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[Reader Insight] Optimizing Matrix Curve Quantification in Mass Spectrometry
This article is submitted by expert chromatographer OU Shuo-Jun. Welch Materials, Inc. is authorized to translate this article to English and publish it on behalf of the author.
Introduction
Matrix curve quantification is a commonly used method in mass spectrometry. Due to the excellent separation capability of the quadrupole, complete baseline separation of the analyte in the chromatographic column is not necessary. To shorten analysis time, gradient elution programs are often set arbitrarily in experiments, which may result in incomplete separation between impurities and the target analyte. As a result, impurities and the target may compete for ionization, affecting the analyte's response, thus causing matrix effects.
Matrix effects can be classified as positive or negative, which are reflected in the increase or decrease in the analyte’s response. These response changes are independent of recovery rates.
Typically, we use sample preparation solutions to create a standard curve for quantification, referred to as a matrix curve. The advantage of using a matrix curve for quantification is that there is no need to purchase expensive isotopic internal standards. However, for accurate quantification, there are several details that need to be carefully considered.
Different Samples Require Corresponding Matrix Solutions
This is easy to understand. For example, when determining pesticide residues, the matrix effect differs greatly between various fruits and vegetables due to significant differences in composition.
But should different apple samples each require their own matrix solution? The author’s recommendation is: yes, they should. This is because the endogenous components like sugars, acids, and pigments in different samples vary, and these components account for a much larger proportion than the analyte itself. This is one of the primary causes of matrix effects.
For another example, even if the base powder is the same, different stages of infant formula may contain different types and amounts of added vitamins, lipids, and proteins. These additives could also contribute significantly to matrix effects.
Only samples that have very similar compositions or manufacturing processes (such as the same type of food contact materials, white vinegar, or glucose syrup) may suffice from one same matrix solution. However, this can only be determined based on the results.
Use Negative Samples When Possible
While using matrix solutions from positive samples to create curves does not alter the slope of the curve, it can raise the intercept, adding an extra calibration parameter.
Additionally, if the volume of the processing solution is small, multiple samples may be needed to obtain the required processing solution. It’s important to first mix the solution before preparing the curve. Otherwise, the errors between parallel experiments could lower the correlation coefficient of the curve, affecting its quantification ability.
In cases where all samples are positive, the dilution factor during sample preparation needs to be adjusted. The analyte concentration should remain below the midpoint of the curve’s concentration range, reducing the impact of baseline values on the slope.
If multiple compounds are being quantified with large concentration differences, the sample concentrations should be grouped and different dilution factors should be used to prepare the matrix curve.
Avoid an Excessively Large Concentration Range for the Matrix Curve
Many compounds exhibit good linearity when prepared with pure solvents over a large concentration range. However, when a matrix solution is used to prepare the curve, the matrix effect may differ at very low and high concentrations.
Taking negative effect (which is more common than positive effect) as an example, the presence of a large amount of matrix may suppress ionization as the concentration increases. However, the final evaluation should be based on the correlation coefficient of the curve. If the correlation coefficient is high, the curve can be considered valid.
Minimize Matrix Effects
Excessive matrix effects indicate that the matrix significantly interferes with the analysis, potentially causing large deviations in the results. When there is an issue with the measurement, it can be difficult to trace the error to its source, as the severity of the matrix effect could obscure other contributing errors.
Again taking negative effect as an example, the author suggests that the suppression rate of matrix effects should be no less than 30%. If the negative effect is below 30%, optimization of sample preparation and purification parameters, or extending the chromatographic retention time, is recommended to reduce the impact of matrix effects.
It’s important to note that matrix curve quantification only corrects for the influence of matrix effects on the results, and does not adjust for recovery rates. This differs from the standard addition method. To assess the feasibility of a method, matrix effects and recovery rates should be discussed and corrected separately.
For samples with strong matrix effects or high background concentrations, quantification using a matrix curve can be challenging. In such cases, readers may refer to another article from the author, “Matrix Effects in Mass Spectrometry Analysis: Causes and Solutions.”
Additionally, since matrix differences are significant among various samples, when preparing matrix curves for different samples, attention should be paid to potential contamination of the instrument and chromatographic column. It’s advisable to extend the equilibration time between elution programs and regularly clean the ion source.
Finally, in the author’s opinion, optimizing the sample preparation process in greater detail and appropriately extending analysis times are the more recommended effective way to minimize matrix effect interference.