells, whether it is added with the microbial suspension or at the surface of the support. Generally, the present article points to a relationship between the microbial adhesion, the surface chemistry of the solid material and the bacteria, and the suspension properties.     

Detection of Electrical Fault in Medium Voltage Installation Using Support Vector Machine and Artificial Neural Network

Russian Journal of Nondestructive Testing - Infrared thermography plays an important role in the inspection of electrical installations allowing to avoid failures and breakdowns. Condition...     

Inferential sensor – Based adaptive principal components analysis for mechanical properties prediction and evaluation

This paper is concerned with a method for on-line quality prediction and evaluation of mechanical properties in metal testing. This method uses an Adaptive Principal Component Analysis (APCA) as a multi predictor of different sub-models defining the mechanical properties such as constraints limits and elongation. The PCA technique, characterized by its multivariate component, is strongly recommended to model a multi-input-multi-output system. The complex system is generally known as a non-linear and unsteady state process. The PCA method is a linear projection. To adapt it and to improve the prediction accuracy, a variant of this method is considered based on iteratively using a specific algorithm. This kind of approaches is applied for constructing an inferential model, which allows a reliable and accurate predictor. Simulation results, based on the measured and computed data using the above-cited method, show that the proposed approach is easily implementable and give an accurate prediction.     

Application of ultrasound and its real-time monitoring of the acoustic field during processing of tofu: Parameter optimization,protein modification,and potential mechanism

Tofu is nutritious, easy to make, and popular among consumers. At present, traditional tofu production has gradually become perfect, but there are still shortcomings, such as long soaking time, serious waste of water resources, and the inability to realize orders for production at any time. Moreover, tofu production standards have not yet been clearly defined, with large differences in quality between them, which is not conducive to industrialized and large-scale production. Ultrasound has become a promising green processing technology with advantages, such as high extraction rate, short processing time, and ease of operation. This review focused on the challenges associated with traditional tofu production during soaking, grinding, and boiling soybeans. Moreover, the advantages of ultrasonic processing over traditional processing like increasing nutrient content, improving gel properties, and inhibiting the activity of microorganisms were explained. Furthermore, the quantification of acoustic fields by real-time monitoring technology was introduced to construct the theoretical correlation between ultrasonic treatments and tofu processing. It was concluded that ultrasonic treatment improved the functional properties of soybean protein, such as solubility, emulsifying properties, foamability, rheological properties, gel strength, and thermal stability. Therefore, the application of ultrasonic technology to traditional tofu processing to optimize industrial parameters is promising.     

Total phenols and antioxidant capacity in 10 Moroccan pomegranate varieties

The study focused on characterizing the 10 major Moroccan cultivars to acknowledge the quality and bioactive compounds of the fruits. The following determinations were assessed: pomegranate fruit maturity index, which truly defines juice taste, as well as organic acids and sugar contents, total phenols, and antioxidant activity of pomegranate juice. The cultivar per se (genotype) behaved as the most influencing factor conditioning pomegranate sugar and organic acids profiles, antioxidant activity, and total phenolics. The assessment of pomegranate chemical compositions implies the great potential of Moroccan cultivars for both fresh market and fruit processing. In fact, the "Hamde" sour cv. seems particularly suitable for juice production because of its high phenolics content. Additionally, the presence of bioactive compounds in pomegranate juices may encourage their consumption for potential health benefits. PRACTICAL APPLICATION: The cultivar per se (genotype) behaved as the most influencing factor conditioning pomegranate sugar and organic acids profiles, antioxidant activity, and total phenolics. Given the divergence observed on bioactive compounds concentrations and antioxidant activity among evaluated cultivars, the genotype factor should be considered as the most influencing factor in future breeding programs to enhance the synthesis of beneficial bioactive compounds.     

Behavior of Listeria monocytogenes and Staphylococcus aureus in yogurt fermented with a bacteriocin-producing thermophilic starter

Streptococcus salivarius subsp. thermophilus B producing a bacteriocin active against Listeria monocytogenes ATCC 7644 and Staphylococcus aureus SAD 30 was isolated from bakery yeast. The bacteriocin was partially purified by an adsorption/desorption technique, and its spectrum of action was compared to that of a neutralized cell-free supernatant (CFS). Although the CFS inhibited a number of gram-positive and -negative bacteria of health and spoilage significance, the spectrum of action of the partially purified bacteriocin was limited to gram-positive bacteria. L. monocytogenes was the most sensitive to both preparations. The bacteriocin-producing streptococcal strain was used in combination with a Bac- Lactobacillus delbrueckii subsp. bulgaricus CY strain isolated from commercial yogurt to assess the effectiveness of the resulting thermophilic starter in controlling L. monocytogenes and S. aureus in yogurt during fermentation and storage at refrigeration (ca. 7 degrees C) or abuse (ca. 22 degrees C) temperature. Yogurt samples were contaminated with L. monocytogenes or S. aureus to the approximate levels of 10(3) and 10(6) CFU/ml of milk, respectively. The results showed that in situ bacteriocin production was more active against L. monocytogenes than against S. aureus in vitro and in contaminated samples. While L. monocytogenes leveled off below the detectable limit in a 1-ml sample of yogurt within 24 h of processing, S. aureus survived in Bac+ and Bac- samples during 10 days of storage at room temperature (ca. 22 degrees C). Use of a Bac+ starter resulted in a 5-day extension of the shelf life.     

Feature selection for fault detection systems: application to the Tennessee Eastman process

In fault detection systems, a massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kinds of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with a kind of failure than another. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, which aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, conventional feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm uses k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator. The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with conventional algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms.