Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Artificial neural networks in food industry

999 views

Published on

Introduction to ANN in Food Industry

Published in: Food
  • Be the first to comment

Artificial neural networks in food industry

  1. 1. ARTIFICIAL NEURAL NETWORK IN FOOD INDUSTRY PRAGATI SINGHAM PH.D. (FS & PHT) 10703 ICAR-IARI
  2. 2. CONTENTS 1. Introduction 2. Biological inspiration 3. Architecture 4. Applications 5. Advantages 6. Disadvantages 7. Conclusion 8. References
  3. 3. INTRODUCTION Food Industry(ibef, 2015) High growth and high potential Current value : US$ 39.71 billion Indian Food Industry • Investment in food processing sector of Rs.100,000 crores (Union Budget 2015-16) • Contributes about 14% of manufacturing GDP • 1st International Mega Food Park worth Rs.136 crores at Punjab, India
  4. 4. DIFFERENT SECTORS OF FOOD INDUSTRY Sectors of Foodindustry Dehydration Baking Canning Extrusion
  5. 5. PROBLEMS ASSOCIATED WITH.. Lack of validity of empirical models in simulating wide range of temperatures, air velocity and humidity during drying (Tohidi et al., 2012) Complexity of mathematical models and large computation time required for modeling of drying process of food (Singh and Pandey, 2011) Dehydration Baking Extrusion Canning
  6. 6. CONTD. Lack of non-linear interdependence of viscoelastic properties and gas retention properties on rheological properties of dough (Abbasi, Djomeh and Seyedin, 2011) Insufficiency in bake level inspection of biscuits (Yeh and Leonard, 1994) Dehydration Baking Canning Extrusion
  7. 7. CONTD. Lack of precision in simulation of dynamic temperature during retort processing (Llave, Hagiwara and Sakiyama, 2012) Dehydration Baking Canning Extrusion
  8. 8. CONTD. Complexity in modelling of non-linear relationship among variables of extrusion (Popescu et al.,2000) Dehydration Baking Extrusion Canning
  9. 9. MAJOR PROBLEMS IDENTIFIED Complexity of biomaterial Non-linearity of process Large computational time Wide range of parameters Precision
  10. 10. ARTIFICIAL NEURAL NETWORK HISTORY
  11. 11. ARTIFICIAL NEURAL NETWORK • It is a dynamic computational modeling tool to solve real- world problems (Chen et al., 2007) • It is comprised of densely interconnected adaptive simple processing elements that are capable of performing massively parallel computations for data processing.
  12. 12. BIOLOGICAL INSPIRATION An artificial neuron is an imitation of the human neuron
  13. 13. WORKING
  14. 14. CONTD.
  15. 15. CONTD.
  16. 16. MODELING WITH ANN R2 Root Mean Square Error RMSE Back Propagation
  17. 17. TRAINING Supervised Learning Reinforcement Learning Unsupervised Learning
  18. 18. ARCHITECTURE Forward feed network Radial Basic Function (RBF) Network Self Organizing Maps (SOMs)
  19. 19. APPLICATIONS Prediction Optimization Control Classification
  20. 20. PREDICTION OF HYDRATION CHARACTERISTICS OF PADDY (KALE ET AL., 2013) Hydration : Important process in parboiling (pre-treatment) to attain complete gelatinization of paddy Model used : Generalized Page Model : Artificial Neural Network ANN Multilayer perceptron Neural Network
  21. 21. RESULTS Data Points 108 Training 60 Testing 21 Validation 27 Modelling of ANN Model R2 MSE Generalized Page Model 0.65 0.0018 Multilayer perceptron Network 0.99 0.0013 Comparison between Generalized Page Model and Multilayer Perceptron Network
  22. 22. RESULTS (a) Generalized Page Model (B) MLP network MOISTURE RATIO
  23. 23. ADVANTAGES Exploits non-linearity High computational speed Offers wide range Learning ability Fault tolerance
  24. 24. DISADVANTAGES Works as black-box Large amount of training data Overfitting of data
  25. 25. CONCLUSION • ANN can be successfully used for modeling complex food materials • Prediction of food characteristics in various thermo- physical processes at high computational rate • Optimization of the supply chain process, parameters, cost and manpower • Control of the quality of the finished or new product can be quantified
  26. 26. REFERENCES Abbasi, H., Djomeh, E.J. and Seyedin, S.M (2011). Applicatin of Artificial Neural Network and Genetic Algorithm for predicting three important parameters in Bakery Industries, 2, 51-63. Chen, C.R., Ramaswamy, H.S. and Marcotte, M. (2007). Neural network applications in heat and mass transfer operation in food processing chapter Heat transfer in food processing, © WIT Press, 13, 39-59. Kale, S.J., Jha, S.K., Jha, G.K., and Samuel, V.K. (2013) Evaluation and modelling of Water absorption characteristics of paddy. J of Agricultural Engg. 50 (3), 29-38. Llave, Y.A., Hagiwara, T. and Sakiyama, T. (2012). Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch based foods. Journal of food engineering, 109, 553-560. Singh, N.J. and Pandey, R.K. (2011). Neural Network approches fr prediction of drying kinetics during drying of sweet potato. Agricultural Engineering International, 13, 11-22. Tohidi, M., Sadeghi, M., Mousavi, S.M. and Mireei, S.A (2012). Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turk Journal of Agriculture, 36, 738-748. Yeh, J.C.H. and Haney, L.C.G (1994). Biscuit bake assessment by an Artificial Neural Network, 5, 266-269. http://www.ibef.org/industry/indian-food-industry.aspx
  27. 27. Thank you

×