Comparison of methods for rapid evaluation of lifetimes of exponential decays

For evaluating exponential luminescence decays, there are a variety of computational rapid integral methods based on the areas of the decay under different binned intervals. Using both Monte Carlo methods and experimental photon counting data, we compare the standard rapid lifetime determination method (SRLD), optimized rapid lifetime determination methods (ORLD), maximum likelihood estimator method (MLE), and the phase plane method (PPM). The different techniques are compared with respect to precision, accuracy, sensitivity to binning range, and the effect of baseline interference. The MLE provides the best overall precision, but requires 10 bins and is sensitive to very small uncorrected baselines. The ORLD provides nearly as good precision using only two bins and is much more immune to uncompensated baselines. The PPM requires more bins than the MLE and has systematic errors, but is largely resistant to baseline issues. Therefore, depending on the data acquisition method and the number of bins that can be readily employed, the ORLD and MLE are the preferred methods for reasonable signal-to-noise ratios.     

Baseline correction method using an orthogonal basis for gas chromatography/mass spectrometry data

A baseline correction method that uses basis set projection to estimate spectral backgrounds has been developed and applied to gas chromatography/mass spectrometry (GC/MS) data. An orthogonal basis was constructed using singular value decomposition (SVD) for each GC/MS two-way data object from a set of baseline mass spectra. A novel aspect of this baseline correction method is the regularization parameter that prevents overfitting that may produce negative peaks in the corrected mass spectra or ion chromatograms. The number of components in the basis, the regularization parameter, and the mass spectral range from which the spectra were sampled to construct the basis were optimized so that the projected difference resolution (PDR) or signal-to-noise ratio (SNR) was maximized. PDR is a metric similar to chromatographic resolution that indicates the separation of classes in a multivariate data space. This new baseline correction method was evaluated with two synthetic data sets and a real GC/MS data set. The prediction accuracies obtained by using the fuzzy rule-building expert system (FuRES) and partial least-squares-discriminant analysis (PLS-DA) as classifiers were compared and validated through bootstrapped Latin partition (BLP) between data before and after baseline correction. The results indicate that baseline correction of the two-way GC/MS data using the proposed methods resulted in a significant increase in average PDR values and prediction accuracies.     

Instrumental phase-based method for Fourier transform spectrometer measurements processing

Phase correction is a critical procedure for most space-borne Fourier transform spectrometers (FTSs) whose accuracy (owing to often poor signal-to-noise ratio, SNR) can be jeopardized from many uncontrollable environmental conditions. This work considers the phase correction in an FTS working under significant temperature change during the measurement and affected by mechanical disturbances. The implemented method is based on the identification of an instrumental phase that is dependent on the interferometer temperature and on the extraction of a linear phase component through a least-squares approach. The use of an instrumental phase parameterized with the interferometer temperature eases the determination of the linear phase that can be extracted using only a narrow spectral region selected to be immune from disturbances. The procedure, in this way, is made robust against phase errors arising from instrumental effects, a key feature to reduce the disturbances through spectra averaging. The method was specifically developed for the Mars IR Mapper spectrometer, that was designed for operation onboard a rover on the Mars surface; the validation was performed using ground and in-flight measurements of the Fourier transform IR spectrometer planetary Fourier spectrometer, onboard the MarsExpress mission. The symmetrization has been exploited also for the spectra calibration, highlighting the issues deriving from the cases of relevant beamsplitter emission. The applicability of this procedure to other instruments is conditional to the presence in the spectra of at least one spectral region with a large SNR along with a negligible (or known) beamsplitter emission. For the PFS instrument, the processing of data with relevant beamsplitter emission has been performed exploiting the absorption carbon dioxide bands present in Martian spectra.     

Trade-off studies of a hyperspectral infrared sounder on a geostationary satellite

Trade-off studies on spectral coverage, signal-to-noise ratio (SNR), and spectral resolution for a hyperspectral infrared (IR) sounder on a geostationary satellite are summarized. The data density method is applied for the vertical resolution analysis, and the rms error between true and retrieved profiles is used to represent the retrieval accuracy. The effects of spectral coverage, SNR, and spectral resolution on vertical resolution and retrieval accuracy are investigated. The advantages of IR and microwave sounder synergy are also demonstrated. When focusing on instrument performance and data processing, the results from this study show that the preferred spectral coverage combines long-wave infrared (LWIR) with the shorter middle-wave IR (SMidW). Using the appropriate spectral coverage, a hyperspectral IR sounder with appropriate SNR can achieve the required science performance (1 km vertical resolution, 1 K temperature, and 10% relative humidity retrieval accuracy). The synergy of microwave and IR sounders can improve the vertical resolution and retrieval accuracy compared to either instrument alone.     

Two-point maximum entropy noise discrimination in spectra over a range of baseline offsets and signal-to-noise ratios

The two-point maximum entropy method (TPMEM) is a useful method for signal-to-noise ratio enhancement and deconvolution of spectra, but its efficacy is limited under conditions of high background offsets. This means that spectra with high average background levels, regions with high background in spectra with varying background levels, and regions of high signal-to-noise ratios are smoothed less effectively than spectra or spectral regions without these conditions. We report here on the cause of this TPMEM limitation and on appropriate baseline estimation and removal procedures that effectively minimize the effects on regularization. We also present a comparative analysis of TPMEM and Savitzky-Golay filtering to facilitate selection of the best technique under a given range of conditions.     

Combined use of conventional and second-derivative data in the SIMPLISMA self-modeling mixture analysis approach

Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) is a successful pure variable approach to resolve spectral mixture data. A pure variable (e.g., wavenumber, frequency number, etc.) is defined as a variable that has significant contributions from only one of the pure components in the mixture data set. For spectral data with highly overlapping pure components or significant baselines, the pure variable approach has limitations; however, in this case, second-derivative spectra can be used. In some spectroscopies, very wide peaks of components of interest are overlapping with narrow peaks of interest. In these cases, the use of conventional data in SIMPLISMA will not result in proper pure variables. The use of second-derivative data will not be successful, since the wide peaks are lost. This paper describes a new SIMPLISMA approach in which both the conventional spectra (for pure variables of wide peaks) and second-derivative spectra (for pure variables of narrow peaks, overlapping with the wide peaks) are used. This new approach is able to properly resolve spectra with wide and narrow peaks and minimizes baseline problems by resolving them as separate components. Examples will be given of NMR spectra of surfactants and Raman imaging data of dust particle samples taken from a lead and zinc factory's ore stocks that were stored outdoors.     

Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

Session-based Recommendation (SBR) aims to accurately recommend a list of items to users based on anonymous historical session sequences. Existing methods for SBR suffer from several limitations: SBR based on Graph Neural Network often has information loss when constructing session graphs; Inadequate consideration is given to influencing factors, such as item price, and users’ dynamic interest evolution is not taken into account. A new session recommendation model called Price-aware Session-based Recommendation (PASBR) is proposed to address these limitations. PASBR constructs session graphs by information lossless approaches to fully encode the original session information, then introduces item price as a new factor and models users’ price tolerance for various items to influence users’ preferences. In addition, PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time. Finally, PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction. Specifically, the intent, the short-term and long-term interests, and the dynamic interests of a user are combined. Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.     

Customized baseline correction

Baseline correction is an important pre-processing technique used to separate true spectroscopic signals from interference effects or remove background effects, stains or traces of compounds, e.g. in 2D gel electrophoresis. In some cases parts of the spectra or images need correction using rigid baselines (limited curvature) while other parts need more flexible baselines (more curvature). Often one has to make a compromise that is not optimal over the whole spectral range, or focus on one part and let the rest be treated sub-optimally. A customizing wrapper is proposed that rescales the spectrum abscissa and therefore makes the baseline correction algorithm behave right in all parts of the spectra. Improvements are demonstrated both visually and through regression using recently reported Raman spectra on melted fat from pork adipose tissue.     

Automated estimation of white Gaussian noise level in a spectrum with or without spike noise using a spectral shifting technique

Various tasks, for example, the determination of signal-to-noise ratios, require the estimation of noise levels in a spectrum. This is generally accomplished by calculating the standard deviation of manually chosen points in a region of the spectrum that has a flat baseline and is otherwise devoid of artifacts and signal peaks. However, an automated procedure has the advantage of being faster and operator-independent. In principle, automated noise estimation in a single spectrum can be carried out by taking that spectrum, shifting a copy thereof by one channel, and subtracting the shifted spectrum from the original spectrum. This leads to an addition of independent noise and a reduction of slowly varying features such as baselines and signal peaks; hence, noise can be more readily determined from the difference spectrum. We demonstrate this technique and a spike-discrimination variant on white Gaussian noise, in the presence and absence of spike noise, and show that highly accurate results can be obtained on a series of simulated Raman spectra and consistent results obtained on real-world Raman spectra. Furthermore, the method can be easily adapted to accommodate heteroscedastic noise.     

A study on the applicability on multicomponent calibration methods in chemometrics

Twelve multivariate calibration method alternatives are compared to establish the effect of spectral nonlinearity and collinearity on accuracy and precision of determined results. Simulated and real spectral data are used in this research. This study can help us to select an optimum method for determination.     

Signal-to-noise ratio trade-offs associated with coarsely sampled Fourier transform spectroscopy

We derive the spectral signal-to-noise ratio (SNR) trade-offs associated with coarsely sampled Fourier transform spectroscopy using a step-and-integrate measurement scheme. We show that there is no SNR penalty in the shot noise limit and a slight SNR benefit in the detector noise limit for the case of coarse sampling to achieve the same spectral resolution as a baseline Nyquist sampling scenario, where the total detector integration time is the same for both sampling cases.     

Multitaper scan-free spectrum estimation using a rotational shear interferometer

Multitaper methods for a scan-free spectrum estimation that uses a rotational shear interferometer are investigated. Before source spectra can be estimated the sources must be detected. A source detection algorithm based upon the multitaper F-test is proposed. The algorithm is simulated, with additive, white Gaussian detector noise. A source with a signal-to-noise ratio (SNR) of 0.71 is detected 2.9 degrees from a source with a SNR of 70.1, with a significance level of 10(-4), approximately 4 orders of magnitude more significant than the source detection obtained with a standard detection algorithm. Interpolation and the use of prewhitening filters are investigated in the context of rotational shear interferometer (RSI) source spectra estimation. Finally, a multitaper spectrum estimator is proposed, simulated, and compared with untapered estimates. The multitaper estimate is found via simulation to distinguish a spectral feature with a SNR of 1.6 near a large spectral feature. The SNR of 1.6 spectral feature is not distinguished by the untapered spectrum estimate. The findings are consistent with the strong capability of the multitaper estimate to reduce out-of-band spectral leakage.     

弱目标检测与定位的空时谱积分方法

研究了一种基于空时谱积分最大化的弱目标检测与距离估计技术。对常规波束形成输出在各频率和方位处的信号功率进行长时间的非相干积累,根据长时间空时谱积分的极大值来确定目标的空间位置,并且对频率-方位谱采用三次样条内插以提高定位精度。由于增大了积分时间,大大提高了对弱目标的检测能力,并且不需要细致的环境参数,提高了测距定位的稳健性。数值分析验证了在低信噪比下,检测和定位性能的提高,并给出了测距误差分析。     

基于高阶累积量的高精度时延估计算法

针对海洋探测中由于接收信号信噪比低并存在各种噪声干扰导致时延估计精度低的问题,提出一种基于二次相关和高阶累积量的具有多种噪声抑制能力的高精度时延估计新方法——SC-HOCS法。该方法首先对两路接收信号进行自相关和互相关处理,抑制部分高斯噪声,然后利用高阶累积量一维切片法对信号进行处理,抑制相关高斯噪声和非高斯色噪声,通过对接收信号的上述处理提高信噪比,最后结合希尔伯特变换对相关峰进行锐化处理,进一步提高时延估计精度。与广义相关法、二次相关法及高阶累积量一维切片法相比,该方法能很好地抑制相关噪声并且能在更低的信噪比下获得较好的时延估计精度,同时该算法计算量较小,可满足对数据实时处理的需求。计算机仿真和水池实验验证了该方法的有效性。该方法为海洋探测中低信噪比信号的高精度时延估计提供一种新的技术途径。     

High-precision isothermal titration calorimetry with automated peak-shape analysis

Isothermal titration calorimetry (ITC) is a powerful classical method that enables researchers in many fields to study the thermodynamics of molecular interactions. Primary ITC data comprise the temporal evolution of differential power reporting the heat of reaction during a series of injections of aliquots of a reactant into a sample cell. By integration of each injection peak, an isotherm can be constructed of total changes in enthalpy as a function of changes in solution composition, which is rich in thermodynamic information on the reaction. However, the signals from the injection peaks are superimposed by the stochastically varying time-course of the instrumental baseline power, limiting the precision of ITC isotherms. Here, we describe a method for automated peak assignment based on peak-shape analysis via singular value decomposition in combination with detailed least-squares modeling of local pre- and postinjection baselines. This approach can effectively filter out contributions of short-term noise and adventitious events in the power trace. This method also provides, for the first time, statistical error estimates for the individual isotherm data points. In turn, this results in improved detection limits for high-affinity or low-enthalpy binding reactions and significantly higher precision of the derived thermodynamic parameters.     

A small-window moving average-based fully automated baseline estimation method for Raman spectra

A fully automated and model-free baseline-correction method for vibrational spectra is presented. It iteratively applies a small, but increasing, moving average window in conjunction with peak stripping to estimate spectral baselines. Peak stripping causes the area stripped from the spectrum to initially increase and then diminish as peak stripping proceeds to completion; a subsequent increase is generally indicative of the commencement of baseline stripping. Consequently, this local minimum is used as a stopping criterion. A backup is provided by a second stopping criterion based on the area under a third-order polynomial fitted to the first derivative of the current estimate of the baseline-free spectrum and also indicates whether baseline is being stripped. When the second stopping criterion is triggered instead of the first one, a proportionally scaled simulated Gaussian baseline is added to the current estimate of the baseline-free spectrum to act as an internal standard to facilitate subsequent processing and termination via the first stopping criterion. The method is conceptually simple, easy to implement, and fully automated. Good and consistent results were obtained on simulated and real Raman spectra, making it suitable for the fully automated baseline correction of large numbers of spectra.     

ICP-OES法测定高速工具钢中钨、铬、钒、钼

采用ICP-OES法同时测定高速工具钢中钨、铬、钒、钼元素的含量。讨论了基体、分析谱线对测定结果的影响,并确定了最佳的测定条件。最后利用精密度和统计学中的t检验分析测定数据的准确与可靠性。结果表明此方法方便快捷,测量数据准确可靠。     

A matrix-based two-dimensional regularization algorithm for signal-to-noise ratio enhancement of multidimensional spectral data

We present a new spectral image processing algorithm, the "matrix maximum entropy method" (MxMEM), which offers efficient signal-to-noise ratio (SNR) enhancement of multidimensional spectral data. MxMEM is based upon two previous regularization methods that employ the maximum entropy concept. The first is a one-dimensional (1D) algorithm, which smoothes individual vectors, called the two-point maximum entropy method (TPMEM). The second is a two-dimensional (2D) form called 2D TPMEM, that smoothes images but processes them one vector at a time. MxMEM is a truly two dimensional image processing algorithm in that its "smoothing engine" performs two-dimensional processing in every iteration. We demonstrate that this matrix-based construction makes more effective use of two-dimensionally embedded information and thus confers significant advantages over other regularization approaches. In addition, we utilize the concept that individual related Raman spectra can be combined in a matrix to form an artificial Raman "image". We show that, when processed as an image, superior SNR enhancement is achieved compared to processing the same data by TPMEM one spectrum at a time.     

Generalized signal-to-noise ratio for spectral sensors with correlated bands

Traditional spectral sensors are intentionally designed to minimize overlap among spectral response functions of different bands. In contrast, some emerging classes of spectral sensors exhibit significant band overlap. An effect introduced by such band overlap is that the photodetector noise of one band is coupled into the others in subsequent data processing steps. Because of this, the traditional band-by-band definition of signal-to-noise ratio (SNR) cannot fully describe the detector's noise level. We devise a general definition of SNR in spectral space based on a recently developed geometrical spectral imaging model [J. Opt. Soc. Am. A24, 2864 (2007)]. With this model, we can find an orthogonal basis of the spectral response functions for the spectral sensor with decreasing instrument SNRs. We can also define the average instrument SNR for the whole sensor, which makes it possible to characterize quantitatively the photodetector noise of a spectral sensor with correlated bands.     

Theoretical maximal precision for mass-to-charge ratio, amplitude, and width measurements in ion-counting mass analyzers

A theory previously developed for spectra with detector-limited (i.e., signal-independent) Gaussian-distributed noise is applied to calculate the maximal precision with which mass spectral peak parameters (mass-to-charge ratio, amplitude, width) can be determined from a discrete spectrum with source-limited Poisson-distributed noise. The precision depends in a calculable way upon the peak shape, signal-to-noise ratio, and number of data points per peak width. Those dependencies are tested by analysis of simulated data. The theory provides estimates for the precision of a repeated experiment, based on data from a single discrete mass spectrum whose parameters are extracted by a least-squares fit to a specified line shape. The relevance of the predictions to present and potential time-of-flight performance is discussed.