TY - JOUR
T1 - Peak capacity optimization of peptide separations in reversed-phase gradient elution chromatography
T2 - Fixed column format
AU - Wang, Xiaoli
AU - Stoll, Dwight R.
AU - Schellinger, Adam P.
AU - Carr, Peter W.
PY - 2006/5/15
Y1 - 2006/5/15
N2 - The optimization of peak capacity in gradient elution RPLC is essential for the separation of multicomponent samples such as those encountered in proteomic research. In this work, we study the effect of gradient time (tG), flow rate (F), temperature (T), and final eluent strength (φ final) on the peak capacity of separations of peptides that are representative of the range in peptides found in a tryptic digest We find that there are very strong interactions between the individual variables (e.g., flow rate and gradient time) which make the optimization quite complicated. On a given column, one should first set the gradient time to the longest tolerable and then set the temperature to the highest achievable with the instrument Next, the flow rate should be optimized using a reasonable but arbitrary value of φfinal. Last, the final eluent strength should be adjusted so that the last solute elutes as close as possible to the gradient time. We also develop an easily implemented, highly efficient, and effective Monte Carlo search strategy to simultaneously optimize all the variables. We find that gradient steepness is an important parameter that influences peak capacity and an optimum range of gradient steepness exists in which the peak capacity is maximized.
AB - The optimization of peak capacity in gradient elution RPLC is essential for the separation of multicomponent samples such as those encountered in proteomic research. In this work, we study the effect of gradient time (tG), flow rate (F), temperature (T), and final eluent strength (φ final) on the peak capacity of separations of peptides that are representative of the range in peptides found in a tryptic digest We find that there are very strong interactions between the individual variables (e.g., flow rate and gradient time) which make the optimization quite complicated. On a given column, one should first set the gradient time to the longest tolerable and then set the temperature to the highest achievable with the instrument Next, the flow rate should be optimized using a reasonable but arbitrary value of φfinal. Last, the final eluent strength should be adjusted so that the last solute elutes as close as possible to the gradient time. We also develop an easily implemented, highly efficient, and effective Monte Carlo search strategy to simultaneously optimize all the variables. We find that gradient steepness is an important parameter that influences peak capacity and an optimum range of gradient steepness exists in which the peak capacity is maximized.
UR - https://www.scopus.com/pages/publications/33646740864
UR - https://www.scopus.com/pages/publications/33646740864#tab=citedBy
U2 - 10.1021/ac0600149
DO - 10.1021/ac0600149
M3 - Article
C2 - 16689544
AN - SCOPUS:33646740864
SN - 0003-2700
VL - 78
SP - 3406
EP - 3416
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 10
ER -