Details

Autor(en) / Beteiligte

• Olalere, Olusegun Abayomi
• Gan, Chee‐Yuen
• Adedeji, Paul Adeola
• Olalere, Modupeola Elizabeth
• Aljbour, Nawzat

Titel

Multi‐objective Deng's grey incidence analysis, orthogonal optimization, and artificial neural network modelling in hot‐maceration‐assisted extraction of African cucumber leaves (Momordica balsamina)

Ist Teil von

• Canadian journal of chemical engineering, 2022-03, Vol.100 (3), p.588-597

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.