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Purge-and-membrane mass spectrometry (PAM-MS) is a combination of dynamic headspace sampling and membrane extraction. A new and simple purge-and-membrane sampler is introduced and its basic testing results for the analysis of VOCs in soil samples are reported. Soil moisture had no effect on desorption times in the case of sand, but the desorption times increased when the content of organic matter in the soil sample (garden soil) increased. The longest desorption times were measured with dry garden soil samples. For both types of samples, minor differences in desorption peak areas were observed between 10 and 20% moisture. Detection limits of the VOCs varied in the range 2-150 microg/kg, depending on the soil type. Good linearity (correlation coefficient > 0.990) was observed in the range 0.5-50 mg/kg. Aging of the spiked soil samples had only a slight effect on desorption peak areas for samples stored at 5 degrees C up to two weeks, but after six months of storing, differences were observed between dry sand and moistened garden soil. In both cases, peak areas were diminished. On average, 46% of compounds could be desorbed from the aged sand and 86% from the aged garden soil. The modified vapor fortification method was used in preparing standard soil samples, which were analyzed by static headspace gas chromatography (HSGC) and PAM-MS. Some authentic soil samples were also analyzed using both of these techniques. Many of the vapor fortification samples and the authentic samples were also analyzed in another laboratory by HSGC. The agreement between the methods and the laboratories was generally good.

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This paper proposes a new maximum likelihood approach for the deconvolution of identity and quantity of individual compounds based on the multicomponent mass spectra measured by mass spectrometry (MS). Mixture analysis of multicomponent mass spectra is, typically, based on a linear multicomponent mass spectrum model, where the compounds of the measured spectra to be solved are explicitly stated and assumed to be known. In many cases, however, the measured spectrum may contain unknown compounds that are not explicitly stated in the model and a commonly used least square (LS) solution fails. Moreover, a standard improvement over the LS method in these cases, namely the M-estimation (ME) approach, also suffers from this same problem. Our method overcomes the limitations of the LS and ME methods by modeling the effect of the unknown compound(s) to the residual of the linear model. The experimental results presented show that this new approach can separate more robustly the complex multicomponent mass spectra into their individual constituents compared to the LS and ME methods.