
Research Article


Investigation of Capacity Behaviors by Linear and Nonlinear Models Chemometrics 

H. Noorizadeh
and
A. Farmany



ABSTRACT

Genetic Algorithm and Multiple Linear Regression (GAMLR) and LevenbergMarquardt Artificial Neural Network (LM ANN) techniques were used to investigate the correlation between Trolox Equivalent Antioxidant Capacity (TEAC) and descriptors for 16 derivative hydroxy compounds. The applied internal validation method was used for the predictive power of four models. The square correlation coefficient between experimental and predicted TEAC for these data by GAMLR and were 0.824 and 0.966, respectively. This is the first research on the QSAR of the antioxidant compounds against the TEAC using the LM ANN.





Received: April 15, 2011;
Accepted: August 17, 2011;
Published: September 28, 2011


INTRODUCTION
The hydroxyl groups attaching to aromatic ring generate a series of compounds
that can scavenge radicals by trapping initiating and/or propagating radicals,
thus, called antioxidant which attracts more scientific attention by medicinal
chemists because the research in this field provides theoretical information
for the medicinal development and supplies some in vitro methods for
quickoptimizing drugs. Phenolic compounds are widely distributed among vascular
plants and are found in numerous fruits, grains, vegetables and other parts
of higher plants (Francisco et al., 2009; Luthria,
2008).
As a specific group of secondary metabolites, Phenolic antioxidants play a
key role in protecting of organisms against harmful effects of oxygen radicals
and other highly active oxygen species (Francisco et
al., 2009; Luthria, 2008). Their formation in
human body is closely connected with the development of a wide range of degenerative
and nondegenerative diseases, mainly arteriosclerosis and other associated complications,
cancer, indispositions and last but not least with the accelerated aging of
organisms (Huang et al., 2007). Some preventive
and defensive systems against the attack of the reactive substances exist in
the human organism; however, they cannot eliminate harmful activities of such
substances completely, particularly when their production is increased in some
metabolic, physiologic, pathologic and other situations. An adequate intake
of natural antioxidants in food is therefore, of great importance for protection
of macromolecules against oxidative damage (Oke and Aslim,
2011) in cells (mainly unsaturated fatty acids in lipids, cholesterol, different
functional polypeptides and proteins and nucleic acids). Antioxidant activity
plays, in many cases, a basic role in their pharmacological effects; thus, it
can be considered the most important (Meng et al.,
2007; Hsu et al., 2006). However, the detailed
mechanism underlying the effect of additional hydroxyl moieties on the antioxidant
potential has not been studied in any detail.
At this time, various methods are employed for the analysis of antioxidant
activity of polyphenols, such as the TEAC (Trolox equivalent antioxidant capacity),
DPPH (using diphenylppicrylhydrazyl radical) and FRAP (ferric reducing antioxidant
power) methods. A commonly used method in the determination of free radical
scavenging activity against the active oxygen species is the determination of
the ability of hydrogendonating antioxidants to scavenge the 2, 20azinobis
(3ethylbenzthiazoline6sulfonic acid) radical cation (ABTS°^{+})
which is expressed in Trolox (a watersoluble vitamin E analog) Equivalent Antioxidant
Capacity (TEAC). TEAC is defined as the millimolar concentration of Trolox with
the same antioxidant activity as a 1 mM concentration of the substance under
investigation. To calculate the TEAC, the gradient of the plot of the percentage
inhibition of absorbance vs. concentration plot for the antioxidant in question
is divided by the gradient of the plot for Trolox (Obon
et al., 2005; Zulueta et al., 2009).
The antioxidant activity of polyphenols can largely be predicted on the basis of their chemical structure. Quantitative Structureactivity Relationship (QSAR) studies have received much attention in chemometrics, biological chemistry, medicinal chemistry and many other fields. QSAR models are mathematical equations relating chemical structure to their biological activity.
In spite of this, only a limited number of studies report on QSAR of benzoic
acid focusing on the radical scavenging activity of flavonoid antioxidants (Tyrakowska
et al., 1999; Rastija and MedicSaric, 2009).
The QSAR models apply to Multiple Linear Regression (MLR) method and for feature
selection, often combined with Genetic Algorithms (GA). Because of the complexity
of relationships between the property of molecules and structures, nonlinear
models are also used to model the structure activity relationships. LevenbergMarquardt
Artificial Neural Network (LM ANN) is nonparametric nonlinear modeling technique
that has attracted increasing interest (Edriss et al.,
2008; Venkatachalam et al., 2008; ElRamsisi
and Khalil, 2007; Terman and Khalafi, 2006; Furferi
and Carfagni, 2010; Ozgan and Demirci, 2008; Ghaemi
et al., 2008; Prasad et al., 2010).
In the present study, GAMLR and LM ANN were employed to generate QSAR models
that correlate the structure of some compound with observed TEAC. The present
study is a first research on QSAR of the antioxidant compounds against the TEAC,
using LM ANN.
MATERIALS AND METHODS
Data set: The data set studied in this work consists of 16 hydroxy benzoic
acid compounds which the antioxidant activities were reported as TEAC values
were taken from literature (Tyrakowska et al., 1999;
Dall’Acqua et al., 2008; Apak
et al., 2004; Tung et al., 2009).
The structure of these compounds is given in Table 1.
Descriptor generation: The derivation of theoretical molecular descriptors
proceeds from the chemical structure of the compounds. In order to calculate
the theoretical descriptors, all molecular structures were constructed with
HyperChem software (version 6). Optimization of molecular structures was carried
out by semiempirical AM1 method using the FletcherReeves algorithm until the
room mean square gradient of 0.01 was obtained by Hemmateenejad
et al. (2007). Since the calculated values of the electronic features
of molecules will be influenced by the related conformation. In the current
research an attempt was made to use the most stable conformations.
Table 1: 
Molecular structure of benzoic compounds 


Some electronic descriptors such as dipole moment and orbital energy of LUMO
were calculated by using the HyperChem software. Also, optimized structures
were used to calculate 1497 descriptors by DRAGON software Version 3 (Todeschini
et al., 2003).
Software and programs: A Pentium IV personal computer (CPU at 3.06 GHz) with windows XP operational system was used. Geometry Optimization was performed by HyperChem (Version 7.0 Hypercube, Inc.), Dragon software was used to calculate of TEAC. MLR analysis was performed by the SPSS Software (version 13, SPSS, Inc.) by using enter method for model building. Cross validation, GAMLR, LM ANN and other calculation were performed in the MATLAB (Version 7, Mathworks, Inc.) environment.
Genetic algorithm: To select the most relevant descriptors with GA,
the evolution of the population was simulated. Each individual of the population,
defined by a chromosome of binary values, represented a subset of descriptors.
The number of the genes at each chromosome was equal to the number of the descriptors.
The population of the first generation was selected randomly. A gene was given
the value of one, if its corresponding descriptor was included in the subset;
otherwise, it was given the value of zero. The number of the genes with the
value of one was kept relatively low to have a small subset of descriptors that
is the probability of generating zero for a gene was set greater. The operators
used here were crossover and mutation. The application probability of these
operators was varied linearly with a generation renewal. For a typical run,
the evolution of the generation was stopped, when 90% of the generations had
taken the same fitness (Noorizadeh and Farmany, 2010a;
Cai et al., 2001; Goldberg,
2000). In this study, size of the population is 30 chromosomes, the probability
of initial variable selection is 5:V (V is the number of independent variables),
crossover is multi Point, the probability of crossover is 0.5, mutation is multi
point, the probability of mutation is 0.01 and the number of evolution generations
is 1000. For each set of data, 3000 runs were performed.
Linear model
Multiple linear regressions: A major step in constructing the QSAR model
is finding a set of molecular descriptors that represent variation in the structural
property of the molecules. The modeling and prediction of the physicochemical
properties of organic compounds is an important objective in many scientific
fields (Noorizadeh and Farmany, 2010b; Citra,
1999). MLR is one of the most modeling methods in QSAR.
Nonlinear model
Artificial neural network: An Artificial Neural Network (ANN) with a layered
structure is a mathematical system that stimulates the biological neural network;
consist of computing units named neurons and connections between neurons named
synapses (Booth et al., 1997; Noorizadeh
and Farmany, 2011). Input or independent variables are considered as neurons
of input layer while dependent or output variables are considered as output
neurons. Synapses connect input neurons to hidden neurons and hidden neurons
to output neurons. The strength of the synapse from neuron i to neuron j is
determined by mean of a weight, Wij. In addition, each neuron j from the hidden
layer and eventually the output neuron, are associated with a real value b_{j},
named the neuron’s bias and with a nonlinear function, named the transfer
or activation function. Because the Artificial Neural Networks (ANNs) are not
restricted to linear correlations, they can be used for nonlinear phenomena
or curved manifolds (Booth et al., 1997). Back
Propagation Neural Networks (BNNs) are most often used in analytical applications
(Noorizadeh and Farmany, 2011). The back propagation network
receives a set of inputs which is multiplied by each node and then a nonlinear
transfer function is applied. The goal of training the network is to change
the weight between the layers in a direction to minimize the output errors.
Levenbergmarquardt algorithm: While basic back propagation is the steepest
descent algorithm, the Levenbergmarquardt algorithm (Noorizadeh
et al., 2011; Salvi et al., 2002)
is an alternative to the conjugate methods for second derivative optimization.
RESULTS AND DISCUSSION
Linear model
GAMLR analysis: To reduce the original pool of descriptors to an
appropriate size, the objective descriptor reduction was performed using various
criteria.
Table 2: 
Experimental, calculated and relative error values by GAMLR
and LM ANN 

Table 3: 
The statistical parameters of different constructed QSRR models 

^{a}Absolute error 
Reducing the pool of descriptors eliminates those descriptors which contribute
either no information or whose information content is redundant with other descriptors
present in the pool. From the variable pairs with r>0.90, only one of them
was used in the modeling while the variables over 90% and equal to zero or identical
were eliminated. In order to minimize the information overlap in descriptors
and to reduce the number of descriptors required in regression equation, the
concept of nonredundant descriptors was used in this study. The best equation
is selected on the basis of the highest multiple correlation Coefficient Leaveoneout
Cross Validation (LOOCV) (Q2), the least RMSECV and relative error of prediction
and simplicity of the model. These parameters are probably the most popular
measure of how well a regression model fits the data. Among the models proposed
by GAMLR, one model had the highest statistical quality and was repeated more
than the others. This model had four molecular descriptors including RDF descriptors
((Radial Distribution Function 3.5/weighted by atomic masses) (RDF 035 m),
WHIM descriptors (2nd component accessibility directional WHIM index/unweighted)
(E 2u), atomcentered fragments (phenol/enol/carboxyl OH) (O057) and electronic
descriptor (Lowest Unoccupied Molecular Orbital (LUMO)). The experimental, calculated
and relative errors are shown in Table 2 and statistical parameters
of this model, constructed by the selected descriptors, are depicted in Table
3. The predicted values of TEAC are plotted against the experimental values
in Fig. 1a.
Description of models descriptors: The antioxidant efficiency of hydroxyl
benzoic acid has been related to the number of hydroxyl groups in the molecule
and also to their hydrogen radical donating abilities. The hydrogen donating
substituents (hydroxyl groups), attached to the aromatic ring structures of
hydroxyl benzoic acid which enable the flavonoids to undergo a redox reaction
that helps them to scavenge free radicals more easily.

Fig. 1(ab): 
Plots of predicted TEAC against the experimental values by
(a) GAMLR and (b) LM ANN models 
The compounds with three hydroxyl groups on the phenyl ring of phenolic acids
and hydroxy benzoic acid had the highest antioxidant activities. The loss of
one hydroxyl group decreased activity slightly while the loss of two hydroxyl
groups decreased activity significantly. Similarly also showed that an increase
in the number of hydroxyl groups led to higher antioxidant activities. For these
reasons, constitutional descriptors, atomcentered fragments, functional groups
and electronic descriptors are very important.
Electronic descriptors were defined in terms of atomic charges and used to describe electronic aspects both of the whole molecule and of particular regions, such atoms, bonds and molecular fragments. This descriptor calculated by computational chemistry and therefore, can be consider among quantum chemical descriptor. LUMO as an electron acceptor represents the ability to obtain an electron. The energy of the LUMO is directly related to the electron affinity and characterizes the susceptibility of the molecule toward attack by nucleophiles. The LUMO energy can be interpreted as a measure of charge transfer interactions and/or of hydrogen bonding effects. The Radial Distribution Function (RDF) descriptors are based on the distances distribution in the geometrical representation of a molecule and constitute a radial distribution function code. These structural codes are independent from the number of atoms that is, the size of a molecule and invariant against translation and rotation of the entire molecule.
The WHIM descriptors are built in such a way as to capture the relevant molecular
3D information regarding the molecular size, shape, symmetry and atom distribution
with respect to some invariant reference frame. WHIM descriptors are based on
principal component analysis of the weighted covariance matrix obtained from
the atomic cartesian coordinates. In relation to the kind of weights selected
for the atoms different sets of WHIM descriptors can be obtained (Todeschini
and Consonni, 2000).
Nonlinear model
LM ANN analysis: With the aim of improving the predictive performance
of nonlinear QSRR model, LM ANN modeling was performed. Descriptors of GAMLR
model were selected as inputs in LM ANN model. The network architecture consisted
of four neurons in the input layer corresponding to the four mentioned descriptors.
The output layer had one neuron that predicts the TEAC. The number of neurons
in the hidden layer is unknown and needs to be optimized. In addition to the
number of neurons in the hidden layer, the learning rate, the momentum and the
number of iterations also should be optimized. In this study, the number of
neurons in the hidden layer and other parameters except the number of iterations
were simultaneously optimized. A MATLAB program was written to change the number
of neurons in the hidden layer from 2 to 7, the learning rate from 0.001 to
0.1 with a step of 0.001 and the momentum from 0.1 to 0.99 with a step of 0.01.
The root mean square errors were calculated for all of the possible combination
of values for the mentioned variables in cross validation. The experimental,
calculated and relative errors of this model are shown in Table
2. The statistical parameters for LM ANN model in Table 3.
Plots of predicted TEAC versus experimental TEAC values by LM ANN are shown
in Fig. 1b. Obviously, there is a close agreement between
the experimental and predicted TEAC and the data represent a very low scattering
around a straight line with respective slope and intercept close to one and
zero. The closeness of the data to the straight line with a slope equal to 1
shows the perfect fit of the data to a nonlinear model. It should be noted that
the data shown in Fig. 1b are the predicted values according
to leaveoneout cross validation and a deviation from the regression line is
expected for some points.
These models were validated by calculating q^{2} values. The q^{2}
values are calculated from Leavegroupout Cross Validation (LGOCV) for LM
ANN (Noorizadeh et al., 2011). In this study,
we use LOO CV for GAMLR model and LGO CV for LM ANN model. A data point is
removed from the set and the regression recalculated; the predicted value for
that point is then compared to its actual value. This is repeated until each
datum has been omitted once; the RMSE and sum of squares of these deletion residuals
can then be used to calculate q^{2}, an equivalent statistic to R^{2}.
The q^{2} values can be considered a measure of the predictive power
of a regression equation: Whereas R^{2} can always be increased artificially
by adding more parameters (descriptors), q^{2} decreases if a model
is over parameterized and therefore, it is a more meaningful summary statistic
for QSAR models.
The statistical parameters obtained by LOOCV for LM ANN and the linear QSRR model are compared in Table 3. Inspections of the results of the table reveals a higher R^{2} and Q^{2} values and lower the RE for LM ANN model compared with their counterparts for GAMLR model. This clearly shows the strength of LM ANN as a nonlinear feature selection method. This capacity offset the large computing time required and complexity of LM ANN model with respect GAMLR model. Model validation and statistical parameters: The applied internal (Leavegroupout Cross Validation (LGOCV)) and external (test set) validation methods were used for the predictive power of models. In the leavegroupout procedure one compound was removed from the data set, the model was trained with the remaining compounds and used to predict the discarded compound. The process was repeated for each compound in the data set. The predictive power of the models developed on the selected training set is estimated on the predicted values of test set chemicals. The data set should be divided into two new subdata sets, one for training and the other one for testing. For the constructed models, some general statistical parameters were selected to evaluate the predictive ability of the models for TEAC values. In this case, the predicted TEAC of each sample in prediction step was compared with the experimental acidity constant. Root Mean Square Error (RMSE) is a measurement of the average difference between predicted and experimental values, at the prediction step. RMSE can be interpreted as the average prediction error, expressed in the same units as the original response values. The RMSE was obtained by the following formula: The second statistical parameter was Relative Error (RE) that shows the predictive ability of each component and is calculated as: The predictive ability was evaluated by the square of the correlation coefficient cross validation (Q^{2 }or R^{2}_{LGO}) which is based on the prediction error sum of squares and was calculated by following equation:
where, y_{i} is the experimental k' in the sample I,
represented the predicted k' in the sample I,
is the mean of experimental k' in the prediction set and n is the total number
of samples used in the test set.
CONCLUSION The increasing interest in antioxidant is due to the appreciation of their broad pharmacological activity. Beneficial effects of antioxidant have been described for diabetes mellitus, allergy, cancer, viral infections and inflammations. They can bind to biomolecules, such as enzymes, hormone carriers and DNA, chelate transition metal ions, catalyze electron transport and scavenge free radicals and including superoxide anions. Benzoic acids and vitamin E derivatives and flavonoids have recently gained significant interest among various antioxidants. In this study, an accurate QSAR model for estimating the Trolox Equivalent Antioxidant Capacity (TEAC) of derivative hydroxy benzoic acid compounds was developed by employing the linear model (GAMLR) and a nonlinear model (LM ANN). Two models have good predictive capacity and excellent statistical parameters. A comparison between these models revealed the superiority of the LM ANN model. It is easy to notice that there was a good prospect for the LM ANN application in the QSAR modeling. This indicates that the TEAC of benzoic acid possesses some nonlinear characteristics. It can also be used successfully to estimate the TEAC for new compounds or for other compounds whose experimental values are unknown.

REFERENCES 
Apak, R., K. Guclu, M. Ozyurek and S.E. Karademir, 2004. Novel total antioxidant capacity index for dietary polyphenols and vitamins C and E, using their cupric ion reducing capability in the presence of neocuproine: CUPRAC method. J. Agric. Food Chem., 52: 79707981. CrossRef  Direct Link 
Booth, T.D., K. Azzaoui and I.W. Wainer, 1997. Prediction of chiral chromatographic separations using combined multivariate regression and neural networks. Anal. Chem., 69: 38793883. CrossRef 
Cai, W., B. Xia, X. Shao, Q. Guo, B. Maigret and Z. Pan, 2001. Molecular interactions of cyclodextrin inclusion complexes using a genetic algorithm. J. Mol. Struct. Theochem, 535: 115119. CrossRef 
Citra, M.J., 1999. Estimating the pKa of phenols, carboxylic acids and alcohols from semiempirical quantum chemical methods. Chemosphere, 38: 191206. CrossRef 
Dall'Acqua, S., R. Cervellati, M.C. Loi and G. Innocenti, 2008. Evaluation of in vitro antioxidant properties of some traditional sardinian medicinal plants: Investigation of the high antioxidant capacity of Rubus ulmifolius. Food Chem., 106: 745749. CrossRef 
Francisco, M., D.A. Moreno, M.E. Cartea, F. Ferreres, C. GarciaViguera and P. Velasco, 2009. Simultaneous identification of glucosinolates and phenolic compounds in a representative collection of vegetable Brassica rapa. J. Chromatogr. A, 1216: 66116619. PubMed 
Goldberg, D.E., 2000. Genetic Algorithms in Search, Optimization and Machine Learning. AddisonWesleyLongman, Reading, MA, USA
Hemmateenejad, B., K. Javadnia and M. Elyasi, 2007. Quantitative structureretention relationship for the Kovats retention indices of a large set of terpenes: A combined data splittingfeature selection strategy. Anal. Chim. Acta, 592: 7281. PubMed 
Huang, Z., B. Wang, D.H. Eaves, J.M. Shikany and R.D. Pace, 2007. Phenolic compound profile of selected vegetables frequently consumed by African Americans in the Southeast United States. Food Chem., 103: 13951402. CrossRef 
Hsu, B., I.M. Coupar and K. Ng, 2006. Antioxidant activity of hot water extract from the fruit of the Doum palm, Hyphaene thebaica. Food Chem., 98: 317328. CrossRef 
Luthria, D.L., 2008. Influence of experimental conditions on the extraction of phenolic compounds from parsley ( Petroselinium crispum) flakes using a pressurized liquid extractor. Food Chem., 107: 745752. CrossRef 
Meng, X., Z. Xin, Y. Li and Z. Cai, 2007. Study on the hydrogen donating abilities of benzofuranones as chainbreaking antioxidants using laser flash photolysis technique. Polym. Degrad. Stabil., 92: 184188.
Noorizadeh, H. and A. Farmany, 2010. Exploration of linear and nonlinear modeling techniques to predict of retention index of essential oils. J. Chin. Chem. Soc., 57: 12681277. Direct Link 
Noorizadeh, H. and A. Farmany, 2010. QSRR models to predict retention indices of cyclic compounds of essential oils. Chromatographia, 72: 563569. CrossRef 
Noorizadeh, H., A. Farmany and M. Noorizadeh, 2011. Quantitative structureretention relationships analysis of retention index of essential oils. Quim. Nova, 34: 242249. CrossRef 
Noorizadeh, H. and A. Farmany, 2011. Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods. Drug Test. Analy., (In Press), CrossRef 
Oke, F. and B. Aslim, 2011. Protective effect of two edible mushrooms against oxidative cell damage and their phenolic composition. Food Chem., 128: 613619.
Obon, J.M., M.R. Castellar, J.A. Cascales and J.A. FernandezLopez, 2005. Assessment of the TEAC method for determining the antioxidant capacity of synthetic red food colorants. Food Res. Int., 38: 843845. CrossRef 
Rastija, V. and M. MedicSaric, 2009. QSAR study of antioxidant activity of wine polyphenols. Eur. J. Med. Chem., 44: 400408. CrossRef 
Salvi, M., D. Dazzi, I. Pellistri, I. Neri and J.R. Wall, 2002. Classification and prediction of the progression of thyroidassociated ophthalmopathy by an artificial neural network. Ophthalmology, 109: 17031708. Direct Link 
Todeschini, R. and V. Consonni, 2000. Handbook of Molecular Descriptors. 1st Edn., WileyVCH, Weinheim
Todeschini, R., V. Consonni, A. Mauri and M. Pavan, 2003. DRAGONSoftware for the calculation of molecular descriptors. Version 3.0 for Windows.
Tung, Y.T., J.H. Wu, C.Y. Huang, Y.H. Ku and S.T. Chang, 2009. Antioxidant activities and phytochemical characteristics of extracts from Acacia confusa bark. Bioresour. Technol., 100: 509514. CrossRef  Direct Link 
Tyrakowska, B., A.E. Soffers, H. Szymusiak, S. Boeren and M.G. Boersma et al., 1999. TEAC Antioxidant Activity of 4Hydroxybenzoates. Free Radical Biol. Med., 27: 14271436. CrossRef 
Zulueta, A., M.J. Esteve and A. Frigola, 2009. ORAC and TEAC assays comparison to measure the antioxidant capacity of food products. Food Chem., 114: 310316. CrossRef  Direct Link 
Edriss, M.A., P. Hosseinnia, M. Edrisi, H.R. Rahmani and M.A. Nilforooshan, 2008. Prediction of second parity milk performance of dairy cows from first parity information using artificial neural network and multiple linear regression methods. Asian J. Anim. Vet. Adv., 3: 222229. CrossRef  Direct Link 
Venkatachalam, S., C. Arumugam, K. Raja and V. Selladurai, 2008. Quality function deployment in agile parallel machine scheduling through neural network technique. Asian J. Sci. Res., 1: 146152. CrossRef  Direct Link 
ElRamsisi, A.M. and H.A. Khalil, 2007. Diagnosis system based on wavelet transform, fractal dimension and neural network. J. Applied Sci., 7: 39713976. CrossRef  Direct Link 
Terman, M.S. and H. Khalafi, 2006. Analysis of Tehran research reactor dynamics behavior in reactivity insertion accidents by recurrent neural network. Trends Applied Sci. Res., 1: 586596. CrossRef  Direct Link 
Furferi, R. and M. Carfagni, 2010. An asshortaspossible mathematical assessment of spectrophotometric color matching. J. Applied Sci., 10: 21082114. CrossRef  Direct Link 
Ozgan, E. and R. Demirci, 2008. Neural networks based modelling of traffic accidents in interurban rural highways, duzce sampling. J. Applied Sci., 8: 146151. CrossRef  Direct Link 
Ghaemi, A., Sh. Shahhoseini, M.G. Marageh and M. Farrokhi, 2008. Prediction of vaporliquid equilibrium for aqueous solutions of electrolytes using artificial neural networks. J. Applied Sci., 8: 615621. CrossRef  Direct Link 
Prasad, R., D. Krishnaiah, A. Bono, P. Pandiyan, R.B.M. Yunus and N. Lakshmi, 2010. Estimation of carrageenan concentration by using ultra sonic waves and back propagation neural networks. J. Applied Sci., 10: 27292732. CrossRef  Direct Link 



