Calibration transfer algorithm for automated qualitative analysis by passive Fourier transform infrared spectrometry

The automated qualitative analysis of passive Fourier transform infrared (FT-IR) remote sensing data is made difficult by the presence in the data of background and instrument-specific variation. For data collected with a single instrument, variation in the data arises from changes in the infrared background radiance, changes in the atmospheric composition within the field-of-view of the spectrometer, and changes in the instrument response function arising from temperature variation in the spectrometer. When more than one spectrometer is used, the variation in detector responses and phase signatures between instruments serves to complicate further the task of implementing an automated processing algorithm for detecting the signature of a target compound. In this work, a combination of signal processing and pattern recognition methodology is applied directly to the interferogram data collected by the FT-IR spectrometer to implement an automated compound detection procedure that is independent of background and instrument-specific variation. The key to this algorithm is the use of highly attenuating digital filters to isolate in the interferogram the frequencies associated with an analyte absorption or emission band while suppressing information at other frequencies. For the test compounds, acetone and sulfur hexafluoride, it is demonstrated that when this digital filtering procedure is coupled with either piecewise linear discriminant analysis or a back-propagation neural network, an automated detection algorithm can be developed with data from a primary instrument and then subsequently used to predict the presence of analyte signatures in data collected with a secondary spectrometer. Correct classification rates in excess of 92% are obtained for both compounds when the algorithm is applied to data collected with the secondary instrument.     

Remote detection of heated ethanol plumes by airborne passive Fourier transform infrared spectrometry

Methodology is developed for the automated detection of heated plumes of ethanol vapor with airborne passive Fourier transform infrared spectrometry. Positioned in a fixed-wing aircraft in a downward-looking mode, the spectrometer is used to detect ground sources of ethanol vapor from an altitude of 2000-3000 ft. Challenges to the use of this approach for the routine detection of chemical plumes include (1) the presence of a constantly changing background radiance as the aircraft flies, (2) the cost and complexity of collecting the data needed to train the classification algorithms used in implementing the plume detection, and (3) the need for rapid interferogram scans to minimize the ground area viewed per scan. To address these challenges, this work couples a novel ground-based data collection and training protocol with the use of signal processing and pattern recognition methods based on short sections of the interferogram data collected by the spectrometer. In the data collection, heated plumes of ethanol vapor are released from a portable emission stack and viewed by the spectrometer from ground level against a synthetic background designed to simulate a terrestrial radiance source. Classifiers trained with these data are subsequently tested with airborne data collected over a period of 2.5 years. Two classifier architectures are compared in this work: support vector machines (SVM) and piecewise linear discriminant analysis (PLDA). When applied to the airborne test data, the SVM classifiers perform best, failing to detect ethanol in only 8% of the cases in which it is present. False detections occur at a rate of less than 0.5%. The classifier performs well in spite of differences between the backgrounds associated with the ground-based and airborne data collections and the instrumental drift arising from the long time span of the data collection. Further improvements in classification performance are judged to require increased sophistication in the ground-based data collection in order to provide a better match to the infrared backgrounds observed from the air.     

Robust classifier for the automated detection of ammonia in heated plumes by passive fourier transform infrared spectrometry

An automated classification algorithm is implemented for the detection of ammonia vapor in heated plumes by passive Fourier transform infrared (FT-IR) spectrometry. This classification methodology allows the real-time detection of chemical signatures in gaseous effluents such as those generated from industrial processes. The characteristics of real-time implementation and excellent robustness are achieved by an analysis strategy based on the application of band-pass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer, followed by the use of piecewise linear discriminant analysis to obtain a yes/no classification regarding the presence of the analyte signature in the filtered data. The optimal classifier developed through this work is based on only 110 interferogram points and employs a single band-pass filter centered at 945 cm(-)(1) with a pass-band full width at half-maximum of 93 cm(-)(1). The average stop-band attenuation of the optimal filter is 42.1 dB. The robustness of the algorithm is tested by exposing it to chemical releases of sulfur hexafluoride, ethanol, methanol, sulfur dioxide, and hydrogen chloride that were not included in the development of the classifier. Excellent classification performance is demonstrated, with missed ammonia detections occurring at a rate of approximately 1%. The occurrence of false detections is less than 0.1% for SF(6) and less than 0.02% for the other interferences tested.     

基于亮温光谱法的大气污染气体探测

在利用被动傅里叶变换红外光谱仪进行大气污染气体的探测识别研究时,由于作为背景的自然界的大部分物质的发射率很高,并且在700~1300cm-1范围近似为常数,因此,可利用计算具有常数基线的亮温光谱的方法来进行探测。该方法不需要预先测量背景光谱,能直接从亮温光谱中扣除掉背景及干扰信号,提取出目标。     

Bilayer free-standing beam splitter for Fourier transform infrared spectrometry

We describe the design, fabrication, testing, and performance of a two-layer free-standing beam splitter for use in far-infrared Fourier transform infrared spectrometers. This bilayer beam splitter, consisting of a low-index polymer layer in combination with a high-index semiconductor layer, has an efficiency that is higher than that of the best combination of four single-layer Mylar beam splitters currently in use for spectrometry from 50 to 550 cm(-1).     

Digital filtering implementations for the detection of broad spectral features by direct analysis of passive Fourier transform infrared interferograms

Finite impulse response (FIR) filters and finite impulse response matrix (FIRM) filters are evaluated for use in the detection of volatile organic compounds with wide spectral bands by direct analysis of interferogram data obtained from passive Fourier transform infrared (FT-IR) measurements. Short segments of filtered interferogram points are classified by support vector machines (SVMs) to implement the automated detection of heated plumes of the target analyte, ethanol. The interferograms employed in this study were acquired with a downward-looking passive FT-IR spectrometer mounted on a fixed-wing aircraft. Classifiers are trained with data collected on the ground and subsequently used for the airborne detection. The success of the automated detection depends on the effective removal of background contributions from the interferogram segments. Removing the background signature is complicated when the analyte spectral bands are broad because there is significant overlap between the interferogram representations of the analyte and background. Methods to implement the FIR and FIRM filters while excluding background contributions are explored in this work. When properly optimized, both filtering procedures provide satisfactory classification results for the airborne data. Missed detection rates of 8% or smaller for ethanol and false positive rates of at most 0.8% are realized. The optimization of filter design parameters, the starting interferogram point for filtering, and the length of the interferogram segments used in the pattern recognition is discussed.     

Remote monitoring of multi-gas mixtures by passive standoff Fourier transform infrared radiometry

The passive remote monitoring of multi-gas mixtures was experimentally investigated using Fourier transform infrared (FT-IR) radiometry. The spectral radiance data were collected using a dual-port radiometrically balanced interferometer for a variety of multi-gas plumes at a standoff distance of 60 m. Two basic sets of mixtures were studied. The first set corresponded to mixtures consisting of three gases with no overlapping spectral bands (C(2)H(2), C(2)H(4), and R14). The second set corresponded to mixtures of three gases having significant spectral overlap (C(2)H(4), R114, and R134a). For each mixture the flow rates of individual constituents were adjusted to yield specific constituent optical-density (CL) ratios. These ratios were compared to the optical-density ratios retrieved from the measured infrared radiance spectra. Results of this study indicated that for both sets of multi-gas mixtures the optical-density ratios retrieved by the passive remote monitoring technique were in good agreement with those derived from the release flow rates, provided that a simple correction scheme was introduced to compensate for the limited accuracy of the fast radiance model implemented in the monitoring algorithm.     

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.     

Artificial neural networks technology for neutron spectrometry and dosimetry

Artificial Neural Network Technology has been applied to unfold neutron spectra and to calculate 13 dosimetric quantities using seven count rates from a Bonner Sphere Spectrometer with a (6)LiI(Eu). Two different networks, one for spectrometry and another for dosimetry, were designed. To train and test both networks, 177 neutron spectra from the IAEA compilation were utilised. Spectra were re-binned into 31 energy groups, and the dosimetric quantities were calculated using the MCNP code and the fluence-to-dose conversion coefficients from ICRP 74. Neutron spectra and UTA4 response matrix were used to calculate the expected count rates in the Bonner spectrometer. Spectra and H(10) of (239)PuBe and (241)AmBe were experimentally obtained and compared with those determined with the artificial neural networks.     

火灾探测的人工神经网络方法

火灾早期探测是较复杂和有重大义意的问题，本文指出了传统火灾探测方法所存在的问题，在分析火灾实验数据的基础上，提出了一种新的火灾探测方法，即人工神经网络（ＡＮＮ）方法。由于ＡＮＮ方法具有自学习能力，能适应各种复杂条件，因此它能克服传统火灾探测方法的缺陷，摸拟实验结果也表明ＡＮＮ方法在火灾自动探测中是一种十分有效的方法     

Optimum condition of Fourier transform infrared multiple-angle incidence resolution spectrometry for surface analysis

A novel measurement technique of pure out-of-plane vibrational modes of thin films on a nonmetallic substrate has recently been proposed, which is named multiple-angle incidence resolution spectrometry (MAIRS). Since this technique could not be replaced by other conventional techniques, MAIRS was expected to be a promising tool for analysis of thin soft materials and surface adsorbates. Nevertheless, some experimental conditions have been found to be inappropriate for MAIRS, which yields incorrect results. In the present study, therefore, the problems in the technique have been investigated in terms of optics to improve the accomplishments of MAIRS. The problems have been found to have a strong relationship with optics in FT-IR, which is influenced by refractive index of the sample material and angle of incidence. In particular, optimization of the size matching of the detector surface and the infrared spot at the detector was a key to having MAIRS perform properly. It has been concluded that reliable MAIRS measurements require overfilling of the detector and a substrate with a high-refractive index.     

Advances in data processing for open-path Fourier transform infrared spectrometry of greenhouse gases

The automated quantification of three greenhouse gases, ammonia, methane, and nitrous oxide, in the vicinity of a large dairy farm by open-path Fourier transform infrared (OP/FT-IR) spectrometry at intervals of 5 min is demonstrated. Spectral pretreatment, including the automated detection and correction of the effect of interrupting the infrared beam, is by a moving object, and the automated correction for the nonlinear detector response is applied to the measured interferograms. Two ways of obtaining quantitative data from OP/FT-IR data are described. The first, which is installed in a recently acquired commercial OP/FT-IR spectrometer, is based on classical least-squares (CLS) regression, and the second is based on partial least-squares (PLS) regression. It is shown that CLS regression only gives accurate results if the absorption features of the analytes are located in very short spectral intervals where lines due to atmospheric water vapor are absent or very weak; of the three analytes examined, only ammonia fell into this category. On the other hand, PLS regression works allowed what appeared to be accurate results to be obtained for all three analytes.     

Early and rapid detection of potato's fungal infection by Fourier transform infrared microscopy

Fungi are considered serious pathogens to many plants and can cause severe economic damage. Early detection of these pathogens is very important and might be critical for their control. The available methods for detection of fungi are time consuming and not always very specific. Fourier transform infrared (FT-IR) microscopy has proved to be a reliable and sensitive method for detection of molecular changes in cells. Fungi pathogens display typical infrared spectra that differ from the spectra of substrate material such as potato. In the present study we used FT-IR microscopy for early and rapid detection of the potato fungal pathogen Colletotrichum coccodes on the surface of potato tubers. Infected potatoes with this fungal pathogen and uninfected potatoes were examined and correctly classified as infected or not infected by FT-IR microscopy at very early stages of infection when no morphological signs of infection could be seen. Unique spectral biomarkers were found in naturally infected potatoes compared to disease-free control potatoes.     

Fourier transform infrared detection in miniaturized total analysis systems for sucrose analysis

In this work, a flow system containing a micromachined lamella-type porous silicon reactor and a novel mid-IR fiber-optic flow cell were used for the enzymatic determination of sucrose in aqueous solution. The method relies on the enzymatic hydrolysis of sucrose to fructose and glucose catalyzed by β-fructosidase and on the acquisition of FT-IR spectra before and after complete reaction. β-Fructosidase was covalently bound to the porous silicon surface of the channels in the microreactor. The porous silicon was achieved by anodization of the silicon reactor in a HF/ethanol mixture. For the measurement of small amounts of aqueous solution, a miniaturized flow cell was developed which consisted of two AgCl(x)Br(1)(-)(x) fiber tips (diameter, 0.75 mm) coaxially mounted in a PTFE block at a distance of 23 μm. The flowing stream was directed through the gap of the two fiber tips which served to define the optical path length and to bring the focused mid-IR radiation to the place of measurement. Using this construction, a probed volume of ～10 nL was obtained. The calibration curve was linear between 10 and 100 mmol/L sucrose. Furthermore, the potential of this method was demonstrated by the analysis of binary sucrose/glucose mixtures showing no interference from glucose and by the successful determination of sucrose in real samples.     

Surface-induced dissociation by Fourier transform mass spectrometry

A detailed procedure for performing surface-induced dissociation (SID) of ions in a dual-cell Fourier transform mass spectrometer is described. It is shown that the technique is applicable to both electron ionization and laser desorption measurements. SID spectra of perfluorotri-n-butylamine, anthracene, (5,10,15,20-tetraphenyl-21H,23H-prophinato)-iron(III) chloride, and [5,10,15,20-tetrakis(2,6-dibromo-phenyl)-21H,23H-prophina to]iron(III) chloride are presented. Conversion efficiencies of molecular ions between 1% and 30% are obtained. It is concluded the method holds promise for dissociation of high mass laser-desorbed ions.