Determination of amygdalin in fifteen different fruit kernels and extraction optimization

IF 0.4 4区 化学 Q4 CHEMISTRY, MULTIDISCIPLINARY Revue Roumaine De Chimie Pub Date : 2023-10-23 DOI:10.33224/rrch.2022.67.10-12.03
Busra Nagihan OZTURK, Serap AYAZ SEYHAN, Dilek BILGIC ALKAYA
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Abstract

Amygdalin (AMG) is mainly as an alternative therapy for traditional cancer treatment. In this study, an effective HPLC-DAD method was developed to investigate the extraction efficiency and determine AMG levels in fifteen fruit kernels belonging to different families. AMG in bitter almond was extracted from using various solvents by different extraction methods (Soxhlet, ultrasonic, orbital shaking and microwave extraction). The most suitable method was determined by the optimized HPLC-DAD method was applied to fifteen different fruit kernels. The recoveries were in the range 94.68% to 95.26%. The LOD and LOQ values were 0.0097 mg/g and 0.0295 mg/g, respectively. The amygdalin content in kernel fruits was determined ranging from 0.074 to 65.21 mg/g. This study showed that AMG was determined in a short time and with high yield, and the presence of AMG was proven in other fruit kernels belonging to different families that had not been proven before.
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15种不同果仁中苦杏仁苷的含量测定及提取工艺优化
苦杏仁苷(AMG)主要作为传统癌症治疗的替代疗法。本研究建立了高效液相色谱- dad法,研究了不同科15种果仁中AMG的提取效率和含量。采用索氏提取法、超声提取法、轨道振荡提取法和微波提取法,对苦杏仁中的AMG进行了不同溶剂的提取。通过优化后的HPLC-DAD法确定了最合适的方法,并应用于15种不同的果仁。加样回收率在94.68% ~ 95.26%之间。检出限和定量限分别为0.0097 mg/g和0.0295 mg/g。测定果仁中苦杏仁苷含量为0.074 ~ 65.21 mg/g。本研究表明,AMG的测定时间短,产量高,并且在不同科的其他果粒中也证实了AMG的存在,这是以前没有证实的。
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来源期刊
Revue Roumaine De Chimie
Revue Roumaine De Chimie 化学-化学综合
CiteScore
0.80
自引率
20.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal Revue Roumaine de Chimie (Roumanian Journal of Chemistry) was founded in 1956 under the name Revue de Chimie. Acad. R. P. R. from 1964, the title was modified in Revue Roumaine de Chimie (preserving the numbering of the volumes started in 1956). In 1997, the English translation of the title – Roumanian Journal of Chemistry – was also included on each issue.
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