Imen Trabelsi

Encapsulation in alginate and alginate coated-chitosan improved the survival of newly probiotic in oxgall and gastric juice

This study was undertaken to develop an optimum composition model for the microencapsulation of a newly probiotic on sodium alginate using response surface methodology. The individual and interactive effects of three independent variables, namely sodium alginate concentration, biomass concentration, and hardening time, were investigated using Box-Behnken design experiments. A second ordered polynomial model was fitted and optimum conditions were estimated. The optimal conditions identified were 2% for sodium alginate, 10(10)UFC/ml for biomass, and 30 min for hardening time. The experimental value obtained for immobilized cells under these conditions was about 80.98%, which was in close agreement with the predicted value of 82.6%. Viability of microspheres (96%) was enhanced with chitosan as coating materials. The survival rates of free and microencapsulated Lactobacillus plantarum TN8 during exposure to artificial gastrointestinal conditions were compared. The results revealed that the encapsulated cells exhibited significantly higher resistances to artificial intestinal juice (AIJ) and artificial gastric juice (AGJ). Microencapsulation was also noted to effectively protect the strain from heating at 65 °C and refrigerating at 4 °C. Taken together, the findings indicated that microencapsulation conferred important protective effects to L. plantarum against the gastrointestinal conditions encountered during the transit of food.

Purification and characterization of a novel exopolysaccharides produced by Lactobacillus sp. Ca6

This study was undertaken to investigate the ability of ten lactic acid bacterial strains to produce exopolysaccharides (EPS) on MRS broth containing 4% sucrose. A maximum EPS production yield of 2.4 g/l was obtained by strain Lactobacillus sp. Ca6. The results from thin layer chromatography (TLC) and high performance chromatography (HPLC) analyses showed that the EPS produced was a polymer of glucose. Further FTIR spectroscopic analysis revealed the presence of carboxyl, hydroxyl and amide groups corresponding to a typical EPS. In addition to EPS production, Lactobacillus sp. Ca6 displayed good probiotic properties (antimicrobial activities and sensitivity to several antibiotics) and resistance to acidic condition (pH 2) and 5% bile bovine. Overall, the findings indicate that this strain has a number of promising properties that make it a potential promising candidate for future application as a food additive.

Effects of Lactobacillus plantarum immobilization in alginate coated with chitosan and gelatin on antibacterial activity

The present study aimed to investigate and evaluate the efficiency of immobilizing the Lactobacillus plantarum TN9 strain in alginate using chitosan and gelatin as coating materials, in terms of viability and antibacterial activity. The results indicate that maximum concentrations of L. plantarum TN9 strain were produced with 2% sodium alginate, 10(8)UFC/ml, and 1M calcium chloride. The viability and antibacterial activity of the L. plantarum TN9 cultures before and after immobilization in alginate, chitosan-coated alginate, and gelatin-coated alginate, were studied. The findings revealed that the viability of encapsulated L. plantarum could be preserved more than 5.8 log CFU/ml after 35 day of incubation at 4 °C, and no effects were observed when gelatin was used. The antibacterial activity of encapsulated L. plantarum TN9 against Gram-positive and Gram-negative pathogenic bacteria was enhanced in the presence of chitosan coating materials, and no activity was observed in the presence of gelatin. The effects of catalase and proteolytic enzymes on the culture supernatant of L. plantarum TN9 were also investigated, and the results suggested that the antibacterial activity observed was due to the production of organic acids. Taken together, the findings indicated that immobilization in chitosan enhanced the antibacterial activity of L. plantarum TN9 against several pathogenic bacteria. This encapsulated strain could be considered as a potential strong candidate for future application as an additive in the food and animal feed industries.

Chemical composition, techno-functional and sensory properties and effects of three dietary fibers on the quality characteristics of Tunisian beef sausage.

This study determined the effects of three dietary fibers namely, VITACEL LC200 powdered cellulose (LC200), barley beta-glucan concentrate (BBC), and VITACEL KF500 potato fiber (KF500), on the techno-functional and sensory properties and quality characteristics of Tunisian beef sausage. The findings revealed interesting functional properties for LC200 fiber. This fiber displayed high water binding capacity (WBC) and oil binding capacity (OBC), values of 16.2 g/g and 10.2 g/g, respectively, which are higher than reported for most fruit and vegetable fiber concentrates. The application of LC200 improved the masticability and elasticity of beef sausage formulations and minimized their hardness and production costs without negatively affecting their sensory properties. Overall, the findings demonstrate the potential functional and economic utility of LC200 fiber as a promising source of dietary fiber.

Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition

Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models GMM and Support Vector Machines SVM. GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters mean vectors, covariance matrices, and mixture weights. GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval GUMI kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral MFCC coefficients and Perceptual Linear Prediction PLP features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

Automatic Speech Emotion Recognition SER is a current research topic in the field of Human Computer Interaction HCI with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speakers utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients MFCC, Linear prediction coefficients LPC, linear prediction cepstrum coefficients LPCC, Perceptual Linear Prediction PLP and Relative Spectral Perceptual Linear Prediction Rasta-PLP features are used to characterize the emotional utterances using a combination between Gaussian mixture models GMM and Support Vector Machines SVM based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

A multi features fusion support vector machine for classification of emotion issue in the design of an audio recognition system

Most state-of-the-art automatic speech emotion recognition rely on utterance level statistics of features. In this study, spoken utterances are represented by a set of statistics from different features computed over all frames. Therefore, for exploiting the complementary emotion-specific information provided by individual features (spectral, prosodic and voice quality features), intelligent combination of features is expected. In this work, we use contour-based low-level descriptors to extract features from the emotional data and then fuse the evidences provided by these features. Finally, multi-class SVM modelling is performed directly at the output of the extracted features. The experiments were carried out on the Berlin corpus consisting of six basic emotions: sadness, boredom, neutral, fear, happiness, anger and the neutral state (no emotion). The results demonstrate that on the average, the features obtained from different information streams and combined at the decision level outperforms the single features or the features combined at the feature level in terms of classification accuracy.

Discrete and continuous emotion recognition using sequence kernels

The field of automatic speech emotion recognition is a highly active and multi-diverse research area. The current state-of-the-art approach in machine analysis of human emotion has focused on recognition of discrete emotional states, such as the six basic emotion categories. However, emotion is deemed complex and is characterised in terms of latent dimensions. Accordingly, this paper aims at recognising discrete and continuous emotional states by adapting the emotional recognition system to the advanced kernel-based machine learning algorithms from the field of speaker recognition, we argue that it is more efficient in terms of recognition performance. The focus in this paper is to build a range of sequence kernel to recognise discrete and continuous emotions from the well-established real-life speech dataset (IEMOCAP) and the acted Berlin emotional speech dataset (Emo-DB).

Effects of supplementation with L. plantarum TN8 encapsulated in alginate-chitosan in broiler chickens.

This study was undertaken to investigate the effects of supplementation of probiotic strain Lactobacillus plantarum TN8 encapsulated in sodium alginate-chitosan or a commercial blend of essential oils on total cholesterol, High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL) and growth performance of broiler chickens. The results showed that the broiler chickens supplemented with encapsulated L. plantarum TN8 or essential oil has a higher growth than the control group. After 35days, the weight means were 1860 and 1880g respectively in dietary supplementation with probiotic or essential oil, while they are 1800g in the control group. The evolution of the feed consumption and feed conversion per week showed that the supplementation of encapsulated TN8 strain or essential oil in broiler chickens food has a positive influence on their appetite. Similarly, supplementation of the feed with this encapsulated strain significantly reduced the rate of cholesterol (HDL and LDL) as well as the contents of triglycerides in broiler chickens. Through our study, it appears that the use of the probiotic supplementation or essential oil to broilers were found to be better than the control group of chickens, resulting in a significant economic impact and promoting effect on health.