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Cited by 11Phytochemicals and Antioxidant ActivitiesAgricultural and Biological ResearchWood Residue Utilization and Solid Biofuels

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Extraction Optimization of Quercus cerris L. Wood Chips: A Comparative Study between Full Factorial Design (FFD) and Artificial Neural Network (ANN)

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Ponticelli, Maria, Carlucci, Vittorio, Mecca, Marisabel, Todaro, Luigi, Milella, Luigi, Russo, Daniela·2024·Antioxidants
DOI 10.3390/antiox13091115PMC PMC11428725
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Abstract

From a circular bio-economy perspective, biomass valorization requires the implementation of increasingly efficient extraction techniques to ensure the environmental and economic sustainability of biorefining processes. This research focuses on optimizing the specialized metabolite extraction of Turkey oak chips from Quercus cerris L. by applying a 3 levels Full Factorial Design (FFD). The goal is to obtain an extract with the highest antioxidant activity [evaluated by 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and ferric reducing antioxidant power (FRAP) assays] and specialized metabolites content [measured as total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and hydrolysable tannins content (THC)]. With this objective, three different variables were investigated and compared: temperature (20 °C, 50 °C, 80 °C), solvents EtOH/H2O (0%, 20%, 40%), and time (3 h, 6 h, 24 h), resulting in 27 different extracts. Following the FFD analysis, the optimal extractive conditions were determined to be 80 °C, 40% EtOH/H2O, and 19.8 h. Finally, the prediction ability of FFD was compared with that of artificial neural network (ANN) for DPPH scavenging activity, FRAP, and TPC data based on the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that ANN predictions were more precise than FFD ones; however, both methods were useful in optimizing the extraction process as they returned comparable optimized extraction parameters.

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Figure 1 (A) General architecture of the feed-forward backpropagation multilayer perceptron (MLP) neural network consisting of 3 neurons as the input layer, 10 as the hidden layer, and 3 as the output layer. (B) Training, validation, testing, and overall datasets’ correlation coefficients (R) for the developed ANN model.
Figure 1 (A) General architecture of the feed-forward backpropagation multilayer perceptron (MLP) neural network consisting of 3 neurons as the input layer, 10 as the hidden layer, and 3 as the output layer. (B) Training, validation, testing, and overall datasets’ correlation coefficients (R) for the developed ANN model.CC BY · PubMed Central
Figure 2 Residual analysis of the model.
Figure 2 Residual analysis of the model.CC BY · PubMed Central
Figure 3 (A) Effects of independent variables (time, temperature, and solvent) on antioxidant activity; surface and contour plot of (B) 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and (C) Ferric Reducing Antioxidant Power (FRAP).
Figure 3 (A) Effects of independent variables (time, temperature, and solvent) on antioxidant activity; surface and contour plot of (B) 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and (C) Ferric Reducing Antioxidant Power (FRAP).CC BY · PubMed Central
Figure 4 (A) Effects of independent variables (time, temperature, and solvent) on extraction of specialized metabolites. Surface and contour plot of (B) total phenolic content (TPC), (C) total flavonoid content (TFC), and (D) hydrolyzable tannin content (HTC).
Figure 4 (A) Effects of independent variables (time, temperature, and solvent) on extraction of specialized metabolites. Surface and contour plot of (B) total phenolic content (TPC), (C) total flavonoid content (TFC), and (D) hydrolyzable tannin content (HTC).CC BY · PubMed Central
Figure 5 Artificial neural network model validation. (A) Best validation performance plot. (B) Error histogram.
Figure 5 Artificial neural network model validation. (A) Best validation performance plot. (B) Error histogram.CC BY · PubMed Central
Figure 6 Performance comparison of ANN (in orange) and FFD (in blue) models for (A) DPPH: 2,2-diphenyl-1-picrylhydrazyl, expressed as milligrams of Trolox equivalents per gram of dry extract; (B) FRAP: ferric reducing antioxidant power, expressed as milligrams of Trolox equivalent per grams of dry extract; and (C) TPC: total phenolic content, expressed as milligrams of gallic acid per grams of dry extract.
Figure 6 Performance comparison of ANN (in orange) and FFD (in blue) models for (A) DPPH: 2,2-diphenyl-1-picrylhydrazyl, expressed as milligrams of Trolox equivalents per gram of dry extract; (B) FRAP: ferric reducing antioxidant power, expressed as milligrams of Trolox equivalent per grams of dry extract; and (C) TPC: total phenolic content, expressed as milligrams of gallic acid per grams of dry extract.CC BY · PubMed Central

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Metadata

Authors
Ponticelli, M., Carlucci, V., Mecca, M., Todaro, L., Milella, L., & Russo, D.
Affiliations
  • University of Basilicata (IT)
  • Centro di Riferimento Oncologico della Basilicata (IT)
Corresponding author
Milella, Luigi
ORCID
Venue
Antioxidants
Publisher
MDPI AG
Published
2024-09-14
Volume
13
Issue
9
Pages
1115
ISSN
2076-3921 (ISSN-L)
DOI
10.3390/antiox13091115
PMC
PMC11428725
OpenAlex
W4402820253
Cited by
11
References
0
Citations by year
2026: 4 · 2025: 5 · 2024: 1
FWCI
2.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Antioxidants (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutional Research Information System (University of Basilicata) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
Funders
  • Regione Basilicata (CUP: G49J19001350004, 527/2019)
  • DGR (CUP: G49J19001350004, 527/2019)
Topics
  • Health Sciences › Medicine › Biochemistry (medical) › Phytochemicals and Antioxidant Activities
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Agricultural and Biological Research
  • Life Sciences › Agricultural and Biological Sciences › Forestry › Wood Residue Utilization and Solid Biofuels
Keywords
Quercus cerris, extraction optimization, full factorial design, artificial neural networks, antioxidant activity, total phenolic content, total flavonoid content, proanthocyanidins, hydrolysable tannins, ethanol-water solvent, extraction temperature, extraction time, secondary metabolites, biomass valorization, biorefining

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