Noninteger Root Transformations for Preprocessing Nanoelectrospray Ionization High-Resolution Mass Spectra for the Classification of Cannabis

Typically, for measurements with a high dynamic range, the range is reduced by using the square root transform. By using noninteger roots coupled with systematic experimental design, improvements to the measurements may be obtained. The effect of using noninteger root transformation was evaluated using high-resolution mass spectrometry (HRMS) combined with nanoelectrospray ionization (Nano-ESI) to differentiate 23 samples of Cannabis. The mass spectra were evaluated and classified using different mass resolving powers and noninteger root transformations. Classification was achieved by super partial least-squares discriminant analysis (sPLS-DA), support vector machine (SVM), and SVM classification tree type entropy (SVMTreeH). The 2.5 root transformation gave the best overall performance at different resolving powers for chemical profiling from a multilevel factorial experimental design using 2 factors and more than 4 levels. Response surface modeling using a cubic polynomial model of the bootstrapped sPLS-DA average prediction accuracies yielded optima at 0.005 for resolving power and 2.3 for the root transformation. Root transformation is an important spectral preprocessing tool for decreasing the dynamic range so that the relative variance of smaller but more important features may be inflated. For the classification of Cannabis using Nano-ESI, the optimal ranges of root and resolution were broad. The chasing-the-optimum method has been introduced for refining the polynomial response surface model.