ARTIFICIAL NEURAL NETWORK IN

FOOD INDUSTRY

PRAGATI SINGHAMPH.D. (FS & PHT)

10703ICAR-IARI

CONTENTS

1. Introduction 2. Biological inspiration 3. Architecture4. Applications5. Advantages6. Disadvantages7. Conclusion 8. References

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

DIFFERENT SECTORS OF FOOD INDUSTRY

Sectors of

Food industry

Dehydration

Baking

Canning

Extrusion

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

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

CONTD.

Lack of precision in simulation of dynamic temperature during retort processing (Llave, Hagiwara and Sakiyama, 2012)

Dehydration

Baking

Canning

Extrusion

CONTD.

Complexity in modelling of non-linear relationship among variables of extrusion (Popescu et al.,2000)

Dehydration

Baking

Extrusion

Canning

MAJOR PROBLEMS IDENTIFIED

Complexity of biomaterial

Non-linearity of process

Large computational time

Wide range of parameters

Precision

ARTIFICIAL NEURAL NETWORK

HISTORY

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.

BIOLOGICAL INSPIRATION

An artificial neuron is an imitation of the human neuron

WORKING

CONTD.

CONTD.

MODELING WITH ANN

R2

Root Mean Square ErrorRMSE

Training Testing

Back Propagation

TRAINING

Supervised Learning

ReinforcementLearning

Unsupervised Learning

ARCHITECTURE

Forward feed network

Radial Basic Function (RBF) Network

Self Organizing Maps

(SOMs)

APPLICATIONS

Prediction

Optimization

Control

Classification

PREDICTION OF HYDRATION CHARACTERISTICS OF PADDY (KALE ET AL., 2013)

Hydration : Important process in parboiling (pre-treatment) to attain complete gelatinization of paddyModel used : Generalized Page Model

: Artificial Neural NetworkANN

Multilayer perceptron Neural Network

RESULTS

Data Points 108Training 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

RESULTS

(a) Generalized Page Model (B) MLP network

MOISTURE RATIO

ADVANTAGES

Exploits non-linearity

High computational speed

Offers wide range

Learning ability

Fault tolerance

DISADVANTAGES

Works as black-box

Large amount of training data

Overfitting of data

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

REFERENCESAbbasi, 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

Thank you