6,401 research outputs found

    Test for the presence of autocorrelation in the modified Gompertz model used in fitting of Burkholderia sp. strain Neni-11 growth on acrylamide

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    The growth of microorganism on substrates, whether toxic or not usually exhibits sigmoidal pattern. This sigmoidal growth pattern can be modelled using primary models such as Logistic, modified Gompertz, Richards, Schnute, Baranyi-Roberts, Von Bertalanffy, Buchanan three-phase and Huang. Previously, the modified Gompertz model was chosen to model the growth of Burkholderia sp. strain Neni-11 on acrylamide, which shows a sigmoidal curve. The modified Gompertz model relies on the ordinary least squares method, which in turn relies heavily on several important assumptions, which include that the data does not show autocorrelation. In this work we perform statistical diagnosis test to test for the presence of autocorrelation using the Durbin-Watson test and found that the model was adequate and robust as no autocorrelation of the data was found

    Empirical Model for Predicting Rate of Biogas Production

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    Rate of biogas production using cow manure as substrate was monitored in two laboratory scale batch reactors (13 liter and 108 liter capacities). Two empirical models based on the Gompertz and the modified logistic equations were used to fit the experimental data based on non-linear regression analysis using Solver tool in Microsoft Excel. The 13-liter reactor was used in Experiments1 & 2 and in Experiment 3 the 108-liter reactor was used. In all the three experiments, Gompertz model gave a better goodness of fit than the modified Logistic model. The cross correlation coefficients for experiment 3 are 0.9972 and 0.9965 for Gompertz and Modified Logistic models respectively. Atomic absorption spectroscopy (AAS) analysis of the biogas indicates that its methane content is above 70% in both reactors.Keywords: Empirical model, Non-linear regression, biogas production, cow manure

    Identifiability of Baranyi model and comparison with empirical models in predicting effect of essential oils on growth of Salmonella typhimurium in rainbow trout stored under aerobic, modified atmosphere and vacuum packed conditions

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    The structural identifiability properties of the Baranyi model were analyzed in fitting the effect of oregano and thyme essential oils on growth of Salmonella typhimurium in rainbow trout stored under aerobic (AP), modified atmosphere (MAP) and vacuum packed (VP) conditions. Although, formally proven to be structurally identifiable using the Taylor-series approach, the Baranyi model was not practically identifiable in the presence of experimental data. In addition, performance of the Baranyi model was compared with those of the empirical modified Gompertz and logistic models and Huang models. Higher values of R2, modeling efficiency and lower absolute values of mean bias error, root mean square error, mean percentage error and chi-square were obtained with modified Gompertz and logistic models than those obtained with the Huang and Baranyi models. The essential oil and packing treatments had remarkable delaying effects on the growth of S. typhimurium. Considering the obtained results in this study, the empirical modified Gompertz and logistic models can be used more effectively than the mechanistic Huang and Baranyi models to predict the effect of plant essential oils on growth potential of Salmonella in fish products stored under aerobic, MAP/VP conditions.Key words: Identifiability of Baranyi model, predictive microbiology, Salmonella typhimurium, essential oil, packing treatments

    A new modified logistic growth model for empirical use

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    Richards model, Gompertz model, and logistic model are widely used to describe growth model of a population. The Richards growth model is a modification of the logistic growth model. In this paper, we present a new modified logistic growth model. The proposed model was derived from a modification of the classical logistic differential equation. From the solution of the differential equation, we present a new mathematical growth model so called a WEP-modified logistic growth model for describing growth function of a living organism. We also extend the proposed model into couple WEP-modified logistic growth model. We further simulated and verified the proposed model by using chicken weight data cited from the literature. It was found that the proposed model gave more accurate predicted results compared to Richard, Gompertz, and logistic model. Therefore the proposed model could be used as an alternative model to describe individual growth

    Improvement of biomethane potential of sewage sludge anaerobic co-digestion by addition of “sherry-wine” distillery wastewater

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    Co-digestion of sewage sludge (SS) with other unusually treated residues has been reported as an efficient method to improve biomethane production. In this work, Sherry-wine distillery wastewater (SWDW) has been proposed as co-substrate in order to increase biomethane production and as a breakthrough solution in the management of both types of waste. In order to achieve this goal, different SS:SW-DW mixtures were employed as substrates in Biomethane Potential (BMP) tests. The biodegradability and biomethane potential of each mixture was determined selecting the optimal co-substrate ratio. Results showed that the addition of SW-DW as a co-substrate improves the anaerobic digestion of SS in a proportionally way in terms of CODs and biomethane production The optimal co-substrates ratio was 50:50 of SS:SW-DW obtaining %VSremoval ¼ 54.5%; YCH4 ¼ 225.1 L CH4/kgsv or 154 L CH4/kgCODt and microbial population of 5.5 times higher than sole SS. In this case, %VSremoval ¼ 48.1%; YCH4 ¼ 183 L CH4/ kgsv or 135 L CH4/kgCODt. The modified Gompertz equation was used for the kinetic modelling of biogas production with successful fitting results (r2 ¼ 0.99). In this sense, at optimal conditions, the maximum productivity reached at an infinite digestion time was (YMAX CH4 ) ¼ 229 ± 5.0 NL/kgSV; the specific constant was K ¼ 25.0 ± 2.3 NL/kgSV$d and the lag phase time constant was (l) ¼ 2.49 ± 0.1

    On Bivariate Exponentiated Extended Weibull Family of Distributions

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    In this paper, we introduce a new class of bivariate distributions called the bivariate exponentiated extended Weibull distributions. The model introduced here is of Marshall-Olkin type. This new class of bivariate distributions contains several bivariate lifetime models. Some mathematical properties of the new class of distributions are studied. We provide the joint and conditional density functions, the joint cumulative distribution function and the joint survival function. Special bivariate distributions are investigated in some detail. The maximum likelihood estimators are obtained using the EM algorithm. We illustrate the usefulness of the new class by means of application to two real data sets.Comment: arXiv admin note: text overlap with arXiv:1501.03528 by other author

    The Heumann-Hotzel model for aging revisited

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    Since its proposition in 1995, the Heumann-Hotzel model has remained as an obscure model of biological aging. The main arguments used against it were its apparent inability to describe populations with many age intervals and its failure to prevent a population extinction when only deleterious mutations are present. We find that with a simple and minor change in the model these difficulties can be surmounted. Our numerical simulations show a plethora of interesting features: the catastrophic senescence, the Gompertz law and that postponing the reproduction increases the survival probability, as has already been experimentally confirmed for the Drosophila fly.Comment: 11 pages, 5 figures, to be published in Phys. Rev.

    Inactivation of Escherichia coli ATCC 25922 and Saccharomyces cerevisiae IMR-R-L 962 in grapefruit [Citrus paradisi (Macf.)] juice by UV-C light: changes in bioactive compounds and quality characteristics

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    The viability of UV-C treatment (0.0-2.36 J/cm²) at 254 nm as a non-thermal preservation process for grapefruit juice on microbial inactivation, the organic acids and individual flavonoids, as well as the quality characteristics (pH, ºBrix, titratable acidity, colour, total phenolics and antioxidant capacity) was evaluated. Additionally, pectin methylesterase (PME) activity was also measured. The effects of UV-C on microbial inactivation were assessed by kinetic studies on the inactivation of inoculated grapefruit juice with one strain of Escherichia coli ATCC 25922 and one strain of Saccharomyces cerevisiae IMR-R-L 962. The suitability of Weibull distribution and modified Gompertz models was analysed to characterise the UV-C inactivation kinetics for E. coli and S. cerevisiae in freshly squeezed grapefruit juice. Likewise, the changes after UV-C treatment in citric (CA), malic (MA), ascorbic (AA) and tartaric (TA) acids, as well as naringin (NAR), hesperidin (HES) and neohesperidin (NEO), were quantified by HPLC, whereas the total phenolics and antioxidant capacity (DPPH? and ABTS?+) were quantified by spectrophotometric methods. Nonlinear inactivation curves were successfully fitted with Weibull-type and modified Gompertz models. However, the Gompertz model allowed a better fit and more accurate estimation of the parameters. UV-C treatment at 1.83 J/cm² achieved a 5.18 ± 0.01 and 2.7 ± 0.15 log CFU/mL reduction in E. coli and S. cerevisiae, respectively, whereas no significant changes occurred in CA, MA, TA, NAR, HES, NEO, total phenolics, ABTS?+, pH, ºBrix, titratable acidity and colour of the grapefruit juices (p>0.05). However, PME was partially inhibited and the AA level and DPPH? decreased significantly after treatment, with losses up to 15.9 and 8% (at 1.83 J/cm²), respectively, which were associated with the UV-C dose intensity.Fil: la Cava, Enzo Luciano Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Sgroppo, Sonia Cecilia. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentin