Neutral Loss Matcher

Match product-ion neutral losses against caller-provided diagnostic differences.

Description

Neutral-loss matching compares precursor-to-product differences with a supplied list of expected losses.

Neutral-loss matching compares precursor-to-product differences with a supplied list of expected losses. A tolerance defines an inclusive matching interval.^[1]

Match product-ion neutral losses against caller-provided diagnostic differences.

When to use Neutral Loss Matcher

Use this tool to match product-ion neutral losses against caller-provided diagnostic differences.

Prepare the measured profile, formula counts, upstream model outputs or caller-defined reference criteria requested. Keep paired entries aligned and units, sample basis and preprocessing consistent.

How to use Neutral Loss Matcher and understand its results

Prepare the measured profile, formula counts, upstream model outputs or caller-defined reference criteria requested. Keep paired entries aligned and units, sample basis and preprocessing consistent.
Enter precursorMz, productMz, expectedNeutralLosses, toleranceMz using the field table and calculation rules, then run the tool.
Read observedLosses, matchedLossIndexes. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
precursorMz	Precursor mz	number	Required
productMz	Product mz	list of number	Required
expectedNeutralLosses	Expected neutral losses	list of number	Required
toleranceMz	Tolerance mz	number	Required

Enter positive precursor, positive products no larger than precursor, positive expected losses and nonnegative tolerance. Empty product or expected lists are allowed. Return differences and matched zero-based product indexes.

loss_i=precursorMz−productMz_i; match if any |loss_i−expected_j|≤tolerance

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
observedLosses	Observed losses	list of number
matchedLossIndexes	Matched loss indexes	list of number

json

{
  "input": {
    "precursorMz": 200,
    "productMz": [
      182,
      172
    ],
    "expectedNeutralLosses": [
      18,
      28
    ],
    "toleranceMz": 0
  },
  "output": {
    "observedLosses": [
      18,
      28
    ],
    "matchedLossIndexes": [
      0,
      1
    ]
  }
}

Limitations and assumptions

Only m/z differences are computed. Charge-state conversion, fragment identity and which reference matched are not returned.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use XIC Mass Window: Plan an extracted-ion m/z window from target mass and ppm tolerance. For a complementary task, use Acyl Fragment Ladder: Count consecutive MS/MS acyl-chain fragment mass differences.

References

Neutral Loss Matcher — tool.io

Similar or alternative tools

XIC Mass Window
Plan an extracted-ion m/z window from target mass and ppm tolerance.
Acyl Fragment Ladder
Count consecutive MS/MS acyl-chain fragment mass differences.