Clarification of Pineapple Juice Following Egg Albumin Pretreatment: Investigation of Membrane Fouling Dynamics Using Hermia’s Empirical Models | Chapter 7 | Food Science and Agriculture: Research Highlights Vol. 1

Pineapple (Ananas comosus L., Merril) is a tropical non-citrus fruit, mainly because of its attractive aroma, refreshing flavour and Brix/acid ratio. This juice has been used in fruit-based beverages individually, in the form of a mixture or combined with other fruit juices. The present study investigated membrane fouling during the clarification of pineapple juice after pretreatment with egg albumin. Pineapple (Ananas comosus L., Merril) is the most popular tropical non-citrus fruit, mainly because of their attractive aroma, refreshing flavour and Brix/acid ratio. The research was focused on the membrane clarification of pineapple juice following pretreatment. Pretreatment of pineapple juice was performed using egg albumin with different concentrations and observed that 2 g/L concentration gave effective removal of colloidal substances of pineapple juice. The membrane processing of pineapple juice was performed with all different pore sizes, pressures, and flow rates and the coefficient of determination (R²) values were analysed.

Hermia’s empirical models were applied to evaluate the fouling phenomena in the microfiltration (MF) and ultrafiltration (UF) of pineapple juice. The flux data was fitted into existing fouling models to elucidate the fouling mechanisms during membrane processing. The coefficient of determination (R²) values for the gel layer model ranged from 0.829 to 0.957 for the 0.2 µm pore size membrane when filtering pineapple juice. In the Microfiltration (MF) membrane, the data revealed that IPB was predominant, as the pore size of the membrane is larger than Ultrafiltration (UF). The coefficient of determination (R²) values for the gel layer model were in the range of 0.829 to 0.957 for 0.2 µm pore size membrane when pineapple juice was filtered. R² values suggested intermediate pore blocking followed by gel layer formation.

 

Author (s) Details

 

Samreen
Department of Food Process Engineering, College of Food Science and Technology, PJTS Agricultural University, Rudrur - 503188, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/fsarh/v1/4581

Predicting Rice Crop Yields in Prayagraj: A Comparison of Regression Techniques and Neural Networks | Chapter 3 | Geography, Earth Science and Environment: Research Highlights Vol. 5

This study investigates rice crop yield prediction in Prayagraj District, Uttar Pradesh, using twenty-nine years of historical weather and crop yield data from 1991 to 2019. The analysis divided the dataset into a calibration segment comprising 90% of the data over 26 years, with the remaining 10% used for validation. In our approach, 75.9% of the data was used to train an artificial neural network (ANN) model, and 24.1% was employed for testing and validation to ensure 100% model efficiency evaluation. Techniques such as stepwise linear regression and neural networks were applied to predict rice yields. The performance of these models was measured primarily by the Normalized Root Mean Squared Error (nRMSE), with the regression-based model achieving superior performance, indicated by the lowest nRMSE values. The study also noted the critical role of Bright Sunshine Hours, which demonstrated a significant predictive power with an nRMSE of 0.00025 and a coefficient of determination of 0.94.

 

Author (s) Details

 

Nilesh Kumar Singh
Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.

 

Shraddha Rawat
Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/geserh/v5/4249

Computational Framework for Trend Pattern Analysis of Wheat Production in Some Wheat Growing States of India | Chapter 11 | Research Advances and Challenges in Agricultural Sciences Vol. 7

The present study deals with the trend pattern analysis of wheat production in some wheat growing states of India on fitting well-known statistical models. The analysis was carried out on utilizing secondary time series data on wheat production. The trend values were obtained on fitting the statistical models, and the goodness of fit of the models was tested using chi-square test. Furthermore, statistical measures, viz. coefficient of determination (R2), root mean square error (RMSE), and relative mean absolute percentage error (RMAPE) were computed for revealing the model accuracy. The model with R2>0.5, and least values of RMSE and RMAPE, is regarded as the best model. Among the models considered under study, the cubic model was found to be the best in terms of precision for exploring the trends of wheat production in the concerned states. The findings of the study provide some useful insights on statistical modeling techniques for forecasting the scenario of wheat production in the concerned states.

Author(s) Details:

Manish Kumar,
Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, 224229, India.

Gyan Prakash,
Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, 224229, India.

Shiv Kumar Rana,
Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, 224229, India.

Please see the link here: https://stm.bookpi.org/RACAS-V7/article/view/14269

Study on Fixed Effect versus Random Effects Modeling in a Panel Data Analysis; a Consideration of Economic and Political Indicators in Six African Countries | Chapter 5 | Recent Advances in Mathematical Research and Computer Science Vol. 4

 Individual heterogeneity in panel data analysis can be addressed to avoid bias in the final estimations. The data in a panel might be balanced or imbalanced, and the panel can be short or long. The fixed effects and random effects modelling methodologies were applied to an economic data set, "Africa," in the R Amelia package, to determine the right model. Both models were fitted to the data, with the assumptions of both models taken into account. The random model estimates passed the Breusch-Pagan Lagrange Multiplier test, however it had a weak coefficient of determination (R2 of 0.48697). The fixed effect was then estimated using four alternative methods (Pooled, LSDV, Within-Group, and First Differentiation) and compared to the random effect model using the Hausman test. The random effect was shown to be inconsistent in all tests, indicating that the fixed effect was more appropriate for the data. The LSDV was determined to have the best fit among the fixed effects models, with an R2 of 0.8851.

Author(S) Details

M. T. Nwakuya
University of Port Harcourt, Rivers State, Nigeria.

M. A. Ijomah
University of Port Harcourt, Rivers State, Nigeria.

View Book:- https://stm.bookpi.org/RAMRCS-V4/article/view/4871