Efforts to explore optimal molecular methods for identifying plant mixtures, particularly pollen, are increasing. Pollen identification (ID) and quantification is important in many fields, including pollination ecology and agricultural sciences, but quantifying mixture proportions remains challenging. Traditional pollen ID using microscopy is time-consuming, requires expertise, and has limited accuracy and throughput. Molecular barcoding approaches being explored offer improved accuracy and throughput. The common approach, amplicon sequencing, employs PCR amplification to isolate DNA barcodes, but introduces significant bias, impairing downstream quantification. We apply a novel molecular hybridisation capture approach to artificial pollen mixtures, to improve upon current taxon ID and quantification methods. The method randomly fragments DNA, and uses RNA baits to capture DNA barcodes, which allows for PCR duplicate removal, reducing downstream quantification bias. Metabarcoding was tested using two reference libraries constructed from publicly available sequences; the matK plastid barcode, and RefSeq complete chloroplast references. Single barcode-based taxon ID did not consistently resolve to species or genus level. The RefSeq chloroplast database performed better qualitatively but had limited taxon coverage (relative to species used here) and introduced ID issues. At family level, both databases yielded comparable qualitative results, but the RefSeq database performed better quantitatively. A restricted matK database containing only mixture species yielded sequence proportions highly correlated with input pollen proportions, demonstrating that hybridization capture usefulness for metabarcoding and quantifying pollen mixtures. The choice of reference database remains one of the most important factors affecting qualitative and quantitative accuracy.