This document discusses artificial neural networks and their applications in the food industry. It begins with an introduction to the food industry in India and some of the major problems faced in food processing sectors like dehydration, baking, canning and extrusion. These problems include a lack of valid models for wide temperature and humidity ranges during drying and complex non-linear relationships between variables. The document then provides an overview of artificial neural networks, including their biological inspiration, architecture, training methods, and advantages like exploiting non-linearity and learning ability. Several applications of neural networks are presented, including predicting hydration of paddy and modeling temperature during retort processing. The conclusion states that neural networks can successfully model complex foods and optimize supply chain processes

1. ARTIFICIAL
NEURAL NETWORK
IN
FOOD INDUSTRY
PRAGATI SINGHAM
PH.D. (FS & PHT)
10703
ICAR-IARI

2. CONTENTS
1. Introduction
2. Biological inspiration
3. Architecture
4. Applications
5. Advantages
6. Disadvantages
7. Conclusion
8. References

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. DIFFERENT SECTORS OF FOOD INDUSTRY
Sectors
of
Foodindustry
Dehydration
Baking
Canning
Extrusion

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. 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. CONTD.
Lack of precision in simulation of dynamic
temperature during retort processing
(Llave, Hagiwara and Sakiyama, 2012)
Dehydration
Baking
Canning
Extrusion

8. CONTD.
Complexity in modelling of non-linear
relationship among variables of
extrusion (Popescu et al.,2000)
Dehydration
Baking
Extrusion
Canning

9. MAJOR PROBLEMS IDENTIFIED
Complexity of biomaterial
Non-linearity of process
Large computational time
Wide range of parameters
Precision

10. ARTIFICIAL NEURAL NETWORK
HISTORY

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. BIOLOGICAL INSPIRATION
An artificial neuron is an imitation of the human neuron

13. WORKING

14. CONTD.

15. CONTD.

16. MODELING WITH ANN
R2
Root Mean Square Error
RMSE
Back
Propagation

17. TRAINING
Supervised
Learning
Reinforcement
Learning
Unsupervised
Learning

18. ARCHITECTURE
Forward feed
network
Radial Basic Function
(RBF) Network
Self Organizing
Maps
(SOMs)

19. APPLICATIONS
Prediction
Optimization
Control
Classification

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. 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. RESULTS
(a) Generalized Page Model (B) MLP network
MOISTURE RATIO

23. ADVANTAGES
Exploits non-linearity
High computational speed
Offers wide range
Learning ability
Fault tolerance

24. DISADVANTAGES
Works as black-box
Large amount of training data
Overfitting of data

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. 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. Thank you