We have analyzed the properties of dust in the high galactic latitude translucent cloud Lynds 1780 using ISOPHOT maps at 100 µm and 200 µm and raster scans at 60 µm, 80 µm, 100 µm, 120 µm, 150 µm and 200 µm. In far-infrared (FIR) emission, the cloud has a single core that coincides with the maxima of visual extinction and 200 µm optical depth. At the resolution of 3.0 , the maximum visual extinction is 4.0 mag. At the cloud core, the minimum temperature and the maximum 200 µm optical depth are 14.9 ± 0.4 K and 2.0 ± 0.2 × 10 −3 , respectively, at the resolution of 1.5 . The cloud mass is estimated to be 18 M . The FIR observations, combined with IRAS observations, suggest the presence of different, spatially distinct dust grain populations in the cloud: the FIR core region is the realm of the "classical" large grains, whereas the very small grains and the PAHs have separate maxima on the Eastern side of the cold core, towards the "tail" of this cometary-shaped cloud. The color ratios indicate an overabundance of PAHs and VSGs in L1780. Our FIR observations combined with the optical extinction data indicate an increase of the emissivity of the big grain dust component in the cold core, suggesting grain coagulation or some other change in the properties of the large grains. Based on our observations, we also address the question, to what extent the 80 µm emission and even the 100 µm and the 120 µm emission contain a contribution from the small-grain component.

Context. Lynds 1780 is a high-latitude cloud where, based on 2MASS, the maximum visual extinction is A max V = 4 mag at a resolution of 3 . In LDN1780, increased far-infrared (FIR) emissivity of dust grains has been observed, and the infrared emission is found to peak at different locations at different wavelengths. Aims. By modelling the FIR observations, we try to quantify spatial variations of dust properties and to determine to what extent the observations could be affected by the asymmetry of the heating radiation field. Methods. We have constructed a three-dimensional cloud model and, with the help of radiative transfer calculations, compare its predictions with the FIR surface brightness measurements of LDN1780 performed with the ISO satellite. The effects of anisotropic radiation, its attenuation in a diffuse extinction layer around the cloud, and variations in the dust properties are investigated. Results. Asymmetry of the radiation field is found to have only a small effect on the morphology of mid-and far-infrared surface brightness. The general agreement between observations and the model predictions is improved by assuming the presence of a low extinction external layer with A V ∼ 0.25 mag. However, to explain the changes in the relative intensity of mid-and far-infrared bands, one has to assume strong variations in the relative abundance of small and large grain components and, at the very centre of the cloud, enhanced emissivity of large grains. Conclusions. The separate emission maxima at different wavelengths in LDN1780 result from real variations in spatial distributions of dust components. Modifications to standard dust models, including a 30% increase in the FIR emissivity, are needed to explain the far-infrared observations towards the centre of LDN1780. The relative abundances of dust components are found to be very sensitive to the strength of the external radiation field.

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.

The aim of this study was to evaluate the benefits of the simultaneous use of two different analytical methods, namely Fourier transform infrared spectroscopy (FTIR) and mass spectrometry (MS), for online analysis of environmental and process samples. A mathematical method (NALMS) that identifies and quantifies all single components from a single multicomponent spectrum was previously developed for MS, and in this study, the same method, named as SPECTACS, was adopted for solving also an FTIR spectrum and a combined FTIR-MS spectrum. The performance of SPECTACS was evaluated by analyzing various gaseous samples, as case studies, containing volatile organic compounds, and the performance was compared with other methods, which are used to identify and quantitate organic compounds from multicomponent spectra. The results obtained show that SPECTACS with optimized noise reduction and solving a combined FTIR-MS spectrum can increase the reliability of identifying components in a single spectrum and also the accuracy in quantitative measurements when compared to the analysis with one analytical technique alone. The reasons for this improvement is evaluated and discussed in detail.