The objective of this thesis is to develop a low-cost infrared absorption spectroscopebased on linear variable filter (LVF) technology for the automated detection of gases andvapors, and the semi-automated detection of liquids. This instrument represents analternative to electronic-nose instruments based on cross-selective gas sensor arrays.Instead, the proposed instrument uses the idea of computational ?pseudo-sensors?,whereby spectral lines in an analytical instrument are clustered into groups and used asindependent variables. We characterize the system on a number of performance metrics,uncovering its detection limits and resolving power. We present calibration methods toestimate the concentration of analytes in a matrix of absorbing species, as well as signalprocessing techniques for spectral classification. Specifically, we validate the instrumenton a mixture calibration problem with simple and complex chemicals, and compare theefficiency of different calibration methods to estimate the concentration of one analyte inthe matrix. Moreover, we demonstrate the use of the instrument on two real-worldapplications in the foodstuffs domain: oil adulteration and trans fatty acid (TFA)detection. The instrument, combined with signal processing techniques, is able to fully discriminate oils, as well as classify margarine and spreads onto high-TFA and low-TFAgroups. Despite operating at a low spectral resolution, our results show that the LVFbased spectroscope is a promising alternative to traditional analytical techniques forselected niche applications.
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