Selective separation and determination of quercetin from red wine by molecularly imprinted nanoparticles coupled with HPLC and ultraviolet detection


Zengin A. , Badak M. U. , Aktaş N.

JOURNAL OF SEPARATION SCIENCE, cilt.41, 2018 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 41 Konu: 17
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1002/jssc.201800437
  • Dergi Adı: JOURNAL OF SEPARATION SCIENCE

Özet

In this study, a highly sensitive and selective sample pretreatment procedure using molecularly imprinted silica nanoparticles was developed for the extraction and determination of quercetin in red wine samples coupled with high-performance liquid chromatography with ultraviolet detection. The imprinted silica nanoparticles were prepared in the presence of N-acryoyl-l-aspartic acid (functional monomer), quercetin (template), azobisisobutyronitrile (initiator) and methylene bisacrylamide (cross-linker) and methanol/water (porogen) via surface-initiated reversible addition-fragmentation chain transfer polymerization. Surface characterization was performed and several imprinting parameters were investigated, and the results indicated that adsorption of quercetin on the imprinted silica nanoparticles followed a pseudo-first-order adsorption isotherm with a maximum adsorption capacity at 26.4mg/g within 60min. The imprinted silica nanoparticles also showed satisfactory selectivity towards quercetin as compared with its structural analogues. Moreover, the imprinted nanoparticles preserved their recognition ability even after five adsorption-desorption cycles. Meanwhile, the nanoparticles were successfully applied to selective extraction of quercetin from red wine with a high recovery (99.7-100.4%). The limit of detection was calculated to be 0.058g/mL with a correlation coefficient 0.9996 in the range of 0.2-50g/mL. As a result, the developed selective extraction method using molecular imprinting technology simplifies the sample pretreatment procedure before determination of quercetin in real samples.