An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

Three-dimensional cluster resolution for guiding automatic chemometric model optimization.

A three-dimensional extension of a previously developed metric termed cluster resolution is presented. The cluster resolution metric considers confidence ellipses (here three-dimensional confidence ellipsoids) around clusters of points in principal component or latent variable space. Cluster resolution is defined as the maximum confidence limit at which confidence ellipses do not overlap and can serve to guide automated variable selection processes. Previously, this metric has been used to guide variable selection in a two-dimensional projection of data. In this study, the metric is refined to simultaneously consider the shapes of clusters of points in a three-dimensional space. We couple it with selectivity ratio-based variable ranking and a combined backward elimination/forward selection strategy to demonstrate its use for the automated optimization of a six-class PCA model of gasoline by vendor and octane rating. Within-class variability was artificially increased through evaporative weathering and intentional contamination of samples, making the optimization more challenging. Our approach was successful in identifying a small subset of variables (644) from the raw GC-MS chromatographic data which comprised ≈ 2 × 10(6) variables per sample. In the final model there was clear separation between all classes. Computational time for this completely automated variable selection was 36 h; slower than solving the same problem using three two-dimensional projections, but yielding an overall better model. By simultaneously considering three dimensions instead of only two at a time, the resulting overall cluster resolution was improved.

Observation of increases in emission from modern vehicles over time in Hong Kong using remote sensing.

In this study on-road gaseous emissions of vehicles are investigated using remote sensing measurements collected over three different periods. The results show that a high percentage of gaseous pollutants were emitted from a small percentage of vehicles. Liquified Petroleum Gas (LPG) vehicles generally have higher gaseous emissions compared to other vehicles, particularly among higher-emitting vehicles. Vehicles with high vehicle specific power (VSP) tend to have lower CO and HC emissions while petrol and LPG vehicles tend to have higher NO emissions when engine load is high. It can be observed that gaseous emission factors of petrol and LPG vehicles increase greatly within 2 years of being introduced to the vehicle fleet, suggesting that engine and catalyst performance deteriorate rapidly. It can be observed that LPG vehicles have higher levels of gaseous emissions than petrol vehicles, suggesting that proper maintenance of LPG vehicles is essential in reducing gaseous emissions from vehicles.

Comparison of real-world emissions from two-wheelers and passenger cars.

Passenger cars are the primary means of transportation in Europe. Over the past decade, a great deal of attention has therefore been paid to reducing their emissions. This has resulted in notable technical progress, leading to unprecedentedly low exhaust emissions. In the meantime, emissions from motorcycles have been ignored due to their subordinate role in traffic. Even though the motorcycle fleet is small in comparison with the car fleet, and logs lower yearly mileage per vehicle, their contribution to traffic emissions has become disproportionately high. Exhaust emissions of CO, HC, NOx, and CO2 from 8 powered two-wheelers were measured and compared to previous measurements from 17 gasoline-powered passenger cars performed at EMPA with the aim of ascertaining their relevance. Using exhaust emission ratios from both vehicle types, comparisons based on mean unit, mean yearly, and fleet emissions are considered. Present-day aftertreatment technologies for motorcycles are not as efficient as those for cars. A comparison of mean unit emissions shows that motorcycles exceed cars in NOx emissions. All comparisons reveal a significant HC ratio, to the detriment of two-wheelers. Overall, the relevance of emissions from powered two-wheelers is not negligible when compared with modern gasoline-powered passenger cars.

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis.

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.

Cold temperature PM emissions measurement: method evaluation and application to light duty vehicles.

This work examines the methodology to sample and measure the number and size of motor vehicle particulate emissions (PM) at subambient temperatures. The study has two principal objectives. The first is to address the following question: which aspects of the particle sampling, dilution, and size measurement process must be made at the vehicle test temperature to obtain an accurate representation of the PM emissions? The second is to perform a preliminary overview of how subambient temperature operation affects PM emissions from the major classes of current model light duty vehicles. The principal findings are the following: (1) The temperature of the particle size instruments, test cell versus room temperature, has little effect on the measurements. (2) Once the engine has warmed, solid particle (soot) mode emissions in the cold test cell are similar to those at room temperature. The first finding simplifies cold temperature emissionstesting because it allows particle sizing instruments to be placed outside the cold test cell and operated at room temperature. The latter is consistent with the expectation that solid particles are formed in the engine and are therefore relatively unaffected by ambient conditions after engine warm-up. Use of cold dilution air in the room-temperature test cell increases the number and size of nuclei particles; however, the effect of dilution airtemperature was inconclusive in the cold test cell.

Fuel identification by neural network analysis of the response of vapor-sensitive sensor arrays.

Neural network analysis of the response of an array of vapor-sensitive detectors has been used to identify six different types of aviation fuel. The data set included 96 samples of JP-4, JP-5, JP-7, JP-8, JetA, and aviation gasoline (AvGas). A sample of each neat fuel was injected into a continuous stream of breathing air through an injection port from a gas chromatograph. The aspirated sample was then swept from the injection port to the chamber without separation. In the chamber, the sample was exposed to an array of eight vapor-sensitive detectors. The analog output of each detector was digitized and stored while the sample was swept into and through the chamber. The response of each detector was then averaged and stored as the final response or pattern of each sample. It was clear from a visual inspection of each of the radar plots that there was a characteristic pattern in the response of the array to five of the six different fuel types. This was confirmed using neural network analysis to study the entire data set. A two-step procedure was developed to separate the patterns of all six fuel tyes into their respective classes. In the first step, fuels were separated into one of five groups: JP-4, JP-5, JP-7, AvGas, or a combined JP-8/JetA group. In the second step, the fuels in the combined group were separated into either JP-8 or JetA groups.

Experimental evaluation of the effect of filtration of diesel exhaust by biologic exposure indicators.

The airway resistance, compliance of the respiratory system, transfer factor, and alveolar volume of 33 healthy rabbits were studied before and after exposure to diluted diesel exhaust generated in an experimental motor. Three diesel fuels and two particle traps were tested. Subsequent to the post-exposure lung function measurements, the animals were sacrificed and the lungs were processed for morphologic examination. The concentrations of particles, nitrogen dioxide, and formaldehyde were measured. The inflammatory airway changes were most pronounced in animals exposed to exhaust from standard fuel. Small changes were identified in animals exposed to exhaust filtered through the catalytic trap as well or exposed to unfiltered exhaust from fuels intended for densely built-up areas. Increase in compliance of the respiratory system was associated with the concentration of soot particles and formaldehyde. Compliance decreased significantly in animals exposed to exhaust from standard fuel filtered through the particle traps and increased almost significantly in animals exposed to unfiltered exhaust from the same fuel.