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Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
Published on: March 9, 2018
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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
Summary
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.
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.
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