Using In Silico Fragmentation to Improve Routine Residue Screening in Complex Matrices.

Area of Science:

• Analytical Chemistry
• Mass Spectrometry
• Computational Chemistry

Background:

• Targeted residue screening often relies on physical reference substances.
• Multi-residue methods analyze numerous compounds, complicating reference substance management.
• In silico fragmentation offers a potential alternative for identifying residues.

Purpose of the Study:

• To investigate the efficacy of in silico fragmentation for routine targeted residue screening.
• To evaluate computational tools for predicting fragment ions and reducing false positives.
• To compare different in silico fragmentation algorithms.

Main Methods:

• Analyzed 97 analytes using reversed-phase liquid chromatography and data-independent acquisition (DIA) with ion mobility-hyphenated quadrupole time-of-flight mass spectrometry.
• Operated the instrument in MS^E mode with alternating low and high energy traces.
• Utilized a "chopping" bond disconnection algorithm and a rule-based algorithm for fragment ion analysis.

Main Results:

• The "chopping" bond disconnection algorithm successfully explained fragment ions for 92 out of 97 compounds.
• This algorithm accounted for a greater percentage of spectral abundance compared to the rule-based software.
• However, the "chopping" algorithm generated significantly more false positive fragments in a complex matrix (bovine liver extract).

Conclusions:

• In silico fragmentation shows promise for targeted residue screening, potentially reducing the need for extensive physical reference standards.
• The choice of algorithm impacts the balance between explaining true fragments and minimizing false positives.
• Further refinement of in silico methods is needed for robust application in complex matrices.