A novel approach for olive leaf extraction through ultrasound technology : Response surface methodology versus artificial neural networks

İlbay, Zeynep; Şahın, Selin; Büyükkabasakal, Kemal

doi:10.1007/s11814-014-0106-3

Cited by 53 publications

(22 citation statements)

References 36 publications

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“…Here, the 1:10 ratio provided the greatest oleuropein extraction yield (29.7 ± 1.7 mg/g), while no significant differences were seen between the other ratios tested. This increase in the oleuropein yield obtained through modulation of the olive leaf mass‐to‐solvent ratio is in agreement with mass transfer principles, as a lower solid‐to‐liquid ratio will provide a higher driving force for the extraction, in agreement with İlbay, Şahin, and Büyükkabasakal () for the same matrix.…”

Section: Resultssupporting

confidence: 83%

Enhanced yield of oleuropein from olive leaves using ultrasound‐assisted extraction

Cifá

Skrt

Pittia

et al. 2018

Food Science & Nutrition

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The aim of this study was to optimize the extraction of oleuropein from olive leaves through a systematic study of the effects of different parameters of ultrasound‐assisted extraction (USAE) on the oleuropein yield, in comparison with conventional maceration extraction. A range of operational parameters were investigated for both conventional maceration extraction and USAE: solvent type, olive leaf mass‐to‐solvent volume ratio, and extraction time and temperature. Oleuropein yield was determined using high‐performance liquid chromatography, with total phenolics content also determined. The optimized conditions (water–ethanol, 30:70 [v/v]; leaf‐to‐solvent ratio, 1:5 [w/v]; 2 hr; 25°C) provided ~30% greater oleuropein extraction yield compared to conventional maceration extraction. The total phenolics content obtained using the optimized USAE conditions was greater than reported in other studies. USAE is shown to be an efficient alternative to conventional maceration extraction techniques, as not only can it offer increased oleuropein extraction yield, but it also shows a number of particular advantages, such as the possibility of lower volumes of solvent and lower extraction times, with the extraction carried out at lower temperatures.

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Section: Resultssupporting

confidence: 83%

Enhanced yield of oleuropein from olive leaves using ultrasound‐assisted extraction

Cifá

Skrt

Pittia

et al. 2018

Food Science & Nutrition

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show abstract

“…Pinelo et al [34] explained that the mass transfer phenomenon accelerates when the taking of lower sample amounts permits one to obtain a higher phenolic concentration gradient between sample and solvent. The decrease in extraction efficiency after a given solid ratio in solvent is consistent with the principles of mass transfer because when a lower solid ratio in solvent is used the pushing force is expected to be higher [35].…”

Section: Effects Of Process Parameters On Maesupporting

confidence: 69%

Modeling and optimizing microwave-assisted extraction of antioxidant compoundsfrom marigold (Calendula officinalisL.) using response surface methodology

Bekdeşer¹

2019

Turk J Chem

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Calendula officinalis L. is a commercially important plant that finds application in the treatment of various diseases in traditional medicine. The total antioxidant capacity, radical scavenging activity, and total phenolic content of marigold extracts were investigated by Folin, CUPRAC, and DPPH assays, respectively. The optimum operating conditions of microwave-assisted extraction (MAE) including temperature, extraction time, solvent-to-solid ratio, and solvent concentration were ascertained by employing response surface methodology (RSM). The solvent (ethanol) concentration was the most significant operating factor among all responses of MAE. At the optimum extraction conditions, the maximum yield of total phenolic content, total antioxidant capacity, and radical scavenging activity obtained experimentally were very close to their predicted values, thus showing the suitability of the model used and the success of RSM in optimizing the extraction conditions. Chromatographic analysis of marigold extract was performed by UPLC-PDA-ESI-MS/MS system and chlorogenic acid was the main component (1742.50 ± 42.23 µ g/g DS).

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“…Evolutionary algorithms such as genetic algorithms and differential evolution are used for optimization and classification of biological systems [4][5][6][7]. Neural network technique has evolved as an efficient classifier and also as a programming methodology for optimization of systems [8][9][10][11]. For the evaluation of gene-expression data, neural networks have been meticulously tested for their capability to precisely distinguish among cancers belonging to several diagnostic categories [12].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

Eswari

Chandrakar

2016

Korean J. Chem. Eng.

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Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

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A novel approach for olive leaf extraction through ultrasound technology : Response surface methodology versus artificial neural networks

Cited by 53 publications

References 36 publications

Enhanced yield of oleuropein from olive leaves using ultrasound‐assisted extraction

Enhanced yield of oleuropein from olive leaves using ultrasound‐assisted extraction

Modeling and optimizing microwave-assisted extraction of antioxidant compoundsfrom marigold (Calendula officinalisL.) using response surface methodology

Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

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