<i>Comparative study of ANN(Artificial Neural Network) versus RSM(Response Surface Methodology) for predicting the recovery of phenolic compounds from spent coffee grounds by conventional and microwave assisted extraction</i>

2018 ◽  
Author(s):  
Sravanthi Budaraju ◽  
ParameswaraKumar Mallikarjunan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Phenolic Compounds ◽  
Comparative Study ◽  
Microwave Assisted Extraction ◽  
Spent Coffee Grounds ◽  
Microwave Assisted ◽  
Assisted Extraction ◽  
Coffee Grounds

scholarly journals Microwave-Assisted Extraction of Phenolic Compounds from Spent Coffee Grounds. Process Optimization Applying Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7320
Author(s):  
José P. Coelho ◽  
Maria P. Robalo ◽  
Stanislava Boyadzhieva ◽  
Roumiana P. Stateva
Keyword(s):  
Phenolic Compounds ◽  
Design Of Experiments ◽  
Process Optimization ◽  
Dominant Factor ◽  
Assay Method ◽  
Microwave Assisted Extraction ◽  
Spent Coffee Grounds ◽  
Microwave Assisted ◽  
Assisted Extraction ◽  
Coffee Grounds

In this study, sustainable technology microwave-assisted extraction (MAE) in association with green solvents was applied to recover phenolic compounds from spent coffee grounds (SCGs). A design of experiments (DOE) was used for process optimization. Initially, a 24−1 two level Fractional Factorial Design was used and ratios “solvent to solute” and “ethanol to water” were identified as the significant experimental factors. Consequently, Central Composite Design (CCD) was applied to analyze the effects of the significant variables on the response yield, total polyphenols content (TPC), and antioxidant activity (AA) by the DPPH assay method, and quadratic surfaces to optimize those responses were generated. The values of the significant factors of 16.7 (solvent/solute) and 68.9% (ethanol/water) were optimized simultaneously the yield (%) at 6.98 ± 0.27, TPC (mg GAE/g) at 117.7 ± 6.1, and AA (µmol TE/g) at 143.8 ± 8.6 and were in excellent agreement with those predicted from the CCD model. The variations of the compositions of the lipids, caffeine, pentacyclic diterpenes, and FAME as a function of the dominant factor % ethanol in the solvent mixture were analyzed by applying NMR and GC-FID, and the results obtained confirmed their determinative significance.

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