Automated mass recalibration using background massesAll data sets contain persistent masses across the time and mass range.

Background masses are collected based on a persistency frequency exceeding

a predefined percentage of the dataset. They are then matched to a list of

known background masses. Figure 1a shows an example plot of assigned

calibrant masses as a function of time and mass.

There is significant additional potential to expand the list of potential calibrant

formulas as shown in Figure 1b.

Masses are recalibrated by calculating the mass deviation between the

theoretical and experimental calibrant masses in each scan which are reduced

to a linear or polynomial mass offset equation. Post calibrated mass differences

are then evaluated to develop an isotopic search window equation as a function

of mass – Figure 2 using either a linear or polynomial fitting.

Automated determination of EIC window widthsFalse positive substance detection increases as the EIC width increases. False

negative substance detection increases as the EIC width decreases. Any

solution is a balance of these two opposing challenges with an implemented

bias towards more false positives than false negatives.

EIC widths are automatically determined as a function of mass by determining

the instrumental mass jitter for all masses in the dataset.

“Taking High Resolution Data LC/MS Reduction to the next level for reduced false positive detection and increased mass accuracy”Mark A. Bayliss1, Joseph Simpkins1, Stephane Murphy1, Timothy R. Croley2, Ann M. Knolhoff2

(1) Virscidian Inc., 7330 Chapel Hill Road, Suite 201, Raleigh NC 27607, USA(2) FDA, CFSAN, 5100 Paint Branch Parkway, College Park, MD 20740, USAs

AbstractBoth targeted and non-targeted full scan MS assays rely on the

ability to extract and process extracted ion chromatograms (EIC) and

spectra. Data processing in almost all commercial and in-house

developed data processing packages requires a user to manually

enter data extraction parameters that are then used in the data

reduction. In most cases data extraction is reduced to a series of

fixed values that are applied irrespective of the mass being

evaluated, leading to a compromise in the specificity of data

extraction. This presentation focuses on recent developments to

automate the determination of the primary influencing variables in

data extraction and the automated recalibration of the mass scale

without the need for manually added mass calibration standards.

The automated asymmetric EIC extraction example in Figure 3, produces an

automated EIC width that is varied automatically based on the extraction mass.

For example at nearly m/z 1200 the EIC windows are just over 1 mDa wide

which is much narrower than is typically used (+/- 10mDa across the entire mass

scale).

12C Mass deviation analysis following automated data reductionMass deviation was evaluated across a number of example datasets. In most

cases the mass accuracy post-recalibration is improved. It is observed in a small

number of cases that mass deviations can be increased when the original mass

data is already close the ideal value and therefore may get shifted to a higher

deviation after recalibration. Figures 4a and 4b both demonstrate how mass

deviations are improved.

Analytical data is a complex multi‐dimensional challenge Even following mass recalibration, minimum EIC window extraction and

minimum search window analysis – there will always be some level of false

positive results that can pass through. These require additional strategies for

their confirmation or rejection.

Conclusions

Automated mass recalibration using background masses

• Using high persistence masses is a valid approach to mass scale

recalibration using either linear or non‐linear interpolation.

Current results suggest that more than 62% of results are better

than 0.5 ppm and 88% better than 1.0 ppm. Additional effort is

underway to see if this can be further improved.

Automated determination of Isotope Search windows

• Using calibrant mass deviations to determine the isotopic search

windows is a valid approach to automatically determining the

search window functions that are used in a variety of downstream

processing and search related functions.

Automated Determination of EIC extraction windows

• Automated determination of EIC windows is possible and allows

the extraction of EICs with the narrowest possible width while

ensuring EIC bins are not compromised with missing scans due to

the EICs being too narrow.

• Key challenges in developing automated data reduction and

substance confirmation

• Chromatographic co‐elution of isotopically overlapping substances

can lead to EIC bin overlapping and broadening unless carefully

managed and confirmed by additional steps including MS/MS or

other techniques.

• Chromatographic co‐elution of isotopically overlapping substances

can lead to a mass shifts due to ion merging events in some

instrument types.

• Confirmation of substance presence based on 12C data alone

increases the risk of false positive substance matches. Additional

strategies are required to ensure high confidence confirmation.

• It is necessary to allow a somewhat wider isotope search window

than might expected to reduce false negatives, which in turns

adds some additional false positive results.

Next Steps

The currently automated implementation for recalibration, EIC width

determination and mass search window shows great promise to

reduce both false positive as well as false negative determinations by

reducing mass deviation through scan‐by‐scan recalibration and

extraction of the narrowest possible EIC windows. The

implementation lends itself towards turn‐key type solutions. Key next

steps are to further evaluate the performance for false positives and

false negatives using a series of model systems. This is a very

challenging comparison to make due to the complexity of chemical

space.

MethodData Acquisition & Processing

A variety of different samples were analyzed using a ThermoFisher

Exactive LC/MS and data were processed with a research version of

Virscidian’s Analytical Studio – Process Chemistry software. Original

data was acquired with acquisition resolutions between 50,000 and

140,000.

Raw Data Extraction

High Resolution LC/MS data in the original vendor profile format

were analyzed to determine the center of gravity of mass [Mass

Centroid] and the uncertainty of measurement.

Mass Recalibration  

Automated recalibration of all masses in all scans was

accomplished by matching high residence background masses with

a lookup list of known background masses and common

contaminants.

Mass Accuracy Determination

The deviation between the mass calibrant expected and actual

masses was used to develop the isotopic search window equations

that are used during data extraction and processing.

Mass Stability Determination

A pre-binning analysis determines the Extracted Ion Chromatogram

(EIC) windows as a function of mass using a novel asymmetric bin

implementation.

Dataset processing

Ionized isotopic masses were calculated for each formula and

requested adduct. For each extracted EIC peak, an averaged

spectrum was created and evaluated for the presence of the target

formula considering both mass deviation from theoretical and the

intensity deviation from theoretical.

All values related to isotope search window, mass binning and EIC

extraction were based on the automatically calculated values.

Data Evaluation

Results were analyzed to determine which are the key influencing

parameters for automated extraction.

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

0 200 400 600 800 1000 1200

Extracted Ion Ch

romatogram W

idth

m/z

Extracted Ion Chromatogram Width Vs m/z

Seed Bin Width Multiplier 0.1, EIC Bin Width Multiplier 0.1

Seed Bin Width Multiplier 0.5, EIC Bin Width Multiplier 0.1

Seed Bin Width Multiplier 1.0, EIC Bin Width Multiplier 0.1

Seed Bin Width Multiplier 1.0, EIC Bin Width Multiplier 0.25

Seed Bin Width Multiplier 1.0, EIC Bin Width Multiplier 0.5

Sed Bin Width Multiplier 1.0, EIC Bin Width Multiplier 0.75

Seed Bin Width Multiplier 1.0, EIC Bin Width Multiplier 1.0

Figure 1a – Image Left – Plot of the identified calibrant masses (Green) as a function of scan and mass across an example dataset. Figure 1b – Image Right – Masses (Red) which represents future potential calibrant masses. Typically the list of unknown calibrant masses accounts for >50% of the high persistent masses in a dataset.

Figure 2 – Example plot of the isotopic search window as a function of mass, that is used in all downstream substance mass window searching.

ScanScan

m/zm/z

Figure 3 –Example plot of the EIC extraction widths as a function of mass for a series of known test compounds. The upper most curve represents the automatically determined and unbiased EIC width – with 100% of compound matching. Lower plots are biased to deliver narrower EIC windows with a concomitant increase in false negative results as the EIC window width is reduced.

Figure 4a – Image Left – Plot of an example dataset of pesticides and toxins (Resolution 50,000), showing mass deviations before (Blue) and after mass recalibration. (Red) Figure 4b – Image Right – An example mass recalibration where the instrument was in need of mass recalibration.

Figure 5a – Image Top –Isotopic overlay predicted Vs experimental for Cymoxanil which co-elutes with Chlordimeform in one example dataset. Mass deviation 12C = 8.22 ppm with an excellent isotope intensity pattern match.

Figure 5b – Image Lower –Isotopic overlay predicted Vs experimental for Chlordimeform. Mass deviation 12C = 0.24 ppm with an excellent isotope intensity pattern match.

100% substance match –

No manual bias

Increased False –ve results

Manually biased extraction settings

Figure 5 – Distribution of mass errors following recalibration and automated extraction for a series of spiked extracts of apple juice, baby milk formula and yoghurt at a variety of concentrations –number of measurements = 1619.