Determination of optimum extraction conditions and evaluation of biological activities of Prunus Armeniaca L. (Apricot) fruit

Abstract

In this study, optimization and biological activities of biologically active components obtained from Prunus armeniaca L. (apricot) fruit were investigated. Within the scope of the study, optimum extraction conditions were determined by using different extraction parameters (temperature, time and ethanol/water ratio) and the most suitable extracts were produced in terms of biological activity. Response Surface Method (RSM) and Artificial Neural Networks-GENetic Algorithms (ANN-GA) techniques were applied for extraction optimization. Antioxidant, antiproliferative and anticholinesterase activities of the extracts obtained under optimum conditions were evaluated. In antioxidant activity tests, it was observed that the extracts optimized by ANN-GA method presented higher values ​​in terms of total antioxidant status (TAS) and DPPH free radical scavenging activity. Likewise, it was determined that total phenolic and flavonoid contents of the extracts obtained by ANN-GA method were higher compared to the extracts obtained by RSM method. In antiproliferative activity tests, cytotoxic effects of optimized extracts on A549 lung cancer cell line were investigated and it was seen that they suppressed cell viability in a dose-dependent manner. It was found that extracts optimized by ANN-GA method showed stronger antiproliferative effect than extracts obtained by RSM method at certain concentrations. In anticholinesterase activity analyses, it was determined that extracts optimized by ANN-GA method had higher potential to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes than extracts obtained by RSM method. However, when compared to galantamine used as reference inhibitor, it was found that the inhibitory effect of the obtained extracts was lower. As a result of phenolic component analysis, it was seen that extracts optimized by ANN-GA method had higher concentrations of phenolic components such as kaempferol, fumaric acid, gallic acid, caffeic acid and naringenin. However, it was found that some compounds (e.g. resveratrol and salicylic acid) were extracted at higher rates with the RSM method. In conclusion, the ANN-GA method increases the extraction efficiency of biologically active components of P. armeniaca fruit, strengthening their antioxidant, antiproliferative and anticholinesterase activities. These findings indicate that AI-supported extraction methods have the potential to be used in functional food and pharmaceutical applications.

Affiliated Institutions

• Kahramanmaraş Sütçü İmam University TR
• Osmaniye Korkut Ata University TR
• Adnan Menderes University TR

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