It's necessary to remove the baseline from the spectra, which measured by open-path Fourier transform infrared spectrometry, for further spectral analysis such as qualitative and quantitative analysis. An automatic baseline correction method, the Iterative Averaging method, is presented. Baseline corrected by this method is accurate, and it also shows more precise than other methods when it is applied to Fourier Transform Infrared experimental spectra and simulated data. This method solves the key technology of the real-time on-line spectral analysis of OP-FTIR and improves the capability and adaptability of the unsupervised on-line system effectively.

Removing the baseline from the spectra, which are measured by a Fourier transform infrared spectrometer (FTIR), is an important preprocessing step for further spectra analysis such as quantitative and qualitative analysis. An automatic baseline correction method named iterative averaging, which is based on the basic knowledge of moving average, is presented. We also compared it to other methods, such as rubber band, adaptive iterative reweight penalized least squares, automatic iterative moving average, and morphological weighted penalized least squares, using simulated and experimental spectra with different signal-to-noise ratios (SNRs) to evaluate the performance of these methods by performance metrics and to select an appropriate method to analyze FTIR spectra. Performance metrics such as root-mean-square error, goodness-of-fit coefficient, and chi-square are calculated. The iterative averaging method achieves the best results, which are judged by performance metrics values, when it is applied to the FTIR spectra with different SNRs. It also can correct the baseline of the FTIR spectra automatically, and improve the capability and adaptability of the unsupervised online analysis of the FTIR system effectively.

BackgroundTo investigate the ameliorated effects of an extract of Ginkgo biloba extract (GBE) on experimental cardiac remodeling in rats induced by acute cardiac infarction, and further explore the mechanism concentrated on myocardial type I collagen, transforming growth factor beta 1 (TGF-β1), matrix metalloproteinase 2 (MMP-2) and matrix metalloproteinase 9 (MMP-9), and provide the experimentaldata for clinical application of GBE.MethodsRats were divided into five groups (n = 20) as following: sham operation group (group A), acute myocardial infarction model group (group B), acute myocardial infarction model + aspirin (10 mg/kg) treatment group (group C), acute myocardial infarction model + captopril (20 mg/kg) treatment group (group D) and acute myocardial infarction model + Ginkgo biloba extract (100 mg/kg) treatment group (group E). The rat acute myocardial infarction model was reproduced by ligaturing the left anterior descending artery excluding the sham operation group which did not ligation only completed the operational process. Each group was further subdivided into treatment regimens lasting 4 weeks and 8 weeks. Immunohistochemistry and real-time polymerase chain reaction (PCR) methods were used to detect the protein expression and mRNA transcriptional levels of rat myocardial TGF-β1, type I collagen, MMP-2 and MMP-9.ResultsCompared with group B, regardless of the length of treatment (4 or 8 weeks), the TGF-β1, MMP-2 and MMP-9 mRNA transcriptional levels, and the protein expression levels of type I collagen, MMP-2 and MMP-9 in groups D, C and E were significantly decreased (P < 0.01). Furthermore, the mRNA expression levels of TGF-β1 in groups D, C and E were significantly lower after 8 weeks compared to after 4 weeks (P < 0.01), as were the expression levels of type I collagen in groups D, C and E (P < 0.05). There was no statistically significant difference in the protein expression levels of MMP-2 and MMP-9 between groups E and C.ConclusionsGBE could inhibit experimental rat myocardial remodeling after acute myocardial infarction via reduced transcription of TGF-β1, MMP-2 and MMP-9 genes and by the decreased expression of type I collagen, MMP-2 and MMP-9 proteins in myocardial cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s12906-015-0719-z) contains supplementary material, which is available to authorized users.

Remote sensing imaging technology is one of the most powerful tools for gas leak monitoring in chemical industrial parks. In the case of leaks, it is necessary to quickly and accurately obtain detailed information of the gas cloud (volume, distribution, diffusion situation and location). This paper proposes a 3-D quantitative reconstruction method for gas clouds. Two scanning Fourier transform infrared (FTIR) remote-sensing imaging systems were used to perform telemetry experiments in a monitored space with a total volume of 314.9 m3, and the released gases were SF6 and CH4. One scanning FTIR remote-sensing imaging system can only measure a 2-D concentration-path-length product (CL) image of a 3-D gas cloud, where each pixel has attitude information of elevation and azimuth. Geometric methods are applied to locate the monitored space and construct a 3-D grid (longitude, latitude, altitude). The optical path length (OPL) sparse matrix of each layer is generated, and the concentration distribution of each layer is reconstructed by the simultaneous algebraic reconstruction technique (SART). The reconstructed results of each layer are stacked into a 3-D gas cloud and displayed on the 3-D Earth software at a set threshold. Three-dimensional leaking gas clouds (CH4, SF6) with geometric information and concentration distribution has been generated through the above processes from measurement, localization to reconstruction and display. On the premise that the gas cloud is completely covered by the field of view of each scanning system, the localization and quantification of the gas cloud is available. Then weighted concentration centers can be calculated from these gas clouds to approximate the leak source. The proposed method effectively extends the online leak monitoring application of the scanning FTIR remote-sensing imaging system.

The concentration-path-length product (CL) image of the leaking gas cloud measured by the passive Fourier transform infrared (FTIR) scanning remote-sensing imaging system has a low resolution. Gas cloud diffusion is affected by wind speed and direction, which makes it difficult to trace the source of a leakage. Therefore, we propose a method to reconstruct the CL image of the leaking gas cloud applied to the passive FTIR scanning remote-sensing imaging system. First, bicubic interpolation is employed to upsample the low-resolution CL image of gas clouds. Second, the maximum noise-equivalent concentration-path-length (NECL) product is used as a threshold to segment the high-resolution gas cloud image. Third, image morphology processing and the evaluation criteria of the leaking gas cloud are applied to detect the leaking gas cloud. Finally, the high-resolution CL image of the leaking gas cloud is superimposed onto the background image. The effectiveness of the reconstruction method is proven by the S F 6 remote-sensing experiment and simulation. The results show that the proposed method should be effectively implemented to reconstruct the high-resolution CL image of the leaking gas cloud. The reconstructed leaking gas cloud plume, as well as the location of the leakage source, are quite obvious. The reconstruction method has been successfully applied to passive FTIR scanning remote-sensing imaging systems, with high accuracy, in real time, and with robustness.