Differential signal detection system for improved analysis of overlapping signals in liquid chromatography

Quantification from highly drifted and overlapped chromatographic peaks using second-order calibration methods

For determining low levels of pesticides and phenolic compounds in river and wastewater samples by high performance liquid chromatography (HPLC), solid phase extraction (SPE) is commonly used before the chromatographic separation. This preconcentration step is not necessarily selective for the analytes of interest and it may retain other compounds of similar characteristics as well. In this case, we present, humic and fulvic acids caused a large baseline drift and overlapped the analytes to be quantified. The inaccurate determinations of the area of the peaks of these analytes made it difficult to quantify them with univariate calibration. Here we compare three second-order calibration algorithms (generalized rank annihilation method (GRAM), parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least squares (MCR-ALS)) which efficiently solve this problem. These methods use second-order data, i.e., a matrix of responses for each peak, which is easily obtained with a high performance liquid chromatography-diode array detector (HPLC-DAD). With these methods, the area does not need to be directly measured and predictions are more accurate. They also save time and resources because they can quantify analytes even if the peaks are not resolved. GRAM and PARAFAC require trilinear data. Biased and imprecise concentrations (relative standard deviation, %R.S.D. = 34) were obtained without correcting the time-shift. Hence, a time-shift correction algorithm to align the peaks was needed to obtain accurate predictions. MCR-ALS was the most robust to the time-shift. All three algorithms provided similar mean predictions, which were comparable to those obtained when sulfite was added to the samples. However, the predictions for the different replicates were more similar for the second-order algorithms (%R.S.D. = 3) than the ones obtained by univariate calibration after the sulfite addition (%R.S.D. = 13).

Procedure for the quantification of rider peaks

Rider peaks are small peaks which are not well resolved from a large and asymmetrical neighbour but sit on its trailing side. The usual case is a large, tailed peak which is eluted just in front of the small peak, although the opposite situation can also occur (a small peak in front of a large peak with fronting). The common integration techniques. i.e. separating the peaks by vertical drop or by a tangent and determining area or height, give erroneous results. We propose a method for their quantification with low error. It is necessary to set up a "two-dimensional" calibration by varying both concentrations, i.e. of the large peak and of the rider. This leads to a series of linear equations which describe the rider size, as found by the integrator, as a function of the size of the large peak. The y-axis intercepts i of these equations show a linear relationship with the concentration x of the rider analyte, whereas the slopes s follow a quadratic relationship. These equations can be used to solve the equation y = s(x) x z + i(x) for x (y and z are the integrated peak size of the rider and the large peak, respectively). The procedure was tested with computer-generated peak pairs as well as with HPLC separations of 2,3-dimethylaniline (large tailing peak) and 2,3-dimethylphenol (symmetrical rider peak).