Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Akkurt, Iskender(1)
• Alyafei, Mohammed(1)
• Anlar, Pinar(1)
• Arslankaya, Seher(1)
• Ay, Mizgin(1)
• Cakir, Ozlem(1)
• Durul, Melekber Sulusoglu(1)
• Ekmekci, Ismail(1)
• Ercisli, Sezai(1)
• Gueven, Sara Altun(1)
• Gunoglu, Kadir(1)
• Hayat, Faisal(1)
• Teke, Cagatay(1)
• Topcu, Kubra Cinar(1)

• MakaleAçık Erişim2025Scopus SJRQ1/r/bayburt/10771
  Optimization of Ethanol Extraction Conditions From Sun-Dried Apricot and Prediction of Antioxidant, Phenolic, and Flavonoid Contents and Their Effects on HCT116 Cells Using Artificial Neural Networks

  Topcu, Kubra Cinar; Gueven, Sara Altun; Cakir, Ozlem; Anlar, Pinar; Durul, Melekber Sulusoglu; Ay, Mizgin; Alyafei, Mohammed; Hayat, Faisal; Ercisli, Sezai

  This study aimed to optimize the ethanol extraction conditions of sun-dried apricot (Prunus armeniaca L.) and evaluate the antioxidant capacity, phenolic, and flavonoid contents of the obtained extracts. Response surface methodology (RSM) was applied to determine the optimal extraction conditions, which were identified as 60 degrees C temperature, 34% ultrasonic power, 46 min sonication time, and a 4 g/mL solid-liquid ratio. Under these conditions, the extract exhibited a total phenolic content (TPC) of 4.20 mg GAE/g, a total flavonoid content (TFC) of 7.09 mg QE/g, a DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging capacity of 1.37 mg TE/g, a ferric reducing antioxidant power (FRAP) value of 9.12 mg TE/g, and a thiobarbituric acid reactive substances (TBARS) level of 1.69 mg MDA/g. The artificial neural network (ANN) model provided highly accurate predictions for these parameters. Additionally, cell culture experiments demonstrated that the extract exerted a dose-dependent cytotoxic effect on HCT116 colon cancer cells, significantly reducing their viability. These findings highlight the potential of sun-dried apricot extracts as natural antioxidants with possible applications in the functional food and pharmaceutical industries.

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  AtıfScopus2·WoS2·OpenAlex2

  İncele
• MakaleYalnız künye2023Scopus SJRQ2/r/bayburt/11763
  Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

  Teke, Cagatay; Akkurt, Iskender; Arslankaya, Seher; Ekmekci, Ismail; Gunoglu, Kadir

  The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

  AtıfScopus18·WoS9·OpenAlex27

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