Multi-objective Deng’s grey incidence analysis, orthogonal optimization, and ANN

ABSTRACTS

Due to the inherent multiple response characteristics in many biological and separation processes, parameter optimization and modelling is usually a daunting task. The integration of Deng’s grey incidence model (GRA) and Taguchi optimization (TM) therefore helps in transforming multiple quality characteristics into a single response presented as the grey relational grade (GRG). This was applied to optimize the multiple quality response characteristics in the maceration-assisted extraction of African cucumber leaves. Two responses and five design factors were selected with L16(25 ) layout using signal-to-noise ratio as a point prediction feature. Under the optimized conditions, the optimum total phenolic content and antioxidant capacity of 0.8569 mg/ml gallic acid equivalence and 0.9259 mg/ml were achieved, respectively. The mass ratio was the highest contributor (38.2%), whereas the maceration time presented the least contribution (9.8%) to the cumulative response grade (GRG). In the neural network analysis, three models were deployed: Levenberg Marquardt backpropagation neural network (LMNN), gradient descent with adaptive learning rate neural network (GDALRNN), and the resilient back-propagation neural network (RPNN). A better prediction of hold-out data was achieved with the GDALRNN model, generating lesser absolute deviation error (MADGDALRNN = 0.099), root mean square error (RMSEGDALRNN = 0.1033), relative mean bias error (rMBEGDALRNN =
0.24), and highest computational time (CTGDALRNN = 8.8), which is expected of an effective model. Based on the GRG and the signal-to-noise ratio, the optimum conditions and the neural network model succinctly provided a benchmark for future assessment of complex relationship among extraction variables, which could form the basis for a potential future scale-up applications.

“The integration of Deng’s grey incidence model (GRA) and Taguchi optimization (TM) therefore helps in transforming multiple quality characteristics into a single response presented as the grey relational grade (GRG).”

CONCLUSION

In this study, the effects of maceration

-assisted extraction parameters on the total phenolic content and antioxidant activities were carefully investigated. The extraction parameters such as maceration time, temperature, solvent concentration, agitation, and mass ratio were considered and their optimum conditions were achieved using the GRA integrated with the L16 (25 ) orthogonal design. Also, an ANN was used for the predictive modelling of the extraction parameters using three algorithms: the LMNN, GDALRNN, and RPNN. The integration of GRA, TM, and GDALRNN is an effective method to maximally predict the multiple response

s for different variations of experimental parameters in the solvent extraction process. The optimization and neural modelling are vital tools that can be applied in a possible large scaling of maceration extraction.

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