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\title{Simulated Neural Network Intelligent Computing Models for Predicting Shelf Life of Soft Cakes}
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             \author[1]{Dr. Sumit  Goyal}

             \author[2]{Gyanendra Kumar  Goyal}

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\date{\small \em Received: 22 June 2011 Accepted: 20 July 2011 Published: 30 July 2011}

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\begin{abstract}
        


This paper highlights the potential of simulated neural networks for predicting shelf life of soft cakes stored at 30o C. Elman and self organizing simulated neural network models were developed. Moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input parameters and overall acceptability score was output parameter. Neurons in each hidden layers varied from 1 to 30. The network was trained with single as well as double hidden layers with 1500 epochs and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. The shelf life predicted by simulated neural network model was 20.57 days, whereas as actual shelf life was 21 days. From the study, it can be concluded that simulated neural networks are excellent tool in predicting shelf life of soft cakes.

\end{abstract}


\keywords{Simulated Neural Networks, Shelf Life, ANN, Elman, Self -Organizing}

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\let\tabcellsep& 	 	 		 \par
ESNN are two layered backpropagation networks, with the addition of a feedback connection from the output of the hidden layer to its input. This feedback path allows ESNN to learn to recognize and generate temporal patterns, as well as spatial patterns.\par
The ESNN has tansig neurons in its hidden (recurrent) layer, and purelin neurons in its output layer. This combination is special in that two-layered networks with these transfer functions can approximate any function (with a finite number of discontinuities) with arbitrary accuracy. The only requirement is that the hidden layer must have enough neurons. More hidden neurons are needed as the function being fitted increases in complexity. ESNN differs from conventional two-layer networks in that the first layer has a recurrent connection. The delay in this connection stores values from the previous time step, which can be used in the current time step. Therefore, even if two ESNN models, with the same weights and biases, are given identical inputs at a given time step, their outputs can be different because of different feedback states. Because the network can store information for future reference, it is able to learn temporal patterns as well as spatial patterns. The ESNN models can be trained to respond to, and to generate, both kinds of patterns \hyperref[b0]{[2]}. The data samples for soft cakes used in this study, relate to overall acceptability score, evaluation at regular intervals by a panel of well trained judges and changes in physicochemical characteristics, viz., moisture, titratable His paper highlights the importance of simulated neural networks for predicting shelf life of soft cakes stored at 10 o C. Soft cakes are exquisite sweetmeat cuisine made out of heat and acid thickened solidified sweetened milk. Soft cakes from water buffalo milk were prepared, milk was standardized to 6\% fat. The cakes were manufactured in a double jacketed stainless steel kettle, and stored at 10 o C. The study of Simulated Neural Networks (SNN) began in the decade before the field artificial intelligence research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm. The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks, which allow feedback. Among the most popular feedforward networks are perceptrons, multi-layer perceptrons. Among recurrent networks, the most popular is the Hopfield net, a form of attractor techniques as Hebbian Learning network, which was first described by John Hopfield in 1982. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, T using such and competitive learning  {\ref [1]}.\par
was 20.57 days, whereas as experimental shelf life was 21 days. From the study, it can be concluded that simulated neural networks are excellent tool in predicting shelf life of soft cakes. 
\section[{b) Self-Organizing}]{b) Self-Organizing} 
\section[{Simulated}]{Simulated}\par
Neural Network (SOSNN) Self-organizing is one of the most interesting topics in the SNN field. These networks can learn to detect regularities and correlations in their input and adapt their future responses to that input accordingly. The neurons learn to recognize groups of similar input vectors. Self-organizing maps learn to recognize groups of similar input vectors in such a way that neurons physically near each other in the neuron layer respond to similar input vectors. Learning vector quantization (LVQ) is a method for training competitive layers in a supervised manner. A competitive layer automatically learns to classify input vectors. However, the classes that the competitive layer finds are dependent only on the distance between input vectors. If two input vectors are very similar, the competitive layer probably will put them in the same class. There is no mechanism in a strictly competitive layer design to say whether or not any two input vectors are in the same class or different classes. LVQ networks, on the other hand, learn to classify input vectors into target classes chosen by the user \hyperref[b0]{[2]} i. Competitive Learning\par
The || dist || box accepts the input vector p and the input weight matrix I W1, 1, and produces a vector having S1 elements. The elements are the negative of the distances between the input vector and vectors i IW1, 1 formed from the rows of the input weight matrix as illustrated in Fig.  {\ref 2} Fig.  {\ref 2} : Architecture of competitive model\par
The net input n1 of a competitive layer is computed by finding the negative distance between input vector p and the weight vectors and adding the biases b. If all biases are zero, the maximum net input a neuron can have is 0. This occurs when the input vector p equals that neuron's weight vector. The competitive transfer function accepts a net input vector for a layer and returns neuron outputs of 0 for all neurons except for the winner, the neuron associated with the most positive element of net input n1. The winner's output is 1. If all biases are 0, then the neuron whose weight vector is closest to the input vector has the least negative net input and, therefore, wins the competition to output a 1 [3]. 
\section[{c) Significance of Shelf Life}]{c) Significance of Shelf Life}\par
Shelf life is the recommendation of time that products can be stored, during which the defined quality of a specified proportion of the goods remains acceptable under expected (or specified) conditions of distribution, storage and display. Most shelf life labels or listed expiry dates are used as guidelines based on normal handling of products. Use prior to the expiration date guarantees the safety of a food product, and a product is dangerous and ineffective after the expiration date. For some foods, the shelf life is an important factor to health. Bacterial contaminants are ubiquitous, and foods left unused too long will often acquire substantial amounts of bacterial colonies and become dangerous to eat, leading to food poisoning  {\ref [3]}. Shelf life can be estimated by sensory evaluation, but it is expensive, very time consuming and does not fit well with the dairy factories manufacturing it. Sensory analyses may not reflect the full quality spectra of the product. Moreover, traditional methods for shelf life dating and small scale distribution chain tests cannot reproduce in a laboratory the real conditions of storage, distribution, and consumption on food quality. In the present era, food researchers are facing the challenges to monitor, diagnose, and control the quality and safety of food products. The consumer demands foods, under the legal standards, at low cost, high standards of nutritional, sensory, and health benefits. Goyal and Goyal \hyperref[b2]{[4]} developed artificial neural engineering and regressions models for forecasting shelf life of instant coffee drink. They developed radial basis and multiple II. 
\section[{MATERIAL AND METHODS}]{MATERIAL AND METHODS} 
\section[{a) Dataset}]{a) Dataset}\par
The experimental data on quality parameters, viz., moisture, titratable acidity, free fatty acids, tyrosine,  \hyperref[b3]{[5]} applied linear layer (design) and time -delay methods of intelligent computing expert system for shelf life prediction of soft mouth melting milk cakes. Neuron based artificial intelligent scientific computer engineering models for estimating shelf life of instant coffee sterilized drink were implemented by Goyal and Goyal \hyperref[b4]{[6]}. Till now no SNN models have been developed for predicting shelf life of soft cakes and this system would be very much beneficial and relevant for food researchers, consumers and store owners. The purpose of this study is to develop simulated neural network models that would predict the shelf life of soft cakes stored at 10 o C easily, at low cost and in less time.\par
linear regression models to study their prediction containing 48 observations (80\% of total observations) and validation set consisting of 10 observations (20\% of total observations). 
\section[{b) Experiments c) Measures for Prediction performance}]{b) Experiments c) Measures for Prediction performance}? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? 2 1 exp N cal n Q Q MSE (1) ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? 2 1 exp exp 1 N cal Q Q Q n RMSE (2) ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? 2 1 2 exp exp 2 1 N cal Q Q Q R (3) ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? = ? 2 1 exp exp exp 2 1 N cal Q Q Q Q E (4)\par
Where, (3) and E (4) were used in order to compare the prediction potential of the developed SNN models.  The regression equations were developed to predict shelf life of soft cakes, i.e., in days for which product has been in the shelf, based on overall acceptability score. The soft cakes were stored at 10 o C taking storage intervals (in days) as dependent variable and overall acceptability score as independent variable. R 2 was found to be 0.99 percent of the total variation as explained by overall acceptability scores. Time period (in days) for which the product has been in the shelf can be predicted based on overall acceptability score for soft cakes stored at 10 o C. (Fig. \hyperref[fig_3]{6}). The shelf life is calculated by subtracting the obtained value of days from experimentally determined shelf life, which was found to be 20.57 days. The predicted value is within the experimentally obtained shelf life of 21 days, hence the product is acceptable. 
\section[{RESULTS AND DISCUSSION}]{RESULTS AND DISCUSSION}\par
IV. 
\section[{CONCLUSION}]{CONCLUSION}\par
In the present era of high competition and marketing, for food manufactures, it is important that food products retain high nutritious quality before reaching to the consumer. Hence, keeping this in mind, simulated neural network based models were developed for predicting shelf life of soft cakes stored at 10 o C. The shelf life predicted by simulated neural networks was 20.57 days, whereas experimental shelf life was 21 days. Therefore, it is evident from the study that simulated neural networks can be used to predict shelf life of soft cakes.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}©}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}expQ=}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{34}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 3 :Fig. 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.2671428571428571\textwidth}P{0.5828571428571429\textwidth}}
Sumit Goyal\tabcellsep ? ,Gyanendra Kumar Goyal\end{longtable} \par
  {\small\itshape [Note: ? a) Elman Simulated Neural Network (ESNN)]} 
\caption{\label{tab_0}}\end{figure}
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\noindent\textbf{1} \par 
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23\tabcellsep 0.0094\tabcellsep 0.09716\tabcellsep 0.88671\tabcellsep 0.99055\\
25\tabcellsep 0.0121\tabcellsep 0.11016\tabcellsep 0.85436\tabcellsep 0.98786\\
28\tabcellsep 0.0071\tabcellsep 0.08448\tabcellsep 0.91435\tabcellsep 0.99286\\
30\tabcellsep 0.0033\tabcellsep 0.05752\tabcellsep 0.96028\tabcellsep 0.99669\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.40452310717797446\textwidth}P{0.2774827925270403\textwidth}P{0.005850540806293019\textwidth}P{0.08775811209439528\textwidth}P{0.07438544739429695\textwidth}}
(2), R 2\tabcellsep 2\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep 2011\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep August\\
\tabcellsep \tabcellsep 2\tabcellsep 2\\
Neurons 2:2 3:3 5:5 7:7 8:8 10:10 12:12 14:14 15:15 16:16 18:18 19:19 20:20 MSE\tabcellsep \multicolumn{2}{l}{MSE 0.0258 0.0167 0.0017 0.0012 0.0058 0.0007 0.0021 0.0002 0.0015 0.0025 0.0049 0.0029 0.0014 Table 3 : Performance of SOSNN RMSE R 0.1608 0.6893 0.1292 0.7995 0.0421 0.9786 0.0352 0.9850 0.0764 0.9298 0.0278 0.9906 0.0462 0.9743 0.0160 0.9969 0.0399 0.9808 0.0509 0.9688 0.0706 0.9401 0.0543 0.9645 0.0384 0.9822 RMSE R 2 2}\tabcellsep E 0.9741 0.9832 0.9982 0.9987 0.9941 0.9992 0.9978 0.9997 0.9984 0.9974 0.9950 0.9970 0.9985 E 2 2\tabcellsep Global Journal of Computer Science and Technology Volume XI Issue XIV Version I\\
0.0009\tabcellsep 0.0313\tabcellsep 0.9882\tabcellsep 0.9990\\
\multicolumn{4}{l}{ESSN and SOSNN models were developed for}\\
\multicolumn{4}{l}{predicting shelf life soft cakes stored at 10 o C. The best}\\
\multicolumn{4}{l}{results of ESSN with single hidden layer having twelve}\\
\multicolumn{4}{l}{neurons were (MSE: 0.000744207,RMSE:0.027280165,}\\
\multicolumn{4}{l}{R2 : 0.991069511, E2: 0.999255793) and with two}\\
\multicolumn{4}{l}{hidden layers having fourteen neurons in the first and}\\
\multicolumn{4}{l}{second layer were (MSE: 0.000257956,RMSE:}\\
\multicolumn{4}{l}{0.016061008, R2 : 0.996904528, E2: 0.999742044).}\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 			\footnote{© 2011 Global Journals Inc. (US)} 			\footnote{. http://en.wikipedia.org/wiki/Shelf\textunderscore life (accessed on 1.7.2011).} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Goyal et al. (2011)]{b2}\label{b2} 	 		‘Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink’.  		 			Goyal 		,  		 			Sumit 		,  		 			G K Goyal 		.  	 	 		\textit{International Journal of Computer Science Issues}  		2011. July 2011. 8  (4) .  	 
\bibitem[Goyal et al. ()]{b3}\label{b3} 	 		‘Development of Intelligent Computing Expert System Models for Shelf Life Prediction of Soft Mouth Melting Milk Cakes’.  		 			Goyal 		,  		 			Sumit 		,  		 			G K Goyal 		.  	 	 		\textit{International Journal of Computer Applications}  		2011. 2011.  	 
\bibitem[Goyal et al. ()]{b4}\label{b4} 	 		‘Development of neuron based artificial intelligent scientific computer engineering models for estimating shelf life of instant coffee sterilized drink’.  		 			Goyal 		,  		 			Sumit 		,  		 			G K Goyal 		.  	 	 		\textit{International Journal of Computational Intelligence and Information Security}  		2011. 2  (7) .  	 
\bibitem[Demuth and Hagan ()]{b0}\label{b0} 	 		\textit{Neural Network Toolbox User's Guide. The MathWorks},  		 			Beale H Demuth 		,  		 			Hagan 		.  		2009. Inc., Natrick, USA.  	 
\bibitem[Results (August)]{b1}\label{b1} 	 		\textit{The best results of all the models were compared with each other and it was observed that ESSN model with double hidden layer was better. The comparison of Actual Overall Acceptability Score (AOAS) and Predicted Overall Acceptability Score (POAS) for ESSN single and double hidden layer models with SOSNN model are illustrated in Fig},  		 			Results 		.  		 MSE: 0.031316629; RMSE: 0.031316629; R2 : 0.988231225; 0.999019269.  		August. 3.  	 
\end{bibitemlist}
 			 		 	 
\end{document}