Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

Youssefi, Sh.; Emam‐Djomeh, Zahra; Mousavi, Mohammad

doi:10.1080/07373930902988247

Cited by 144 publications

(72 citation statements)

References 34 publications

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“…Results from this study showed that the estimation capability of ANN for optimum levels of variables for lipase production by recombinant E. coli was more accurate than RSM. Similar observations have also been reported [8,28,29]. Both methods (RSM and ANN) estimated low glucose concentration as optimal levels.…”

Section: Methodssupporting

confidence: 74%

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Nelofer

Ramanan

Rahman

et al. 2012

Journal of Industrial Microbiology and Biotechnology

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Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R² and adjusted R² values for the model. Although the R (2) value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R², adjusted R², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.

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Section: Methodssupporting

confidence: 74%

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Nelofer

Ramanan

Rahman

et al. 2012

Journal of Industrial Microbiology and Biotechnology

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“…Youssefi et al [38] compared ANN and Response Surface Methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice. RSM is a frequently used method for empirical modeling and prediction in the processing of biological media.…”

Section: Aa Pomegranatementioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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“…Response surface polynomials are local approximations of the real input-and-output relationships and can thus be used as an interesting alternative for conventional models, such as numerical simulations during optimization [14].…”

mentioning

confidence: 99%

Modeling and stability of polyphenol in spray-dried and freeze-dried fruit encapsulates

2015

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Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

Cited by 144 publications

References 34 publications

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Artificial Neural Networks in Fruits: A Comprehensive Review

Modeling and stability of polyphenol in spray-dried and freeze-dried fruit encapsulates

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