Syllabus

Fundamentals of Linear Regression [14 hrs]
• Introduction to regression analysis
• Simple linear regression and its assumptions
• Estimation of regression coefficients
• Hypothesis testing in linear regression.
• Model evaluation metrics: R-squared, adjusted R-squared, Mean Squared Error (MSE)
• Introduction to multiple linear regression

Empirical Model Development [5 hrs]
• Factorial, fractional factorial and rotatable central composite experimental design
• Developing empirical equations using experimental data.

Artificial Neural Networks (ANN) [8 hrs]
• Overview of the biological basis for ANNs
• Neurons, activation functions, and the sigmoid function
• The backpropagation algorithm for training ANNs
• Feedforward Neural Networks
• Developing predictive model using Neural network

Optimization using Genetic Algorithm [5 hrs]
• Overview of the biological basis for genetic algorithms
• Genetic operators, such as selection, crossover, and mutation
• Fitness functions and their role in optimization
• Optimization of processing parameters using Genetic algorithms

Convolutional Neural Network (CNN) [8 hrs]
• Introduction to CNNs.
• CNN-based simulation in food processing.
• Tools and software for simulation in food processing

To equip students with skills to develop predictive models using linear regression, multiple regression, and empirical modeling techniques for food processing applications.

To train students in optimizing food processing parameters using Artificial Neural Networks (ANNs) and Genetic Algorithms.

To help students utilize deep learning models by developing and evaluating Convolutional Neural Networks (CNNs) for food process simulations and predictions.

Develop and analyze linear and multiple regression models for predicting food process outcomes.

Design empirical models using appropriate experimental design techniques.

Implement and evaluate ANN models and genetic algorithms for food process optimization.

Develop and evaluate the CNN based models.

M.H. Kutner, C.J. Nachtsheim, J. Neter, W. Li, Applied Linear Statistical Models, McGraw-Hill , 5th Edition

D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons , 5th Edition

H. Das, Food Processing Operations, Asian Books Private Limited , 1st Edition

R.J. Freund and W.J. Wilson, Statistical Methods, Academic Press , 2nd Edition

Journal: Trends in Food Science & Technology; Publisher: Elsevier B.V. ; ISSN: 1879-3053

Conference: IFT FIRST Annual Event & Expo, Institute of Food Technologists, USA