Peter de B. Harrington

Direct detection of trimethylamine in meat food products using ion mobility spectrometry

Biogenic amines are degradation products generated by bacteria in meat products. These amines can indicate bacterial contamination or have a carcinogenic effect to humans consuming spoiled meats; therefore, their rapid detection is essential. Trimethylamine (TMA) is a good target for the detection of biogenic amines because its volatility. TMA was directly detected in meat food products using ion mobility spectrometry (IMS). TMA concentrations were measured in chicken meat juice for a quantitative evaluation of the meat decaying process. The lowest detected TMA concentration in chicken juice was 0.6+/-0.2 ng and the lowest detected signal for TMA in a standard aqueous solution was 0.6 ng. IMS data were processed using partial least squares (PLS) and Fuzzy rule-building expert system (FuRES). Using these two chemometric methods, trimethylamine concentrations of different days of meat spoilage can be separated, indicating the decaying of meat products. Comparing the two methods, FuRES provided a better classification of different days of meat spoilage.

DIFFERENT DISCRETE WAVELET TRANSFORMS APPLIED TO DENOISING ANALYTICAL DATA

Discrete wavelet transform (DWT) denoising contains three steps:  forward transformation of the signal to the wavelet domain, reduction of the wavelet coefficients, and inverse transformation to the native domain. Three aspects that should be considered for DWT denoising include selecting the wavelet type, selecting the threshold, and applying the threshold to the wavelet coefficients. Although there exists an infinite variety of wavelet transformations, 22 orthonormal wavelet transforms that are typically used, which include Haar, 9 daublets, 5 coiflets, and 7 symmlets, were evaluated. Four threshold selection methods have been studied:  universal, minimax, Steins unbiased estimate of risk (SURE), and minimum description length (MDL) criteria. The application of the threshold to the wavelet coefficients includes global (hard, soft, garrote, and firm), level-dependent, data-dependent, translation invariant (TI), and wavelet package transform (WPT) thresholding methods. The different DWT-based denoising m...

Two-dimensional correlation analysis

Abstract This tutorial reviews the recent computational advances in two-dimensional (2D) correlation spectroscopy, presents the theory, and provides examples applying 2D correlation analysis. Two-dimensional correlation analysis is a method for visualizing the relationships among the variables in multivariate data and their temporal behavior by applying the complex cross-correlation function. This function measures correlations that occur at the same rate or frequency with respect to the data acquisition time. The complex cross-correlation function yields real and imaginary components that contain information about the phase behavior of the variables. The real component provides information about mutually dependent in-phase variations. Variations that occur out-of-phase (with time lags or leads) are given by the imaginary component. Two-dimensional correlation analysis is a general analysis method that can be used for the treatment of data from a variety of applications including image, distribution, environmental, and kinetic analysis.

Biomarker Profiling and Reproducibility Study of MALDI-MS Measurements of Escherichia coli by Analysis of Variance−Principal Component Analysis

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has proved useful for the characterization of bacteria and the detection of biomarkers. Key challenges for MALDI-MS measurements of bacteria are overcoming the relatively large variability in peak intensities. A soft tool, combining analysis of variance and principal component analysis (ANOVA-PCA) (Harrington, P. D.; Vieira, N. E.; Chen, P.; Espinoza, J.; Nien, J. K.; Romero, R.; Yergey, A. L. Chemom. Intell. Lab. Syst. 2006, 82, 283-293. Harrington, P. D.; Vieira, N. E.; Espinoza, J.; Nien, J. K.; Romero, R.; Yergey, A. L. Anal. Chim. Acta. 2005, 544, 118-127) was applied to investigate the effects of the experimental factors associated with MALDI-MS studies of microorganisms. The variance of the measurements was partitioned with ANOVA and the variance of target factors combined with the residual error was subjected to PCA to provide an easy to understand statistical test. The statistical significance of these factors can be visualized with 95% Hotelling T2 confidence intervals. ANOVA-PCA is useful to facilitate the detection of biomarkers in that it can remove the variance corresponding to other experimental factors from the measurements that might be mistaken for a biomarker. Four strains of Escherichia coli at four different growth ages were used for the study of reproducibility of MALDI-MS measurements. ANOVA-PCA was used to disclose potential biomarker proteins associated with different growth stages.

SELF-CONFIGURING RADIAL BASIS FUNCTION NEURAL NETWORKS FOR CHEMICAL PATTERN RECOGNITION

Construction of radial basis function neural networks (RBFN) involves selection of radial basis function centroid, radius (width or scale), and number of radial basis function (RBF) units in the hidden layer. The K-means clustering algorithm is frequently used for selection of centroids and radii. However, with the K-means clustering algorithm, the number of RBF units is usually arbitrarily selected, which may lead to suboptimal performance of the neural network model. Besides, class membership and the related probability distribution are not considered. Linear averaging (L-A) was devised for selection of centroids and radii for the RBFs and computing the number of RBF units. The proposed method considers the class membership and localized probability density distribution of each class in the training sets. The parameters related to the network construction were investigated. The network was trained with the QuickProp algorithm (QP) or Singular Value Decomposition (SVD) algorithm and evaluated with the po...

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. Two classification methods, partial least-squares-discriminant analysis (PLS-DA) and a fuzzy rule-building expert system (FuRES), were used to evaluate the signal correction of the fused vibrational spectra from the olive oils. The number of components was determined by using bootstrap Latin partitions (BLPs) in the signal correction routine and maximizing the average projected difference resolution (PDR). The same approach was used to select the number of latent variables in the PLS-DA evaluation and perfect classification was obtained. Biased PLS-DA models were also evaluated that optimized the number of latent variables to yield the minimum prediction error. Fuzzy or soft classification systems benefit from background removal. The FuRES prediction results did not differ significantly from the results that were obtained using either the unbiased or biased PLS-DA methods, but was an order of magnitude faster in the evaluations when a sufficient number of PC-OSC components were selected. The importance of bootstrapping was demonstrated for the automated OSC and PC-OSC methods. In addition, the PLS-DA algorithms were also automated using BLPs and proved effective.

Detection of cocaine and its metabolites in urine using solid phase extraction-ion mobility spectrometry with alternating least squares.

A reliable, alternative screening method for detection of cocaine and its metabolites, benzoylecgonine and cocaethylene in urine is demonstrated using solid phase extraction (SPE) coupled with ion mobility spectrometry (IMS). Data analysis with alternating least squares (ALS) is used to model IMS spectral datasets and separate the reactant ion peak from the product ion peaks. IMS has been used as a screening device for drug and explosive detection for many years. It has the advantages of atmospheric pressure operation, simple sample preparation, portability, fast analysis, and high sensitivity when compared to similar methods. Coupling SPE with IMS decreases the detection limits of drug metabolites in urine while removing salts and other polar compounds that suppress ionization during the measurement. The IMS analysis time in this experiment is 20s, much shorter than traditional chromatographic analysis. The application of ALS further increases the sensitivity and selectivity of this method. The detection limits of benzoylecgonine and cocaethylene are 10 ng/mL and 4 ng/mL, respectively. Commercial adulteration of urine specimens does not influence the ability to detect cocaine metabolites after sampling the urine with SPE. This method provides forensic chemists a viable approach for fast and simple drug screening.

Rapid multivariate curve resolution applied to identification of explosives by ion mobility spectrometry

Ion mobility spectrometers (IMS) are routinely used by airport personnel to screen for explosive residues on luggage, and law enforcement officials use ion mobility spectrometers to screen samples at crime scenes for drugs of abuse. IMS has several advantages when screening which include high sensitivity, low detection limits, and short analysis times. The Barringer Ionscan® 350, coupled to LabVIEW™ for data acquisition, can collect 50 spectra per second. These features are very important because of the high throughput demanded for screening luggage. Carry-on luggage in airports undergoes screening for explosive residues by swiping a small filter over the surface. The filter is then inserted into the spectrometer and analyzed. Results of the analysis are available in a matter of seconds. The detection method used must be accurate and rapid because of the large number of samples that must be analyzed in short periods. Unfortunately, IMS spectra can contain interferents because of the sample collection method. Rapid temperature programming coupled with chemometrics has been shown as a useful tool for the separation of analytes from interferents. Pentaerythritol tetranitrate (PETN) and cyclotetramethylene tetranitrate (HMX) are nonvolatile explosives. They decompose rapidly upon heating, making traditional analyses by gas chromatography difficult. In this work, PETN and HMX with interfering compounds were analyzed using rapid temperature programming. The Barringer Ionscan® 350, was modified so that the desorber heater could be programmed and the spectra acquired in real-time. Simple-to-use interative self-modeling mixture analysis (SIMPLISMA) was applied to the data to resolve spectral features that vary with respect to temperature.

Trace Explosive Detection in Aqueous Samples by Solid-Phase Extraction Ion Mobility Spectrometry (SPE-IMS)

Law enforcement agencies use ion mobility spectrometers for the detection of explosives, drugs of abuse, and chemical warfare agents. Ion mobility spectrometry (IMS) has the advantages of short analysis times, detections in the parts per billion concentrations, and high sensitivity. On-site environmental analysis of explosives or explosive residues in water is possible with ion mobility spectrometers. Unfortunately, the direct analysis of low levels of explosives in water is difficult. Extraction provides a method for pre-concentrating the analytes and removing interferents. Coupling solid-phase extraction (SPE) with IMS is useful for the identification of trace amounts of explosives in water. Commercially available SPE disks were used. After extraction, the sample disk is inserted into the ion mobility spectrometer, where the analytes are thermally desorbed from the disk. Concentrations as low as one part per trillion were detected with a Barringer Ionscan® 350. An external computer and acquisition software (LabVIEW™, National Instruments) were used to collect data. SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis) was applied to the data to resolve features that vary with respect to time.

Effects of static spectrum removal and noise on 2D-correlation spectra of kinetic data

Abstract The applicability of two-dimensional (2D) correlation spectroscopy to kinetic data is demonstrated. The photodecomposition of Mo(CO)6 was used as a model kinetic system. The effects of the steady-state and time-average methods for static spectrum removal were evaluated. The time-average method provided 2D-correlation spectra that were more easily interpreted. The influence of noise was evaluated with the synthetic data. The methods of static spectral removal were affected differently by the presence of noise and the time-average method was detrimentally affected by noise. A low-pass Fourier filtering method was devised to remove noise in the 2D-correlation spectra. Kinetic simulations were demonstrated as useful tools for interpretation of noisy data.