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Updated: Jan 31, 2026

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
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Noninteger Root Transformations for Preprocessing Nanoelectrospray Ionization High-Resolution Mass Spectra for the

Yue Tang1, Peter B Harrington1

  • 1Ohio University Center for Intelligent Chemical Instrumentation , Department of Chemistry and Biochemistry, Clippinger Laboratories , Athens , Ohio 45701-2979 , United States.

Analytical Chemistry
|December 20, 2018
PubMed
Summary
This summary is machine-generated.

Noninteger root transformations improve high-resolution mass spectrometry (HRMS) for differentiating Cannabis samples. A 2.5 root transformation optimized chemical profiling, enhancing data analysis for complex mixtures.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics

Background:

  • High dynamic range measurements often require data transformation, like the square root transform, to reduce range.
  • Noninteger root transformations offer potential improvements over traditional methods for data preprocessing.

Purpose of the Study:

  • To evaluate the effectiveness of noninteger root transformations in improving chemical profiling and classification of Cannabis samples using high-resolution mass spectrometry (HRMS).
  • To determine optimal parameters for root transformation and mass resolving power for Cannabis sample differentiation.

Main Methods:

  • Utilized high-resolution mass spectrometry (HRMS) coupled with nanoelectrospray ionization (Nano-ESI) for analyzing 23 Cannabis samples.
  • Applied noninteger root transformations (e.g., 2.5 root) and varied mass resolving powers.
  • Employed classification algorithms including super partial least-squares discriminant analysis (sPLS-DA), support vector machine (SVM), and SVM classification tree type entropy (SVMTreeH).
  • Used response surface modeling to identify optimal parameters for root transformation and resolving power.

Main Results:

  • The 2.5 root transformation demonstrated superior performance for chemical profiling across different mass resolving powers.
  • Optimal parameters for root transformation (2.3) and resolving power (0.005) were identified using response surface modeling.
  • Noninteger root transformations effectively decrease dynamic range, enhancing the visibility of smaller, significant spectral features.
  • Broad optimal ranges for root and resolution were found for Cannabis classification via Nano-ESI.

Conclusions:

  • Noninteger root transformation is a valuable spectral preprocessing technique for managing high dynamic range data in HRMS.
  • Optimized root transformation and resolving power significantly improve the classification accuracy of Cannabis samples.
  • The study introduces a "chasing-the-optimum" method for refining response surface models in spectral data analysis.