Sample preparation product addresses challenge of testing high fat samples
The Agilent Bond Elut Enhanced Matrix Removal (EMR)-Lipid is a dispersive solid phase extraction (SPE) sorbent that selectively removes lipids from the matrix of samples.
EMR – Lipid is a proprietary sorbent developed to target lipids using size exclusion and hydrophobicity, which is what allows it to remove lipids while retaining the analytes, said the firm.
Removal of lipids from samples enables laboratories to improve data quality, increase confidence in results, and reduce sample re-runs – meaning higher throughput and lower operating costs, said Agilent.
‘Many techniques are cumbersome’
April DeAtley, sample preparation product manager at Agilent Technologies, said everyone is looking for the simplest route to effective sample preparation.
“Many techniques that effectively remove fats from samples are very cumbersome often requiring many steps over a multi-day procedure. Even then samples may not be clean enough to detect residue levels of analytes,” she told FoodQualityNews.
“Dispersive techniques are great for multi-class analysis of target analytes, but for the preparation of high fat samples the current techniques are ineffective at reliably removing lipid/matrix interferences. Often target compounds are removed along with lipids causing reproducibility issues and increased error in results.”
Agilent said labs will be able to remove matrix interferences that have made test results challenging to reproduce and can achieve cleaner sample profiles with improved reliability.
DeAtley said labs want to get results quickly while maintaining data integrity.
“They would like to see reduced sample-to-sample variability that can be caused by ineffective sample preparation. This is especially true with high fat samples. Matrix interferences lead to variability, difficult data integration and interpretation and increase the likelihood of error,” she said.
“Even small amounts of lipids in samples being analyzed by LC-MS and GC-MS can affect instrument performance.”
Current techniques
Agilent said techniques being used now range from multi-step, multi-class solid phase extraction, liquid/liquid extraction (LLE), GPC and modified QuEChERS techniques – but all existing sample preparation techniques have drawbacks.
“SPE and GPC and yield clean samples but at the expense of time, materials and solvents and preparations may take up to three days to complete,” said DeAtley.
“LLE and modified QuEChERS techniques can be ineffective at selectively removing lipids and can also lead to lower analyte recoveries.
“Some labs even freeze samples and scrape the solid lipids off of the sample prior to employing these techniques. In every instance, there are trade-offs. Do you want really clean samples or do you want to retain and detect your analytes in a timely manner?”
DeAtley said EMR-Lipid can speed up sample preparation workflows for challenging high-fat samples.
“Existing techniques like LLE or typical dispersive sorbents in QuECHeRS workflows are ineffective at removing lipids from high-fat samples. Often, additional sample preparation steps or procedures are required to get the samples to a state where they can by analyzed by highly sensitive LC-MS or GC-MS instrumentation,” she said.
“These additional steps require more operator time and increase the error and variability associated with multiple sample preparation steps. With EMR-Lipid, one procedure can effectively remove lipids to provide a clean sample that is ready to analyze by LC-MS or GC-MS.”
Matrix interference challenges
Matrix interferences, lipids being chief among them, can create chromatographic challenges with Ion suppression is one of the issues.
“If the interfering compound elutes at the same time as the compound of interest, it will interfere causing a lower signal,” said DeAtley.
“This can lead to a failed sample if the lab is determining component analysis or a false negative, which means a contaminant, could slip through and make its way into the food chain. The presence of lipids can also cause a very noisy baseline making integration difficult and time consuming.”
DeAtley said re-runs are very common when analyzing high fat samples for these reasons.
“This can cause samples to fall below the guaranteed level, lead to QC or calibration samples to be outside of the acceptable RSD or recovery levels or simply make integrating the peak of interest too risky,” she said.
“When a failure occurs, most labs have to re-run samples in duplicate. Inadequate removal of lipids from samples can result in poor confidence in data, uncertainty in the analysis and troubleshooting ambiguous results in addition to re-running the sample.
“All of these steps require additional time and can result in loss of productivity and increased cost per sample due to multiple runs.”
