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20193
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Subject: Statistics × End date: 2019 × Start date: 2010 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Effect of sugar syrup adulteration on ultrasonic velocity and viscosity of honey
    (Punjab Agricultural University, Ludhiana, 2019) Jaswal, Deepshikha; Paramjit Singh
    The usage of honey for consumption and as a medicine has been appreciated over decades. The large consumption rate of honey also introduces malpractices which places impure honey in the market. These malpractices include the adulteration of honey, which deteriorates its quality as well as its medicinal value. Honey is adultered either by directly feeding sugar to the bees or by the addition of molasses, glucose, sucrose, water and sugar syrups into honey. The method suitable for detection of adulteration of honey is the non destructive test. Through this test the chemical composition of honey remains the same. Ultrasonic waves are used for the detection as these are non destructive, non toxic and used in food processing. In the present study simple sugar syrup was prepared and was used as an adulterant. The parameters like ultrasonic velocity, viscosity, density and pH were evaluated for different concentrations (100, 90, 80, 70, 60 and 50%) of honey at different temperatures (20, 30, 40, 50, 60 and 70˚C). From these parameters thermodynamic parameters like surface tension, adiabatic compressibility, bulk modulus, acoustic impedance and intermolecular free length were measured. All parameters except, density and pH show significant results with temperature. The viscosity decreased exponentially whereas; ultrasonic velocity, density, pH, surface tension, bulk modulus and acoustic impedance decreased linearly with temperature. Other parameters like adiabatic compressibility and intermolecular free length increased linearly with temperature. A linear relation was observed between ultrasonic velocity and logarithmic viscosity. Correlation between ultrasonic velocity, surface tension and density was also confirmed.
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    Wheat yield forecasting using statistical models
    (Punjab Agricultural University, Ludhiana, 2019) Lovepreet Kaur; Amrit Kaur
    The present study had been conducted to develop models based on weather variables for forecasting the wheat productivity of Amritsar, Ludhiana and Patiala districts of Punjab. The forty years data: (1970-71 to 2009-10) on wheat productivity and weather variables were used for model development and seven years data (2010-11 to 2016-17) for validation. The linear and non-linear models; simple linear, quadratic, cubic, fourth degree polynomial, monomolecular, logistic and gompertz were developed to remove the effect of technological factors over time. On the basis of goodness of fit statistics, the logistic model came best from linear and non-linear models. The detrended wheat productivity obtained after fitting the logistic model was used for forecasting the wheat productivity on the basis of weather variables; maximum temperature, minimum temperature, rainfall, relative humidity morning, relative humidity evening and bright sunshine hours. The fourteen weeks weather data of vegetative period of wheat crop had been utilized to forecast the productivity. Three weather indices based models; model I (un-weighted), model II (weighted) and model III (combined) were developed for each district using weather indices and detrended wheat productivity. The stepwise regression technique was applied and the results revealed that weighted model (model II) declared as best model for Amritsar, Ludhiana and Patiala districts explaining 60%, 67% and 52% variation in the detrended wheat productivity and had RMSPE 8.57%, 6.93% and 6.20% respectively. The weighted interaction of maximum and minimum temperature played crucial role in wheat productivity. The selected models followed the assumptions of residuals.
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    Construction of New Ratio Type Estimator of Population Parameter
    (Punjab Agricultural University, Ludhiana, 2019) Manjinder Singh; Javed, Mohammed
    In the present project research, an improved ratio type estimator of population mean for the study variable (Y) has been proposed, using the information on the auxiliary variable (X). The sample was selected from the population using simple random sampling without replacement (SRSWOR). The expressions for large sample properties such as bias and mean squared error (MSE) of proposed ratio type estimator were obtained, up to the first order of approximation. To judge the performance of the proposed ratio type estimator of population mean, an empirical study was conducted on the basis of MSE and percent relative efficiency (PRE). To achieve the merits of the proposed ratio type estimator, a comparison study was carried out with respect to the existing ratio estimators present in the literature. The optimum value of the characterizing scalar (α) has been obtained and the minimum value of the mean square error of the proposed ratio type estimator for this optimum value has also been obtained. The theoretical results are validated numerically using four population data sets considered earlier by Murthy (1967) and Mukhopadhyay (2009). Also, the graphical illustrations shows that the proposed ratio-type estimator performs better than the existing estimators of population mean under certain conditions.