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<p>Electrospray ionisation matrix effects (ME) remain a central challenge in multi-residue pesticide analysis by ultra-performance liquid chromatography tandem mass spectrometry (UPLC–ESI–MS/MS). This study presents a data-driven characterisation of ME across 90 pesticide analytes in ten structurally diverse food matrices using a QuEChERS protocol. Of 900 analyte–matrix measurements, 80.8% showed negligible ME (± 10%), 11.1% moderate (± 10–20%), 7.7% strong enhancement (&gt; + 20%), and 0.4% strong suppression (&lt; − 20%); 57.9% of interactions were enhancing, giving a net mean signed ME of + 3.3%. Two composite metrics were introduced: the matrix severity index (MSI), the mean absolute ME per matrix, and the matrix mitigation efficiency index (MMEI), the fraction of analytes within ± 15% of the reference under alternative injection solvents; the MSI is interpreted jointly with the mean signed ME to retain directional information. MSI ranged from 3.68 (green bean) to 10.07 (fennel), with aromatic, high-organic-matter commodities forming a high-severity cluster in principal component analysis (PCA; 77.3% of variance). A weak but significant log K<sub>ow</sub> –ME correlation (R<sup>2</sup> = 0.118, <i>p</i> = 0.0009) indicated lipophilicity as one contributory determinant (≈ 12% of variance). ACN/H₂O (10:90, v/v) was the optimal injection solvent (MMEI = 86.0%). Recovery at 100 µg kg⁻<sup>1</sup> was 100.2 ± 35.4%, with HorRat values below 0.73 confirming excellent precision; expanded uncertainty (k = 2) was below 25% for 72% of analytes under the GUM framework. The framework supports matrix-matched calibration, dilution, and uncertainty-budget decisions in regulatory food monitoring.</p>

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Mechanistic Mapping of Matrix Effects in LC–ESI–MS/MS Multi-residue Pesticide Analysis

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Abderrahim Eddahby,
  • Abdelkrim Chahine,
  • Lahcen Hssaini

摘要

Electrospray ionisation matrix effects (ME) remain a central challenge in multi-residue pesticide analysis by ultra-performance liquid chromatography tandem mass spectrometry (UPLC–ESI–MS/MS). This study presents a data-driven characterisation of ME across 90 pesticide analytes in ten structurally diverse food matrices using a QuEChERS protocol. Of 900 analyte–matrix measurements, 80.8% showed negligible ME (± 10%), 11.1% moderate (± 10–20%), 7.7% strong enhancement (> + 20%), and 0.4% strong suppression (< − 20%); 57.9% of interactions were enhancing, giving a net mean signed ME of + 3.3%. Two composite metrics were introduced: the matrix severity index (MSI), the mean absolute ME per matrix, and the matrix mitigation efficiency index (MMEI), the fraction of analytes within ± 15% of the reference under alternative injection solvents; the MSI is interpreted jointly with the mean signed ME to retain directional information. MSI ranged from 3.68 (green bean) to 10.07 (fennel), with aromatic, high-organic-matter commodities forming a high-severity cluster in principal component analysis (PCA; 77.3% of variance). A weak but significant log Kow –ME correlation (R2 = 0.118, p = 0.0009) indicated lipophilicity as one contributory determinant (≈ 12% of variance). ACN/H₂O (10:90, v/v) was the optimal injection solvent (MMEI = 86.0%). Recovery at 100 µg kg⁻1 was 100.2 ± 35.4%, with HorRat values below 0.73 confirming excellent precision; expanded uncertainty (k = 2) was below 25% for 72% of analytes under the GUM framework. The framework supports matrix-matched calibration, dilution, and uncertainty-budget decisions in regulatory food monitoring.