Electronic commerce or ecommerce is a term for any type of business, or profitable transaction, that includes the transmission of information transversely the Internet. It covers a range of unlike types of businesses, from consumer-based retail sites, through sale or music sites, to business connections trading goods and services among companies. It is currently one of the most significant aspects of the Internet to develop. Ecommerce allows customers to electronically exchange goods and services with no barricades of time or distance. Electronic commerce has prolonged rapidly over the past five years and is forecast to continue at this rate, or even hasten. In the near future the limitations between "conventional" and "electronic" commerce will become gradually blurred as more and more businesses move The analysis and advice presented in this is based on the data collected from the study conducted in Bangalore with a sample size of 100 which includes 63 male respondents and 37 female respondents within the age group of 18 to 40 years and above. All the data was represented in the form of tables and charts (pie charts). Correlation analysis has been used to examine the hypothesis and the findings of the survey by using MS-Excel. During the study it was found that Artificial Intelligence has a major role to play in managing the customer relationship of e-commerce giant like Amazon and people are now getting familiar about the benefits that Artificial Intelligence brings and how it totally enhances their user experience.
Little research exists on Salmonella inactivation during extrusion processing, yet many outbreaks associated with low water activity foods since 2006 were linked to extruded foods. The aim of this research was to study Salmonella inactivation during extrusion of a model cereal product. Oat flour was inoculated with Salmonella enterica serovar Agona, an outbreak strain isolated from puffed cereals, and processed using a single-screw extruder at a feed rate of 75 kg/h and a screw speed of 500 rpm. Extrudate samples were collected from the barrel outlet in sterile bags and immediately cooled in an ice-water bath. Populations were determined using standard plate count methods or a modified most probable number when populations were low. Reductions in population were determined and analyzed using a general linear model. The regression model obtained for the response surface tested was Log (N R /N O ) = 20.50 + 0.82T − 141.16a w -0.0039T 2 + 87.91a w 2 (R 2 = 0.69). The model showed significant (p < 0.05) linear and quadratic effects of a w and temperature and enabled an assessment of critical control parameters. Reductions of 0.67 ± 0.14 to 7.34 ± 0.02 log CFU/g were observed over ranges of a w (0.72 to 0.96) and temperature (65 to 100°C) tested. Processing conditions above 82°C and 0.89 a w achieved on average greater than a 5-log reduction of Salmonella. Results indicate that extrusion is an effective means for reducing Salmonella as most processes commonly employed to produce cereals and other low water activity foods exceed these parameters. Thus, contamination of an extruded food product would most likely occur postprocessing as a result of environmental contamination or through the addition of coatings and flavorings.Keywords: cereal, extrusion, flour, low water activity, Salmonella, thermal inactivation Practical Application: This study investigated inactivation of Salmonella enterica serovar Agona during extrusion processing of an oat flour model food over a wide range of water activity (0.72 to 0.96) and temperature (65 to 100°C). Results of this study indicate that extrusion is an effective means for reducing bacterial pathogens and may be used by industry when establishing critical limits for extrusion processes.
Inactivation curves of Listeria monocytogenes by high pressure were obtained at three pressure levels (400, 500 and 600 MPa) and three temperature levels (27, 43 and 60C) in ultra‐high temperature (UHT) whole milk. The milk samples after treatment were plated on both nonselective and selective media to determine the log reduction value and to obtain the inactivation curve. The inactivation curves were fitted to the widely used two‐parameter Weibull model with parameters α (characteristic time) and β (shape factor) and a reduced one‐parameter Weibull model where the α value with the correct physical interpretation was predetermined. The log reduction value at the characteristic time from the regressed two‐parameter Weibull model and the experimental data must both be 0.434; however, in the literature, there is evidence that the regressed model and the experimental values do not match always. Accordingly, this study focuses on the two‐parameter and one‐parameter Weibull models to bring out this inconsistency. Although there was loss in goodness of fit by adapting the one‐parameter model, yet the model represented the correct physical interpretation of parameter α. Development of such mathematically consistent models may help industry to design, develop and optimize safe processing conditions that are based on reliable model parameters.Practical ApplicationIn previous studies, inactivation curves of microorganisms have been modeled using the widely used two‐parameter Weibull model. However, it was not verified or investigated whether the parameters obtained by nonlinear regression represent the correct experimental value, especially the scale factor/characteristic time. The log reduction value obtained at a treatment time equals 5 to the regressed scale characteristic time should be 1/0.434 depending on the form of Weibull model used. However, in the literature, there is evidence that the regressed model and the experimental values do not match always. In this study, it was shown that predetermining the scale factor/characteristic time from the experimental data addresses this self‐consistency issue. Development of such mathematically consistent models would help industry to design reliable and optimum safe processing conditions.
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