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.

Feature expansion by a continuous restricted Boltzmann machine for near-infrared spectrometric calibration.

A modified algorithm for training a restricted Boltzmann machine (RBM) has been devised and demonstrated for improving the results for partial least squares (PLS) calibration of wheat and meat by near-infrared (NIR) spectroscopy. In all cases, the PLS calibrations improved by using the abstract features generated from the RBM so long as the nonlinear mapping increased the dimensionality. The evaluations were validated using bootstrapped Latin partitions (BLPs) with 5 bootstraps and 3-Latin partitions which proved useful because of the statistical learning and random initial conditions of the RBM networks.

Support Vector Machine Classification Trees.

Proteomic and metabolomic studies based on chemical profiling require powerful classifiers to model accurately complex collections of data. Support vector machines (SVMs) are advantageous in that they provide a maximum margin of separation for the classification hyperplane. A new method for constructing classification trees, for which the branches comprise SVMs, has been devised.

Fuzzy grid encoded independent modeling for class analogies (FIMCA).

A novel representation of chemical measurements has been devised for which the data are encoded as fuzzy grids instead of the standard convention as a vector. The fuzzy grid encoded data and data in the standard format were evaluated with soft independent modeling for class analogies (SIMCA). The fuzzy version of SIMCA is referred to as FIMCA.

Automated principal component-based orthogonal signal correction applied to fused near infrared-mid-infrared spectra of French olive oils.

An approach for automating the determination of the number of components in orthogonal signal correction (OSC) has been devised. In addition, a novel principal component OSC (PC-OSC) is reported that builds softer models for removing background from signals and is much faster than the partial least-squares (PLS) based OSC algorithm. These signal correction methods were evaluated by classifying fused near- and mid-infrared spectra of French olive oils by geographic origin.

Bootstrap classification and point-based feature selection from age-staged mouse cerebellum tissues of matrix assisted laser desorption/ionization mass spectra using a fuzzy rule-building expert system.

A bootstrap method for point-based detection of candidate biomarker peaks has been developed from pattern classifiers. Point-based detection methods are advantageous in comparison to peak-based methods. Peak determination and selection are problematic when spectral peaks are not baseline resolved or on a varying baseline.

Nonlinear wavelet compression of ion mobility spectra from ion mobility spectrometers mounted in an unmanned aerial vehicle.

Linear and nonlinear wavelet compression of ion mobility spectrometry (IMS) data are compared and evaluated. IMS provides low detection limits and rapid response for many compounds. Nonlinear wavelet compression of ion mobility spectra reduced the data to 4-5% of its original size, while eliminating artifacts in the reconstructed spectra that occur with linear compression, and the root-mean-square reconstruction error was 0.

Evaluation of neural network models with generalized sensitivity analysis.

A sensitivity analysis method for discovering characteristic features of the input data using neural network classification models has been devised. The sensitivity is the gradient of the neural network model response function, and because neural network models are nonlinear, the gradient depends on the point where it is evaluated. Two criteria are used for measuring the sensitivity.