Why use one bracketing standard after six test samples in sequences on hplc?

Answer 1

Because to know the hplc system is working perfectly till the last sample.

Actually, System Suitability is run to show that the system is working perfectly. The purpose of "Bracketing Standards" or "Check Standards":

Bracketed Calibration

There are times when the HPLC conditions can change

during a sequence of samples. The longer the runtime and

the more samples in the sequence, the greater the

likelihood of this happening. Sometimes these changes

affect the detector response, and hence affect the validity

of the calibration. We can monitor changes by running a

QC standard periodically through the sequence, but this

does not update the calibration. We can re-run the

calibration standards periodically during the run, but this

will either average with the previous calibration or replace

it, and either way, it means that every few samples, the

calibration changes, making it hard to compare results. We

could ignore the changes, but this means that the

calibration accuracy becomes progressively worse during

the sequence. The solution is to use bracketed calibration.

Essentially this means running the calibration standards at

the beginning of the sequence and at the end, and makes

the assumption that any changes occurred in a linear

manner during the sequence. The data system then

changes the calibration incrementally from the beginning

to the end, and applies this to the results. If the

assumption that the change was linear is correct, the data

should then all be correctly quantified. Not all data systems

have this function, and for long runs it is very useful.

Answer 2

Using bracketing standards after every sixth sample in an HPLC sequence helps to monitor and correct any potential drift or variation in the system over time. By interspersing the test samples with bracketing standards, you can ensure the accuracy and reliability of the analytical results by calibrating the instrument performance and detecting any changes that might impact the data quality.