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Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model
Crude extracts from cashew apple pomace (CAP) dried at different temperatures were used in High-Pressure Liquid Chromatography to quantify total alkaloids content (TAC), total flavanoids content (TFC), total saponin content (TSC) and total phenolics content (TPC). Diphenyl-1-picrylhydrazyl (DPPH) wa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479022/ https://www.ncbi.nlm.nih.gov/pubmed/36119886 http://dx.doi.org/10.1016/j.heliyon.2022.e10461 |
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author | Luka, Bobby Shekarau Yuguda, Taitiya Kenneth Adnouni, Meriem Zakka, Riyang Abdulhamid, Ibrahim Bako Gargea, Bumbyerga Garboa |
author_facet | Luka, Bobby Shekarau Yuguda, Taitiya Kenneth Adnouni, Meriem Zakka, Riyang Abdulhamid, Ibrahim Bako Gargea, Bumbyerga Garboa |
author_sort | Luka, Bobby Shekarau |
collection | PubMed |
description | Crude extracts from cashew apple pomace (CAP) dried at different temperatures were used in High-Pressure Liquid Chromatography to quantify total alkaloids content (TAC), total flavanoids content (TFC), total saponin content (TSC) and total phenolics content (TPC). Diphenyl-1-picrylhydrazyl (DPPH) was used to determine the antioxidant capacity (AOC) of CAP. Fourier-Transformed Infrared Spectroscopy-Attenuated Total Reflectance (FTIR-ATR) was used to identify the functional groups present in the pomace. TAC, TFC, TSC and TPC were used as inputs to model AOC using Gaussian Process Regression (GPR), and Support Vector Regression (SVR) and a coupled model was developed using the residuals of GPR and SVR. It was found that increasing drying temperature decreased TAC, TFC, TPC and AOC but TSC increased. Both GPR and SVR predicted AOC with high accuracy. Drying CAP at lower temperature preserved more bioactive compounds hence high AOC; FTIR-ATR showed that CAP has good hydration capacity and contains majorly inorganic phosphates, aliphatic hydrocarbons and primary alcohols. Model coupling enhanced AOC prediction. |
format | Online Article Text |
id | pubmed-9479022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-94790222022-09-17 Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model Luka, Bobby Shekarau Yuguda, Taitiya Kenneth Adnouni, Meriem Zakka, Riyang Abdulhamid, Ibrahim Bako Gargea, Bumbyerga Garboa Heliyon Research Article Crude extracts from cashew apple pomace (CAP) dried at different temperatures were used in High-Pressure Liquid Chromatography to quantify total alkaloids content (TAC), total flavanoids content (TFC), total saponin content (TSC) and total phenolics content (TPC). Diphenyl-1-picrylhydrazyl (DPPH) was used to determine the antioxidant capacity (AOC) of CAP. Fourier-Transformed Infrared Spectroscopy-Attenuated Total Reflectance (FTIR-ATR) was used to identify the functional groups present in the pomace. TAC, TFC, TSC and TPC were used as inputs to model AOC using Gaussian Process Regression (GPR), and Support Vector Regression (SVR) and a coupled model was developed using the residuals of GPR and SVR. It was found that increasing drying temperature decreased TAC, TFC, TPC and AOC but TSC increased. Both GPR and SVR predicted AOC with high accuracy. Drying CAP at lower temperature preserved more bioactive compounds hence high AOC; FTIR-ATR showed that CAP has good hydration capacity and contains majorly inorganic phosphates, aliphatic hydrocarbons and primary alcohols. Model coupling enhanced AOC prediction. Elsevier 2022-09-06 /pmc/articles/PMC9479022/ /pubmed/36119886 http://dx.doi.org/10.1016/j.heliyon.2022.e10461 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Luka, Bobby Shekarau Yuguda, Taitiya Kenneth Adnouni, Meriem Zakka, Riyang Abdulhamid, Ibrahim Bako Gargea, Bumbyerga Garboa Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title | Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title_full | Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title_fullStr | Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title_full_unstemmed | Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title_short | Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model |
title_sort | drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled gaussian process regression and support vector regression (gpr–svr) model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479022/ https://www.ncbi.nlm.nih.gov/pubmed/36119886 http://dx.doi.org/10.1016/j.heliyon.2022.e10461 |
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