Effect of citric acid and glycine addition on acrylamide and flavor in a potato model system

Acrylamide levels in cooked/processed food can be reduced by treatment with citric acid or glycine. In a potato model system cooked at 180 degrees C for 10-60 min, these treatments affected the volatile profiles. Strecker aldehydes and alkylpyrazines, key flavor compounds of cooked potato, were monitored. Citric acid limited the generation of volatiles, particularly the alkylpyrazines. Glycine increased the total volatile yield by promoting the formation of certain alkylpyrazines, namely, 2,3-dimethylpyrazine, trimethylpyrazine, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, and 2,5-diethyl-3- methylpyrazine. However, the formation of other pyrazines and Strecker aldehydes was suppressed. It was proposed that the opposing effects of these treatments on total volatile yield may be used to best advantage by employing a combined treatment at lower concentrations, especially as both treatments were found to have an additive effect in reducing acrylamide. This would minimize the impact on flavor but still achieve the desired reduction in acrylamide levels.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Response of the middle atmosphere to CO2 doubling: results from the Canadian Middle Atmosphere Model

The Canadian Middle Atmosphere Model (CMAM) has been used to examine the middle atmosphere response to CO2 doubling. The radiative-photochemical response induced by doubling CO2 alone and the response produced by changes in prescribed SSTs are found to be approximately additive, with the former effect dominating throughout the middle atmosphere. The paper discusses the overall response, with emphasis on the effects of SST changes, which allow a tropospheric response to the CO2 forcing. The overall response is a cooling of the middle atmosphere accompanied by significant increases in the ozone and water vapor abundances. The ozone radiative feedback occurs through both an increase in solar heating and a decrease in infrared cooling, with the latter accounting for up to 15% of the total effect. Changes in global mean water vapor cooling are negligible above ~30 hPa. Near the polar summer mesopause, the temperature response is weak and not statistically significant. The main effects of SST changes are a warmer troposphere, a warmer and higher tropopause, cell-like structures of heating and cooling at low and middlelatitudes in the middle atmosphere, warming in the summer mesosphere, water vapor increase throughout the domain, and O3 decrease in the lower tropical stratosphere. No noticeable change in upwardpropagating planetary wave activity in the extratropical winter–spring stratosphere and no significant temperature response in the polar winter–spring stratosphere have been detected. Increased upwelling in the tropical stratosphere has been found to be linked to changed wave driving at low latitudes.

A dynamic model of neurovascular coupling: Implications for blood vessel dilation and constriction

Neurovascular coupling in response to stimulation of the rat barrel cortex was investigated using concurrent multichannel electrophysiology and laser Doppler flowmetry. The data were used to build a linear dynamic model relating neural activity to blood flow. Local field potential time series were subject to current source density analysis, and the time series of a layer IV sink of the barrel cortex was used as the input to the model. The model output was the time series of the changes in regional cerebral blood flow (CBF). We show that this model can provide excellent fit of the CBF responses for stimulus durations of up to 16 s. The structure of the model consisted of two coupled components representing vascular dilation and constriction. The complex temporal characteristics of the CBF time series were reproduced by the relatively simple balance of these two components. We show that the impulse response obtained under the 16-s duration stimulation condition generalised to provide a good prediction to the data from the shorter duration stimulation conditions. Furthermore, by optimising three out of the total of nine model parameters, the variability in the data can be well accounted for over a wide range of stimulus conditions. By establishing linearity, classic system analysis methods can be used to generate and explore a range of equivalent model structures (e.g., feed-forward or feedback) to guide the experimental investigation of the control of vascular dilation and constriction following stimulation.

A generalised Bayesian instrumental variable approach under student t-distributed errors with application

Bayesian analysis is given of an instrumental variable model that allows for heteroscedasticity in both the structural equation and the instrument equation. Specifically, the approach for dealing with heteroscedastic errors in Geweke (1993) is extended to the Bayesian instrumental variable estimator outlined in Rossi et al. (2005). Heteroscedasticity is treated by modelling the variance for each error using a hierarchical prior that is Gamma distributed. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm with an augmented draw for the heteroscedastic case. An example using real data illustrates the approach and shows that ignoring heteroscedasticity in the instrument equation when it exists may lead to biased estimates.

How well can we model stream phosphorus concentrations in agricultural catchments?

Mechanistic catchment-scale phosphorus models appear to perform poorly where diffuse sources dominate. We investigate the reasons for this for one model, INCA-P, testing model output against 18 months of daily data in a small Scottish catchment. We examine key model processes and provide recommendations for model improvement and simplification. Improvements to the particulate phosphorus simulation are especially needed. The model evaluation procedure is then generalised to provide a checklist for identifying why model performance may be poor or unreliable, incorporating calibration, data, structural and conceptual challenges. There needs to be greater recognition that current models struggle to produce positive Nash–Sutcliffe statistics in agricultural catchments when evaluated against daily data. Phosphorus modelling is difficult, but models are not as useless as this might suggest. We found a combination of correlation coefficients, bias, a comparison of distributions and a visual assessment of time series a better means of identifying realistic simulations.

Additive genetic relationships between scrotal circumference, heifer pregnancy, and stayability in Nellore cattle

Scrotal circumference data from 47,605 Nellore young bulls, measured at around 18 mo of age (SC18), were analyzed simultaneously with 27,924 heifer pregnancy (HP) and 80,831 stayability (STAY) records to estimate their additive genetic relationships. Additionally, the possibility that economically relevant traits measured directly in females could replace SC18 as a selection criterion was verified. Heifer pregnancy was defined as the observation that a heifer conceived and remained pregnant, which was assessed by rectal palpation at 60 d. Females were exposed to sires for the first time at about 14 mo of age (between 11 and 16 mo). Stayability was defined as whether or not a cow calved every year up to 5 yr of age, when the opportunity to breed was provided. A Bayesian linear-threshold-threshold analysis via Gibbs sampler was used to estimate the variance and covariance components of the multitrait model. Heritability estimates were 0.42 +/- 0.01, 0.53 +/- 0.03, and 0.10 +/- 0.01, for SC18, HP, and STAY, respectively. The genetic correlation estimates were 0.29 +/- 0.05, 0.19 +/- 0.05, and 0.64 +/- 0.07 between SC18 and HP, SC18 and STAY, and HP and STAY, respectively. The residual correlation estimate between HP and STAY was -0.08 +/- 0.03. The heritability values indicate the existence of considerable genetic variance for SC18 and HP traits. However, genetic correlations between SC18 and the female reproductive traits analyzed in the present study can only be considered moderate. The small residual correlation between HP and STAY suggests that environmental effects common to both traits are not major. The large heritability estimate for HP and the high genetic correlation between HP and STAY obtained in the present study confirm that EPD for HP can be used to select bulls for the production of precocious, fertile, and long-lived daughters. Moreover, SC18 could be incorporated in multitrait analysis to improve the prediction accuracy for HP genetic merit of young bulls.

Deviance residuals in generalised log-gamma regression models with censored observations

In this article, we compare three residuals based on the deviance component in generalised log-gamma regression models with censored observations. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. For all cases studied, the empirical distributions of the proposed residuals are in general symmetric around zero, but only a martingale-type residual presented negligible kurtosis for the majority of the cases studied. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for the martingale-type residual in generalised log-gamma regression models with censored data. A lifetime data set is analysed under log-gamma regression models and a model checking based on the martingale-type residual is performed.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)