RADIAL BASIS ARTIFICIAL NEURAL NETWORK MODELS FOR PREDICTING SOLUBILITY INDEX OF ROLLER DRIED GOAT WHOLE MILK POWDER

Sumit GoyalG. K. Goyal

WSC17 10-21 Dec.2012

INTRODUCTION

A study was planned for predicting the solubility index of roller dried goat whole milk powder by developing radial basis function models.

In today’s tough competition, a key issue that defines the success of a manufacturing organization is its ability to adapt easily to the changes of its business environment.

WSC17 10-21 Dec.2012

INTRODUCTION

It is very useful for a modern company to have a good estimate of how key indicators are going to behave in the future, a task that is fulfilled by forecasting.

A competent predictive method can improve machine utilization, reduce inventories, achieve greater flexibility to changes and increase profits.

WSC17 10-21 Dec.2012

INTRODUCTION

The contribution of goat milk to the economic and nutritional well being of humanity is undeniable in many developing countries, especially in the Mediterranean, Middle East, Eastern Europe and South American countries.

Goat milk has played a very important role in health and nutrition of young and elderly people.

WSC17 10-21 Dec.2012

INTRODUCTION

In present era, the consumers are extremely conscious about quality of the foods they buy.

Regulatory agencies are also very vigilant about quality and safety issues and insist on the manufacturers adhering to the label claims about quality and shelf life.

WSC17 10-21 Dec.2012

INTRODUCTION

Such discerning consumers, therefore, pose a far greater challenge in product development and marketing.

The development of RBF-ANN models for predicting the solubility index of useful dairy product, viz., roller dried goat whole milk powder would be extremely beneficial to the manufactures, retailers, consumers and regulatory agencies from the quality, health and safety points of view.

WSC17 10-21 Dec.2012

WSC17 10-21 Dec.2012

REVIEW OF LITERATURE

Butter Cheese Processed Cheese Milk Burfi Cherries

Cakes Apple juice Chicken nuggets Iranian flat bread Potato chips Pistachio nuts

WSC17

ANNs have been used as a predictive modelling tool for several foods, such as :

WSC17 10-21 Dec.2012

REVIEW OF LITERATURE

WSC17 10-21 Dec.2012

The published literature shows that no study has been conducted using ANN modelling for predictive analysis on goat milk powder.

The present study would be of great significance to the dairy industry, academicians and researchers.

WSC17 10-21 Dec.2012

METHOD MATERIAL

For developing Radial Basis (Exact Fit) and Radial Basis (Fewer Neurons) models for predicting the solubility index of roller dried goat whole milk powder, several combinations were tried and tested to train the RBF-ANN models with spread constant ranging from 10 to 200.

The dataset was randomly divided into two disjoint subsets namely, training set (having 78% of the total observations) and testing set (22% of the total observations).

WSC17 10-21 Dec.2012

METHOD MATERIAL

The input variables for RBF-ANN models were the data of the product pertaining to loose bulk density, packed bulk density, wettability and dispersibility, while solubility index was the output variable.

WSC17 10-21 Dec.2012

METHOD MATERIAL

2

1

expN

cal

n

QQMSE

2

1 exp

exp1 Ncal

Q

QQ

nRMSE

2

12

exp

exp2 1N

cal

Q

QQR

2

1 expexp

exp2 1N

cal

QQ

QQE

(1)

(2)

(3)

(4)

MSE, RMSE, R2 and E2 were used with the aim to compare the prediction ability of the developed models.

WSC17 10-21 Dec.2012

METHOD MATERIAL

Training pattern of ANN models is illustrated:

Training ANN modelsTraining ANN

models

Calculating error and making

adjustment to weights

Calculating error and making

adjustment to weights

Selecting minimum errorSelecting minimum error

WSC17 10-21 Dec.2012

WSC17 10-21 Dec.2012

RESULTS & DISCUSSION

The Radial Basis (Exact Fit) and Radial Basis (Fewer Neurons) models got simulated very well, and gave high R2 and E2 values.

The best results for radial basis model were with the spread constant 20MSE 6.18519E-05; RMSE: 0.007864599; R2: 0.992135401; E2: 0.999938148.

WSC17 10-21 Dec.2012

RESULTS & DISCUSSION

Our results are similar to the earlier findings of Sutrisno et al.(2009), who developed ANN models with backpropagation algorithm to predict mangosteen quality during storage at the most appropriate pre-storage conditions which performed the longest storage period.

In their experiments R2 was found close to 1 (more than 0.99) for each parameter, indicating that the model was good to memorize data.

WSC17 10-21 Dec.2012

RESULTS & DISCUSSION

Fernandez et al.(2006) studied the weekly milk production in goat flocks and clustering of goat flocks by using self organizing maps for prediction, establishing the effectiveness of ANN modelling in animal science applications.

Another study showed that ANN modelling is a successful alternative to statistical regression analysis for predicting amino acid levels in feed ingredients (Cravener et al.,1999).

WSC17 10-21 Dec.2012

WSC17 10-21 Dec.2012

CONCLUSION

The RBF-ANN models predicted the solubility index of roller dried goat whole milk powder with excellent accuracy with coefficient of determination and Nash - Sutcliffe coefficient close to 1.

From the study, it is concluded that RBF-ANN models are a promising tool for predicting the solubility index of roller dried goat whole milk powder.

WSC17 10-21 Dec.2012