The integrator samples the digital data at a rate determined by the initial peak width or by the calculated peak width, as the run progresses. It considers each data point as a potential baseline point.
The integrator determines a baseline envelope from the slope of the baseline, using a baseline-tracking algorithm in which the slope is determined by the first derivative and the curvature by the second derivative. The baseline envelope can be visualized as a cone, with its tip at the current data point. The upper and lower acceptance levels of the cone are:
+ upslope + curvature + baseline bias must be lower than the threshold level,
- upslope - curvature + baseline bias must be more positive (i.e. less negative) than the threshold level.
As new data points are accepted, the cone moves forward until a break-out occurs.
To be accepted as a baseline point, a data point must satisfy the following conditions:
it must lie within the defined baseline envelope,
the curvature of the baseline at the data point (determined by the derivative filters), must be below a critical value, as determined by the current slope sensitivity setting.
The initial baseline point, established at the start of the analysis is then continuously reset, at a rate determined by the peak width, to the moving average of the data points that lie within the baseline envelope over a period determined by the peak width. The integrator tracks and periodically resets the baseline to compensate for drift, until a peak up-slope is detected.