Passive Airborne Fourier Transform Infrared Remote Detection of Methanol by Use of Wavelet Analysis as A Feature Extraction Method

Wavelet analysis was evaluated as a data preprocessing tool in the construction of automated classifiers for the detection of volatile organic compounds from passive Fourier transform infrared remote sensing data collected in a downward-looking mode from an aircraft platform. The discrete wavelet transform was applied to single-beam spectra and patterns were formed with either the wavelet coefficients directly or with spectra reconstructed with selected resolution levels of the wavelet decomposition. Automated classifiers were constructed with support vector machines (SVM) and used to detect releases of methanol from an industrial site. A key issue in this work was the desire to use data collected during controlled experiments on the ground to train the SVM classifiers. Spectral backgrounds in these ground-collected data are different than those encountered as the aircraft flies, however, and the development of successful classification models requires spectral preprocessing to suppress background signatures. Biorthogonal wavelets were used to generate patterns and resulted in SVM models that produced no missed methanol detections and false detection rates of less than 0.1% when applied to prediction data not used in the development of the model. The SVM classifiers constructed with wavelet processing were compared to one based on unprocessed spectra and also to one computed with spectra preprocessed with Butterworth high-pass digital filters.     

Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements

Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.     

Real-time airborne radiation analysis and collection (RTARAC) searching for airborne species characteristic to nuclear proliferation

An effective means of detecting airborne radioactive aerosol plumes has been developed and tested on aircraft platforms. The Real-Time Airborne Radiation Analysis and Collection (RTARAC) system was mounted in the wing pod of a Navy P-3 where it sampled 20 cubic meters of air per minute on each of eleven sequentially advanced filters. A 140% intrinsic gemanium detector counted radioactive particles collected on the 15 cm circular filters in real-time. Gamma-energy spectrum and near real-time analysis of the sample were displayed on a laptop computer.     

Synthetic training sets for the development of discriminant functions for the detection of volatile organic compounds from passive infrared remote sensing data

A novel synthetic data generation methodology is described for use in the development of pattern recognition classifiers that are employed for the automated detection of volatile organic compounds (VOCs) during infrared remote sensing measurements. The approach used is passive Fourier transform infrared spectrometry implemented in a downward-looking mode on an aircraft platform. A key issue in developing this methodology in practice is the need for example data that can be used to train the classifiers. To replace the time-consuming and costly collection of training data in the field, this work implements a strategy for taking laboratory analyte spectra and superimposing them on background spectra collected from the air. The resulting synthetic spectra can be used to train the classifiers. This methodology is tested by developing classifiers for ethanol and methanol, two prevalent VOCs in wide industrial use. The classifiers are successfully tested with data collected from the aircraft during controlled releases of ethanol and during a methanol release from an industrial facility. For both ethanol and methanol, missed detections in the aircraft data are in the range of 4 to 5%, with false positive detections ranging from 0.1 to 0.3%.     

Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images

A novel approach for quantification of chemical vapor effluents in stack plumes using infrared hyperspectral imaging are presented and examined. The algorithms use a novel application of the extended mixture model to provide estimates of background clutter in the on-plume pixel. These estimates are then used iteratively to improve the quantification. The final step in the algorithm employs either an extended least-squares (ELS) or generalized least-squares (GLS) procedure. It was found that the GLS weighting procedure generally performed better than ELS, but they performed similarly when the analyte spectra had relatively narrow features. The algorithms require estimates of the atmospheric radiance and transmission from the target plume to the imaging spectrometer and an estimate of the plume temperature. However, estimates of the background temperature and emissivity are not required which is a distinct advantage. The algorithm effectively provides a local estimate of the clutter, and an error analysis shows that it can provide superior quantification over approaches that model the background clutter in a more global sense. It was also found that the estimation error depended strongly on the net analyte signal for each analyte, and this quantity is scenario-specific.     

Fluorescence detection system for capillary separations utilizing a liquid core waveguide with an optical fibre-coupled compact spectrometer

A fluorescence detection system for capillary liquid separation methods is described. The system is based on a silica capillary coated with a low refractive index fluoropolymer Teflon AF that serves both as a separation channel and as a liquid core waveguide (LCW). A fibre-coupled laser excites separated analytes in a detection point and arising fluorescence is collected at one end of the LCW capillary into the other optical fibre which brings it to a compact charge-coupled device (CCD) array spectrometer installed in a desktop computer. No additional components such as focusing optics or filters are necessary. This system was used for detecting isoelectrically focused fluorescent low-molecular-mass pI (isoelectric point) markers and fluorescein isothiocyanate (FITC) labelled proteins. The ability of the system to acquire fluorescent spectra is also demonstrated.     

The use of MS classifiers and structure generation to assist in the identification of unknowns in effect-directed analysis

Structure generation and mass spectral classifiers have been incorporated into a new method to gain further information from low-resolution GC-MS spectra and subsequently assist in the identification of toxic compounds isolated using effect-directed fractionation. The method has been developed for the case where little analytical information other than the mass spectrum is available, common, for example, in effect-directed analysis (EDA), where further interpretation of the mass spectra is necessary to gain additional information about unknown peaks in the chromatogram. Structure generation from a molecular formula alone rapidly leads to enormous numbers of structures; hence reduction of these numbers is necessary to focus identification or confirmation efforts. The mass spectral classifiers and structure generation procedure in the program MOLGEN-MS was enhanced by including additional classifier information available from the NIST05 database and incorporation of post-generation ‘filtering criteria’. The presented method can reduce the number of possible structures matching a spectrum by several orders of magnitude, creating much more manageable data sets and increasing the chance of identification. Examples are presented to show how the method can be used to provide ‘lines of evidence’ for the identity of an unknown compound. This method is an alternative to library search of mass spectra and is especially valuable for unknowns where no clear library match is available.     

Beurteilungskriterien für Klassifikatoren zur automatischen Spektreninterpretation (pattern recognition)

In recent years numerous methods of pattern recognition have been tested for automatic interpretation of physicochemical data. Classifiers have been used successfully, especially with low resolution mass spectra. However, judgement of spectral classifiers (‘percentage of correctly classified spectra’) was often mathematically insufficiently defined. In this paper basic principles of the probability theory and information theory are used to derive objective criteria for binary classifiers. A classifier is an algorithm that uses a pattern vector (mass spectrum) and a priori probabilities for the classes (chemical structures) to which this vector belongs; the classification results are a posteriori probabilities for the classes. Predictive abilities for both classes or the information gain are suitable, objective criteria, to compare classifiers. Mathematical formulae are given and explained by examples from mass spectrometry.     

Hyphenation of a near-infrared Echelle spectrometer to a microplasma for element-selective detection in gas chromatography

The coupling of a near-infrared Echelle spectrometer (NIRES) with a gas chromatograph for element-selective detection is introduced. The miniaturized capacitive plasma device is operated at a frequency of 40.68 MHz and is mounted directly on an Hewlett-Packard HP6890 GC. First results with a mixture of halogenated standard compounds are presented and discussed in terms of the advantages and problems with this system.     

Suppressing the charged coupled device noise in univariate thin-layer videoscans: A comparison of several algorithms

The digital processing of chromatographic thin-layer plate images has increasing popularity among last years. When using a camera instead of flatbed scanner, the charged coupled device (CCD) noise is a well-known problem—especially when scanning dark plates with weakly fluorescing spots. Various techniques are proposed to denoise (smooth) univariate signals in chemometric processing, but the choice could be difficult. In the current paper the classical filters (Savitzky–Golay, adaptive degree polynomial filter, Fourier denoising, Butterworth and Chebyshev infinite impulse response filters) were compared with the wavelet shrinkage (31 mother wavelets, 3 thresholding techniques and 8 decomposition levels). The signal obtained from 256 averaged videoscans was treated as the reference signal (with noise naturally suppressed, which was found to be almost white one). The best choice for denoising was the Haar mother wavelet with soft denoising and any decomposition level larger than 1. Satisfying similarity to reference signal was also observed in the case of Butterworth filter, Savitzky–Golay smoothing, ADPF filter, Fourier denoising and soft-thresholded wavelet shrinkage with any mother wavelet and middle to high decomposition level. The Chebyshev filters, Whittaker smoother and wavelet shrinkage with hard thresholding were found to be less efficient. The results obtained can be used as general recommendations for univariate denoising of such signals.     

The Kalman filter in analytical chemistry

During the past ten years, the means by which more information can be extracted from experimental data have become an important area of research in analytical chemistry. Digital filters have been demonstrated to have a number of applications to analytical problems. These techniques typically involve a least-squares fit of experimental data to some model of the process being filtered. One method for filtering experimental data is based on the Kalman filter, a recursive, linear digital filter first developed for use in navigation, but now used in many fields. This paper discusses the implementation of Kalman filters in analytical chemistry. The principles of state-space digital filtering are reviewed, and the development of state/space models is discussed. Discussion is focused on the discrete Kalman algorithms. Two examples are provided to demonstrate the operation of the discrete Kalman filtering algorithm. Similarities between Kalman filtering and weighted least-squares methods are considered, and the specific advantages and disadvantages of linear and nonlinear Kalman filtering approaches are evaluated. To illustrate the range of problems which benefit from use of the filter, a comprehensive literature survey of the application of Kalman filtering to chemical problems is provided.     

Application of digital filters to process data for ion mobility spectrometry

Digital filters can be used to remove interferent and background spectra features from ion mobility spectra. A finite impulse response digital filter is used to improve the detection limit of bis-2chloroethyl sulfide when it is in the presence of an interferent species. Preliminary investigations on the use of narrow-bandpass digital filters for extracting signals of interest from other signals in IMS spectra are described.     

The homogeneity of heavy metal deposition on glass fibre filters collected using a high-volume sampler in the vicinity of an opencast chrome mine complex at Kemi,Northern Finland

The homogeneity of heavy metal (Cr, Ni, Cu, Fe and Cd) distribution on glass fibre filters (Munktell MG 160, 203 x 254 mm, 75 g m(-2)) collected using a high-volume sampler (Wedding & Associates) at an opencast chrome mine complex at Kemi, Northern Finland was studied. The heavy metals in the total suspended particulate (TSP) material were analysed by inductively coupled plasma atomic emission spectrometry (ICP-AES) or graphite furnace atomic absorption spectrometry (GFAAS). The glass fibre filters were digested in a microwave oven using a mixture of aqua regia+HF acids. There was significant non-uniform distribution of heavy metals on glass fibre filters. The TSP material containing chromite was very difficult to dissolve by acid digestion. The results from X-ray fluorescence spectrometry (XRF), and from energy filtering transmission electron microscope (EFTEM) equipped with energy dispersive X-ray spectrometer (EDS), showed that insoluble residue left after microwave oven digestion with aqua regia+HF acids was probably partly due to chemical reactions occurring during microwave heating.     

Merits of random forests emerge in evaluation of chemometric classifiers by external validation

Real-world applications will inevitably entail divergence between samples on which chemometric classifiers are trained and the unknowns requiring classification. This has long been recognized, but there is a shortage of empirical studies on which classifiers perform best in ‘external validation’ (EV), where the unknown samples are subject to sources of variation relative to the population used to train the classifier. Survey of 286 classification studies in analytical chemistry found only 6.6% that stated elements of variance between training and test samples. Instead, most tested classifiers using hold-outs or resampling (usually cross-validation) from the same population used in training. The present study evaluated a wide range of classifiers on NMR and mass spectra of plant and food materials, from four projects with different data properties (e.g., different numbers and prevalence of classes) and classification objectives. Use of cross-validation was found to be optimistic relative to EV on samples of different provenance to the training set (e.g., different genotypes, different growth conditions, different seasons of crop harvest). For classifier evaluations across the diverse tasks, we used ranks-based non-parametric comparisons, and permutation-based significance tests. Although latent variable methods (e.g., PLSDA) were used in 64% of the surveyed papers, they were among the less successful classifiers in EV, and orthogonal signal correction was counterproductive. Instead, the best EV performances were obtained with machine learning schemes that coped with the high dimensionality (914–1898 features). Random forests confirmed their resilience to high dimensionality, as best overall performers on the full data, despite being used in only 4.5% of the surveyed papers. Most other machine learning classifiers were improved by a feature selection filter (ReliefF), but still did not out-perform random forests.     

Feasibility of Rapid Diagnosis of Colorectal Cancer by Near-Infrared Spectroscopy and Support Vector Machine

The feasibility of diagnosing colorectal cancers based on the combination of near-infrared (NIR) spectroscopy and supervised pattern recognition methods was investigated. A total of fifty-eight colorectal tissues were collected and prepared. The spectra were first preprocessed by standard normalize variate (SNV) and first derivatives of Savitzky-Golay polynomial filter for removing unwanted background variances. The information of CH-stretching overtones and combination regions proved to be the most valuable. Four pattern recognition methods including K-nearest neighbor classifier (KNN), perceptron, Fisher discriminant analysis (FDA), and support vector machine (SVM) were used for constructing classifiers. In terms of the total accuracy, sensitivity and specificity, the SVM classifier achieved the best performance; the sensitivity and specificity were 92.8% and 86.7%, respectively. These findings suggest that NIR spectroscopy offers the possibility of constructing a simple, feasible and sensitive method for diagnosing colorectal cancer, avoiding the need of laborious visual inspection from experts.     

Absolute characterization of laser-induced breakdown spectroscopy detection systems

A quantitative comparison of the performance of four different laser-induced breakdown spectroscopy detection systems is presented. The systems studied are an intensified photodiode array coupled with a Czerny–Turner spectrometer, an intensified CCD coupled with a Czerny–Turner spectrometer, an intensified CCD coupled to an Echelle spectrometer, and a prototype multichannel compact CCD spectrometer system. A simple theory of LIBS detection systems is introduced, and used to define noise-equivalent spectral radiance and noise-equivalent integrated spectral radiance for spectral detectors. A detailed characterization of cathode noise sources in the intensified systems is presented.     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.     

Study of the spectrum of cherenkov light

It is pessible to study experimentally Cherenkov light spectra in a liquid scintillation spectrometer with colour filters. Cherenkov light spectra of⁶⁰Co,¹⁹⁸Au,¹¹⁵Cd,¹⁴³Ce,¹⁴⁰La,³²P,⁸⁶Rb,⁷⁶As,⁴²K and an external standard were studied in a Packard 3375 type liquid scintillation spectrometer, using 11 various Kodak Wratten filters. The absorption maxima of the filters ware in the 410–796 nm interval.     

Support vector regression with digital band pass filtering for the quantitative analysis of near‐infrared spectra

This paper proposes a novel calibration technique based on combining support vector regression with a digital band pass (DBP) filter for the quantitative analysis of near‐infrared spectra. The efficacy of the proposed method is investigated and validated in the determination of glucose from near‐infrared spectra of a mixture composed of urea, triacetin and glucose. In this paper, the DBP filtering was implemented as a pre‐processing technique in the frequency domain as a Gaussian band pass filter and in the time domain as a Chebyshev filter. The grid‐search optimization method was used to optimize the filter parameters. The results demonstrate that utilization of the optimized DBP filters as a pre‐processing technique improved the performance of the predictive models. Copyright © 2013 John Wiley & Sons, Ltd.     

Functional group screening of complex mixtures with a double quadrupole mass spectrometer

A double quadrupole mass spectrometer is used to obtain mass spectra which represent only those species in a complex mixture which undergo fragmentation with loss of a constant neutral moiety. Examples are given where these spectra for Brxxx loss and NO2xxx loss allow recognition of bromo compounds and the nitro functional group in complex mixtures. The method is easier to implement than the corresponding scans on asector instrument.