Glucosinolates are anti-nutritional factors present abundantly in the seed meal fraction of oilseed Brassica species. They are found in varying levels among different genotypes. Those genotypes containing less than 30 µmol/ g are considered low/zero glucosinolate type and are preferred for edible purposes due to low pungency. Twenty two different genotypes were taken for the analysis of glucosinolates by spectrophotometry. A regression model was obtained using Ordinary Least Square technique which predicted a formula. Total glucosinolates (µmol/g) = 1.40 + 118.86 × A 425 , where A 425 is the absorbance at 425 nm. The total glucosinolate content obtained by the prediction formula when compared with HPLC data showed a correlation coefficient of 0.942. This high correlation between the two data sets validated the developed methodology. This method also simplifies the estimation of total glucosinolates by excluding the use of HPLC or other sophisticated instruments.

No abstract

This paper has studied the autoregressive integrated moving-average (ARIMA) model, generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model along with their estimation procedures for modelling and forecasting of three price series, namely domestic and international edible oils price indices and the international cotton price 'Cotlook A' index. The Augmented Dickey-Fuller (ADF) and Philips Peron (PP) tests have been used for testing the stationarity of the series. Lagrange multiplier test has been applied to detect the presence of autoregressive conditional heteroscedastic (ARCH) effect. A comparative study of the above three models has been done in terms of root mean square error (RMSE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models have been used for diagnostic checking. The study has revealed that the EGARCH model outperformed the ARIMA and the GARCH models in forecasting the international cotton price series primarily due to its ability to capture asymmetric volatility pattern. The SAS software version 9.3 has been used for data analysis.

No abstract

Exponential autoregressive (EXPAR) family of parametric nonlinear timeseries models, which is a discrete-time approximation of continuous-time nonlinear stochastic dynamical system, is considered. A heartening feature of this model is that it is capable of describing those data sets that depict cyclical variations. The estimation procedure for EXPAR models is developed using extended Kalman filter (EKF). Through simulation studies, it is shown that EKF is very efficient for fitting EXPAR models. Formulae for optimal one-step and two-step ahead out-of-sample forecasts are derived analytically by recursive use of conditional expectation. Conditions for the existence of limit cycle behaviour for EXPAR models are also established. Superiority of EKF method vis-a-vis Genetic algorithms (GA) method for fitting EXPAR models is shown through simulation studies. As an illustration, EXPAR models are employed for modelling and forecasting Oil sardine, Mackerel and Bombay duck time-series landings data in India. It is shown that all the three fitted models exhibit the desirable feature of existence of limit cycle behaviour. It is concluded that the EXPAR model performs better than ARIMA methodology for both modelling and forecasting purposes for the data sets under consideration.