Study on the glycerolysis reaction of high free fatty acid oils for use as biodiesel feedstock

Biodiesel is the main alternative to fossil diesel and it may be produced from different feedstocks such as semi-refined vegetable oils, waste frying oils or animal fats. However, these feedstocks usually contain significant amounts of free fatty acids (FFA) that make them inadequate for the direct base catalyzed transesterification reaction (where the FFA content should be lower than 4%). The present work describes a possible method for the pre-treatment of oils with a high content of FFA (20 to 50%) by esterification with glycerol. In order to reduce the FFA content, the reaction between these FFA and an esterification agent is carried out before the transesterification reaction. The reaction kinetics was studied in terms of its main factors such astemperature, % of glycerin excess, % of catalyst used, stirring velocity and type of catalyst used. The results showed that glycerolysis is a promising pretreatment to acidic oils or fats (> 20%) as they led to the production of an intermediary material with a low content of FFA that can be used directly in thetransesterification reaction for the production of biodiesel. (C) 2011 Elsevier B.V. All rights reserved.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Fatty acids, IL6, and TNFα polymorphisms: an example of nutrigenetics in Crohn's disease

Objectives - The aim of this work was to study the interaction between genetic polymorphisms (single-nucleotide polymorphisms, SNPs) of pro- and anti-inflammatory cytokines and fat intake on the risk of developing Crohn's disease (CD) or modifying disease activity. Methods - Seven SNPs in interleukin 1 (IL1), tumor necrosis factor alpha (TNFalpha), lymphotoxin alpha (LTalpha), and IL6 genes were analyzed in 116 controls and 99 patients with CD. The type of fat intake was evaluated, and the interaction between SNPs and dietary fat in modulating disease activity was analyzed. Results - Individuals who were homozygous for the IL6-174G/C polymorphism had a six-fold higher risk for CD (odds ratio (OR)=6.1; 95% confidence interval (95% CI)=1.9-19.4), whereas the TT genotype on the TNFalpha-857C/T polymorphism was associated with more active disease (OR=10.4; 95% CI=1.1-94.1). A high intake of total, saturated, and monounsaturated fats, as well as a higher ratio of n-6/n-3 polyunsaturated fatty acid (PUFA), was associated with a more active phenotype (P<0.05). Furthermore, there was an interaction between dietary fat intake and SNPs, with a high intake of saturated and monounsaturated fats being associated with active disease, mainly in patients carrying the variant alleles of the 857 TNFalpha polymorphism (OR=6.0, 95% CI=1.4-26.2; OR=5.17; 95% CI=1.4-19.2, respectively) and the 174 IL6 polymorphism (OR=2.95; 95% CI=1.0-9.1; OR=3.21; 95% CI=1.0-10.4, respectively). Finally, low intake of n-3 PUFA and high n-6/n-3 PUFA ratio in patients with the TNFalpha 857 polymorphism were associated with higher disease activity (OR=3.6; 95% CI=1.0-13.0; OR=5.92; 95% CI=1.3-26.5, respectively). Conclusions - These results show that different types of fat may interact with cytokine genotype, modulating disease activity.

The effect of long-chain polyunsaturated fatty acids intake during pregnancy on adiposity of healthy full-term offspring at birth

Objective - The adjusted effect of long-chain polyunsaturated fatty acid (LCPUFA) intake during pregnancy on adiposity at birth of healthy full-term appropriate-for-gestational age neonates was evaluated. Study Design - In a cross-sectional convenience sample of 100 mother and infant dyads, LCPUFA intake during pregnancy was assessed by food frequency questionnaire with nutrient intake calculated using Food Processor Plus. Linear regression models for neonatal body composition measurements, assessed by air displacement plethysmography and anthropometry, were adjusted for maternal LCPUFA intakes, energy and macronutrient intakes, prepregnancy body mass index and gestational weight gain. Result - Positive associations between maternal docosahexaenoic acid intake and ponderal index in male offspring (β=0.165; 95% confidence interval (CI): 0.031–0.299; P=0.017), and between n-6:n-3 LCPUFA ratio intake and fat mass (β=0.021; 95% CI: 0.002–0.041; P=0.034) and percentage of fat mass (β=0.636; 95% CI: 0.125–1.147; P=0.016) in female offspring were found. Conclusion - Using a reliable validated method to assess body composition, adjusted positive associations between maternal docosahexaenoic acid intake and birth size in male offspring and between n-6:n-3 LCPUFA ratio intake and adiposity in female offspring were found, suggesting that maternal LCPUFA intake strongly influences fetal body composition.

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.