Quantitative analysis of underivatized amino acids in the sub- to several-nanomolar range by ion-pair HPLC using a corona-charged aerosol detector (HPLC–CAD)

The separation and quantification of underivatized naturally occurring amino acids (AAs) by high-performance liquid chromatography (HPLC) is advantageous for reducing preparation time, running costs, and analytical errors, and is compatible with the isolation of intact AAs. This study establishes and validates an analytical method for the separation and quantification of underivatized AAs and taurine (Tau) in the sub- to several-nanomolar range (ca. 0.1–1.6 nmol) by optimizing ion-pair HPLC coupled to a corona charged aerosol detector (corona CAD). Chromatographic separation of 19 AAs and Tau was achieved using a porous graphite carbon (PGC) column and nonafluoropentanoic acid (NFPA) as a volatile ion-pair reagent. The response of the corona CAD to the AAs was highly dependent on the eluate composition, whereas these response factors were similar for AAs in eluate with similar compositions. Regression curves and coefficients (r2) >0.998 were obtained for plots of injection amount versus peak area, except for Arg which co-eluted with a background peak. On the other hand, all plots of injection amount versus peak height regressed to curves with r2 > 0.997. Repeat quantification based on peak area showed lower relative standard deviations (RSDs) (typically better than 5%) than those based on peak height. The present method is useful for quantifying AAs from proteins and Tau in the sub- to several-nanomolar range without derivatization, and constant repeatability can be maintained by quantification using peak areas. •A quantification method for underivatized amino acids was developed by HPLC–CAD.•Chromatographic separation of amino acids and taurine is achieved by ion-pair HPLC.•Corona CAD shows steady responses to amino acids under similar eluate conditions.•Amino acids and taurine can be quantified in sub to several nanomolar range.•Relative standard deviations are mostly better than 5%.