With the popularity of multi-residue pesticide analysis based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) cleanup and liquid chromatography–mass spectrometry (LC-MS), matching optimal matrix-matched calibration protocols and LC-MS conditions to reduce matrix effects (MEs) has become a crucial task for analysts in their routines. However, dozens to hundreds of pesticide analytes in a single run generate increasingly multi-dimensional ME data, requiring appropriate tools to handle these data sets. Therefore, we established an ME analysis strategy by drawing on analytical thinking and tools from metabolomics analysis. Using this, matrix species-induced and mass spectrometry-induced systematic ME variations were distinguished, and pesticides contributed to the variations were scanned out. A simultaneous weakening of MEs on 24 pesticides in 32 different matrices was achieved using the time-of-flight-mass spectrometry (TOF-MS) scan under the information-dependent acquisition (IDA) mode of high-resolution mass spectrometry (HR-MS), compared to multiple reaction monitoring (MRM) scanning by tandem mass spectrometry (MS/MS). Bay leaf, ginger, rosemary, Amomum tsao-ko, Sichuan pepper, cilantro, Houttuynia cordata, and garlic sprout showed enhanced signal suppression in the MRM scan for 105 differential MRM transitions for 42 pesticides and in IDA mode for 33 pesticides, respectively. This study revealed the interference of matrix species and mass spectrometry on MEs and provided a novel strategy for ME analysis.

Background: Astilbin is a bioactive flavonoid with documented anti-inflammatory properties; however, its sustained interactions with the gut microbiota remain poorly understood. Fecal samples from three healthy donors were pooled and fermented with astilbin in an in vitro human colonic model over 7 days. This exploratory study aimed to characterize the temporal ecological shifts associated with prolonged astilbin exposure. Methods: Time-resolved 16S rRNA gene sequencing, PICRUSt2 functional prediction, BugBase phenotypic inference, and pseudo-targeted metabolomics were integrated to track microbial-metabolic dynamics. Results: Astilbin exposure was associated with a highly coordinated, three-stage microbial succession. Day 3 (D3) emerged as a putative inflection point, where the enrichment of pioneer degraders (Flavonifractor, Bacteroides) was temporally correlated with the appearance of polyphenol cleavage intermediates. This transition featured an early decrease in markers of proteolytic fermentation alongside a transient in vitro lipid-stress response. By D7, the community shifted toward a stable configuration enriched in butyrogenic taxa (Roseburia, Subdoligranulum, Megamonas), with progressive depletion of potentially opportunistic pathogens (Escherichia-Shigella). Multi-omics integration suggested that these structural successions were strongly associated with marked metabolic shifts. Inflammatory lipid markers (e.g., leukotriene B4) showed a characteristic “D3-burst/D7-clearance” pattern, whereas potentially barrier-protective metabolites, particularly 3-indolepropionic acid (3-IPA, log₂FC = 2.00) and urolithin B (log₂FC = 1.18), accumulated substantially. Conclusions: This exploratory study provides valuable high-resolution insights into astilbin’s potential as a dynamic ecological modulator. It outlines a temporal framework illustrating how the gut microbiota may shift from proteolytic fermentation toward 3-IPA-associated homeostasis. Although limited by a pooled fecal model and the absence of a vehicle control, these hypothesis-generating findings offer a solid foundation for future in vivo studies and mechanistic validations across diverse human cohorts.