thesis
NIR spectroscopy for personal screening
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Abstract
This work presents investigations into the use of the near-infrared (NIR) signals to
interrogate, detect and image specific chemical compounds of interest in a security
screening application, including when such compounds are hidden behind single layers
of clothing fabric.
In an initial set of experiments, the mechanisms governing the interaction of NIR
signals with clothing fabrics and similar materials has been studied, in order to account
for the influence of fabric layers when detecting hidden chemicals. Throughout the rest
of the work, NIR spectroscopy has been used as a means to perform qualitative and
quantitative analysis, in order to detect the presence of chemicals, and quantify the
concentration in aqueous solution of liquids.
It has been shown that, while the compounds can be identified on the basis of the
characteristic features that appear in the relevant NIR spectra, the origin and nature of
these spectra necessitate that such identification be performed with a chemometricsbased
approach. Accordingly, multivariate calibration models based on neural networks
and partial least squares regression (PLSR) have been developed to perform the
requisite analyses. Results of calibration and testing with a range of data are reported.
In order to facilitate operation in practical security screening, the development and
testing of a software-based lock-in amplifier is reported, as a mean to enhance the
signal-to-noise ratio (SNR) of the spectral data. It is shown that the amplifier can
process up to 40 wavelength channels in parallel, to extract the spectral data buried in
noise in each channel. Hence, with the SNR of the input signal set as low as -60 dB (by
introducing software-generated additive white noise in the spectra), adequate noise
suppression has been obtained, allowing the resulting spectral data to be used for
requisite chemical detection.
Finally, an integrated spectroscopic imaging application is developed to perform twodimensional
cross-sectional scans of chemical samples, carry out lock-in amplification
of the recorded intensity spectra, and plot the results of neural network-based chemical
detection in the form of intensity images colour-coded to depict the presence of the
pertinent chemicals at the scanned coordinates