In view of the emergent threat of terrorist activities, and the increasing importance of robust and effective scanning mechanisms to detect and categorize harmful and dangerous substances that might be used in improvised explosive device, several scanning technologies have been investigated and tested in scanning systems at vulnerable locations such as airports. It is, however, felt that these current scanning techniques have certain inherent limitations, which might be overcome with the use of NIR spectroscopy in such an application. In this context, this work presents an investigation into the interaction of NIR wavelengths with a range of clothing fabrics, and the use of NIRS to detect and image specific chemical compounds alone and hidden behind such fabrics.
The use of near infrared spectroscopy in various industrial applications has grown in popularity in recent decades, and it has been adopted as the technique of choice for multifarious process monitoring and quality control tasks. This has been due in part to the breadth of compositional and structural information available in the NIR overtone and combination spectra of pertinent test samples, the inexpensive and portable instrumentation available to perform NIRS, and the high data throughput rates achievable with this technique.
NIRS falls under the purview of vibrational spectroscopy, with the origin of spectra that appear in this region traceable to the anharmonic vibrations that take place in the molecular bonds of test samples: as NIR energy at specific characteristic wavelengths is absorbed in these bonds, it prompts overtone and combination transitions that excite vibrations at higher energy levels. The lack of specificity and redundancy encountered in the resultant absorbance spectra, however, necessitate a chemometrics-based approach to glean useful information about the qualitative and quantitative structure of
6.1.2 Experiments on NIR transmission
In a preliminary set of investigations, the mechanism of interaction between NIR energy and materials such as sheets of paper, polystyrene foam and various types of clothing fabrics was studied. The results of through-transmission experiments with an 850 nm NIR laser diode across a range of material samples revealed that the magnitude and spatial distribution of through-transmitted energy was dependent on factors such as thickness of the material, porosity of fabric samples, spatial distribution of the pores, and surface texture of the samples. Particular attention was paid to investigating the effect of varying these parameters in fabric samples on the spatial distribution and intensity of resulting signal energy. It was concluded that, while the magnitude of through-transmitted signal was directly proportional to the overall porosity of the fabric sample, the layout of the radiation pattern as it emerged on the far side of the sample was determined by the distribution and size of individual pores. This was further investigated by using a special type of test samples, comprising different varieties of metallic TEM grids. These had a uniform thickness (~9 µm) and diameter (~3.05 mm), while the pore sizes varied from around 6 µm to 100 µm. The radiation pattern recorded with the largest pores did not show any evidence of scattering, while the one recorded with the smallest pore sizes had clear effects of scatter manifested in the form of diffraction patterns with side lobes etc.
Additional experiments were performed with wet fabric samples, and samples dyed in various colours. It was revealed that an increase in the moisture content of the sample resulted in increased through-transmitted signal levels, which was attributed to the reduced optical impedance of the sample due to the water content, as well as greater optical homogeneity of the surface texture with the water filling in the pores. The different coloured dyes, however, had minimal impact on the resulting signal levels. It was, therefore, concluded that fabric materials with larger pores and a more uniform, even and less chaotic surface texture would allow higher signal magnitudes to be transmitted, while the colour of the fabric would not impact NIR through-transmission in any significant manner.
Following on from the above investigations at a single NIR wavelength, instrumentation was designed to perform spectroscopic measurements utilizing the entire range of
wavelengths (850 nm – 2,200 nm) in the NIR region. The optical bench, which was comprised of commercial off-the-shelf components, was designed to record measurements at a standoff distance of 3 m to test the operability of such an arrangement in a practical security screening environment. Measurements were made in diffuse reflectance mode with solids, and in transflectance mode with liquids, using a diffuse PTFE reflector in the latter case.
The nature of the interaction between NIR energy and fabrics, followed by solid chemical powders comprising different ammonium salts, and aqueous solutions of ethanol, hydrogen peroxide and ammonium nitrate was investigated. The results with fabrics showed characteristic absorbance spectra with features specific to each fabric type. The amplitude of these features was found to be relatively low at wavelengths up to 1,400 nm, which facilitated the use of the range between 850 nm – 1,400 nm for locating the characteristic spectra of chemicals and, if found to be present, announce detection of the relevant samples hidden behind such fabrics.
It was observed that collecting absorbance spectra of chemicals, with reference to the intensity spectra of the intervening fabric layers, allowed improved discernment of spectral features characteristic of the chemicals. The spectra collected in this manner for different ammonium salts in powdered form had remarkably similar features owing to the presence of the ammonium ion in each case, while the spectra of aqueous solutions of different chemicals also contained features specific to the chemicals. An interesting feature that emerged in the spectra of solid chemicals as well as aqueous solutions collected in the presence of different fabric layers including polyester, cotton, acrylic and wool, was the similarity in the recorded spectral profiles with features characteristic of the relevant chemicals, irrespective of the type of fabric used. This was deemed to be a significant finding, as it allowed for the possibility of detecting chemicals based on a standardized set of features for each chemical, applicable to all types of concealing fabrics.
6.1.3 The need for chemometrics-based approach
amplitudes recorded in the presence of, and with reference to, fabric layers were around 20 times less than those recorded with the chemicals alone. Additionally, certain characteristic features observed in the spectra of the chemicals alone were either absent or largely diminished in the presence of fabric layers, especially at wavelengths where such features coincided with those of the fabrics. On account of such inconsistencies, a chemometrics-based approach to prediction modelling was deemed to be essential, in order to base the requisite calibration models on multivariate data rather than relying on a univariate approach.
Prediction modelling was thus undertaken to detect the presence of hidden chemicals through pattern recognition/ classification, using neural network-based calibration models. Specific data pre-processing routines and training algorithms were instituted to ensure optimal training of the neural networks, while preventing overfitting and ensuring adequate generalization capability to allow the model to extrapolate beyond the training domain. This extrapolation capability was specifically tested by presenting test data to the model that were largely comprised of the spectra of chemicals hidden behind such fabric layers as had not been included in the training data. With 68% of the test data comprised of such spectra, the trained model correctly classified 91% of the samples.
Following neural network-based detection, a second PLSR-based model was used to quantify the detected chemical in terms of its concentration in aqueous solution. Using test spectra collected at the same standoff distance in a PLSR model calibrated to quantify hydrogen peroxide, it was concluded that concentrations above 10% in the presence of a fabric layer could be predicted within a margin of error of ±5%. Further, an outlier detection algorithm based on prediction influence plots was used to enable segregation of hydrogen peroxide samples with concentration as low as 3% (in the absence of fabric layer) from other chemical samples that might have been wrongly classified as hydrogen peroxide in the initial step.
As the quantification and outlier detection measures were obtained within the same arrangement as that used in the initial detection step, these were deemed to provide a useful add-on module to the main detection step.
A preliminary processing step was next introduced in the form of lock-in amplification of raw spectral data, with the aim to sufficiently enhance the SNR to enable recovery of spectral data buried in background noise in a practical screening environment. A through-transmission arrangement was used for this set of experiments, with a software- based lock-in amplifier designed to process multi-channel spectral data, using an optical source modulated with the reference frequency of the amplifier. The results demonstrated adequate suppression of noise when SNR was set as low as -60 dB, to enable subsequent chemical detection using neural network-based calibration models.
Additionally, two-dimensional spectroscopic imaging was performed by carrying out cross-sectional scans of test samples, and plotting the corresponding results of chemical detection in the form of intensity images colour-coded to depict the presence of specific chemicals at the scanned coordinates.
Based on all the experimental evidence gathered in the course of this work, it has been concluded that NIRS allows the functionality to detect specific chemicals from a standoff distance, when such chemicals are hidden from view underneath a layer of clothing. Furthermore, detection can be performed in the presence of relatively high levels of noise in the relevant wavelength channels, by performing lock-in amplification of the spectra to enhance the pertinent SNR.