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Kendrick Mass Defect Filtering Enables High-Throughput Untargeted Annotation of Minor Phytocannabinoids: Toward

Andrea Cerrato1,2, Giuseppe Cannazza3,4, Cinzia Citti3,4

  • 1Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.

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Kendrick Mass Defect (KMD) filtering improves the identification of minor phytocannabinoids in untargeted high-resolution mass spectrometry (HRMS) analysis. This method enhances accuracy and efficiency for analyzing complex cannabis compounds.

Keywords:
C. sativacannabinoidschemovarcompound discoverergeographical originhigh-resolution mass spectrometry

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Area of Science:

  • Analytical Chemistry
  • Natural Product Chemistry
  • Pharmacology

Background:

  • Phytocannabinoids from *Cannabis sativa* possess pharmacological relevance.
  • Untargeted high-resolution mass spectrometry (HRMS) faces challenges in annotating minor phytocannabinoids due to structural similarity and low abundance.
  • Comprehensive annotation of minor cannabinoids is crucial for understanding their biological roles.

Purpose of the Study:

  • To comparatively evaluate Kendrick Mass Defect (KMD)-based filtering workflows for efficient untargeted annotation of minor phytocannabinoids.
  • To assess different KMD filtering strategies in terms of coverage, accuracy, and computational efficiency.
  • To highlight the biological significance of minor cannabinoids through statistical analysis of chemical differentiation.

Main Methods:

  • Implementation of three KMD filtering workflows (pre-detection, post-detection, pseudo-KMD) using Compound Discoverer.
  • Analysis of 50 *Cannabis* inflorescence samples using untargeted HRMS.
  • Evaluation of workflows based on phytocannabinoid coverage, false positive rates, computation burden, and versatility.

Main Results:

  • Annotation of 61 phytocannabinoids, including alkyl homologues, isomers, O-methylated derivatives, and sesquicannabinoids.
  • KMD filtering significantly improved the throughput and accuracy of untargeted HRMS workflows.
  • Statistical analysis revealed chemical differentiation based on seed origin, chemovar, and reproductive strategy.

Conclusions:

  • KMD filtering is a valuable tool for enhancing the analysis of structurally related compounds in untargeted HRMS.
  • The study successfully identified and differentiated minor phytocannabinoids, underscoring their biological significance.
  • Optimized KMD workflows facilitate more comprehensive phytocannabinoid profiling in *Cannabis sativa*.