Matrix-Aware LC–MS/MS Quantification of Cyhexatin in Chemically Complex Herbal Matrices Using DOE-Assisted Extraction Optimization

Abstract

Matrix-dependent analytical interference remains a persistent challenge in LC–MS/MS quantification of hydrophobic pesticides in chemically complex herbal matrices. Cyhexatin, a highly hydrophobic organotin compound, exemplifies this challenge by exhibiting pronounced response variability across matrices under empirically optimized conditions. To address this limitation, we developed a matrix-aware LC–MS/MS workflow combining design-of-experiments (DOE)-assisted extraction optimization with matrix-matched calibration and surrogate internal standard (IS) normalization. Fractional factorial design was first applied to identify influential extraction parameters, followed by Box–Behnken design (BBD) to establish optimal extraction conditions in a representative challenging matrix, Angelicae Gigantis Radix. The optimized method demonstrated robust quantitative performance across Angelicae Gigantis Radix and Paeoniae Radix, satisfying Codex-aligned validation criteria for selectivity, linearity, accuracy, and precision. The method provided low-ppb analytical sensitivity, with limits of detection of approximately 3–4 ppb and a lower limit of quantification of 10 ppb. Notably, DOE-guided optimization improved cyhexatin recovery in the most challenging matrix from approximately 38% under empirical conditions to within the Codex acceptance range (70–120%). Matrix-matched calibration combined with surrogate IS normalization further reduced matrix-dependent signal variability and improved the stability of quantitative responses across matrices. The combined workflow provided robust quantitative performance in the two investigated herbal matrices and illustrated a design-oriented strategy for improving quantitative reliability under matrix-affected LC–MS/MS conditions.