Multivariate calibration of on-line enrichment near-infrared (NIR) spectra and determination of trace lead in water

An on-line enrichment near-infrared spectroscopy (NIRS) technique was developed in order to improve the sensitivity for the determination of trace heavy metal lead. Trace Pb²⁺ was enriched by chelating resin with on-line enrichment equipment and then the enriched lead on the resin was directly determined by near-infrared diffuse reflectance spectroscopy without elution procedure for simplicity of measurements. Partial least squares (PLS) regression was used to build models between the corrected spectra and concentrations of Pb²⁺, and segmental cross-validation was used to search for a reasonable number of PLS factors. The results showed that NIR spectra and concentrations of Pb²⁺ had a good linear relationship with the maximum of correlation coefficient of 0.9228 in the 1006–1383 nm region. The minimum RMSEP was 0.4777 mg L^− 1 when the latent variable number was 5 by using the region of 1006–1383 nm. The limit of detection (LOD) of lead was 0.2384 mg L^− 1 in this case. This study demonstrates that the prediction of trace heavy metal lead concentrations by NIRS is feasible with enrichment technique.     

A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

The application of direct orthogonal signal correction for linear and non-linear multivariate calibration

Traditionally, the direct orthogonal signal correction (DOSC) is always used together with a latent variable method such as partial least square (PLS) or principal component regression (PCR), to build a linear calibration model. In this study, PLS and least square support vector machine (LSSVM) were used to develop the linear and non-linear relation between spectra and components, respectively. DOSC was used to preprocess the input data, and the effect of DOSC pretreatment on linear and non-linear calibration model was investigated. The experiment was performed with three data sets. The first one was the acousto-optic tunable filter near infrared (AOTF-NIR) spectra of apples, the second one was the temperature-induced spectra of a ternary mixture of ethanol, water and 2-propanol, and the third one was the NIR spectra of corn. For all of the applications, the relation between spectra and components can be clearly observed in the spectra plot or the score plot after DOSC pretreatment. DOSC improved the predictive ability of PLS model. However, DOSC removed useful non-linear information that was related to components, thus, was not able to improve the performance of LSSVM model. DOSC pretreatment seems to be not suitable for non-linear calibration.     

基于进化蒙特卡洛方法的特征选择在机械故障诊断中的应用

特征选择可以从原始特征集中去除冗余特征,选择出优化特征子集,提高机械故障诊断精度和诊断效率。将进化蒙特卡洛方法引入机械故障诊断的特征选择。应用支持向量机(SVM)作为故障决策器,采用Wrapper式特征子集评价标准,并采用进化蒙特卡洛算法搜索最优特征子集。运用滚动轴承故障振动信号数据对提出的方法进行验证,实验结果表明该方法是有效的。     

温度对果糖含量近红外光谱检测模型传递的影响

以果糖溶液为研究对象,采用斜率/截距(S/B)算法,通过不同温度的果糖含量检测模型在两台不同厂家的傅里叶变换近红外光谱仪间的传递,讨论了样品温度变化对模型传递的影响.主、从仪器直接建模时,预测集的均方根偏差(RMSEP)均随温度升高呈增大的趋势,但整体变化不显著.直接预测时,预测集的RMSEP均大于0.86,效果不理想;采用S/B算法传递后,预测结果得到改善,主、从仪器相同温度光谱间的传递结果优于不同温度光谱间的传递,15℃、22℃和28℃时相同温度光谱间传递后的RMSEP分别为0.317、0.389和0.416,差异显著.实验结果表明,进行模型传递时,样品温度变化会对模型传递产生较大影响;保证样品温度一致下,选择合适温度有利于取得最佳传递效果.     

A passenger reduces sleepy driver's activation in the right prefrontal cortex: A laboratory study using near-infrared spectroscopy

The present study aimed to examine how a passenger affects the sleepiness effect (awake vs. sleepy) on an individual's prefrontal activation during a simulated driving-game task using a wireless portable near-infrared spectroscopy (NIRS) device. Participants drove from start to goal along default routes either solely (no-passenger group) or with a friend sitting beside him/her as a passenger (with-passenger group). Sleepiness level was assessed by a five-item scale questionnaire. In the no-passenger group, there were no performance and activation differences between the sleepy and awake participants. In the with-passenger group, by contrast, the sleepy participants showed more errors and lower activations in their right prefrontal cortex than the awake participants. These results suggest that a passenger has little effect on awake participants, but may weaken the sleepy participants’ vigilance and/or their cognitive abilities of action control. Practically, the present study demonstrates that NIRS may provide us a new possibility to monitor and examine the driver's mental states in the brain.     

Fault detection based on Gaussian process latent variable models

Gaussian processes, GPs, can be used to approximate complex non-linear functions with relative simplicity. Their regression performance is, at least, comparable to that achieved via artificial neural networks (ANN) and, in fact, both methods are intrinsically related. They are both non-parametric and, as Neal (1994) [1] has shown, when the number of nodes in the hidden layer of a neural network tends to infinity the ANN converge to a Gaussian process.In most of the cases, the GP will map a multivariate input into a univariate response. In this paper, however, we present an approach to process monitoring that combines several GPs so that multivariate responses can be appropriately modeled. We review a similar approach recently proposed in the literature and highlight some concerns related to it that needs to be taken into consideration. Additionally, we propose an alternative procedure to the way in which new observations are mapped into the non-linear model. A simulation study is provided that will help understand the method flexibility. Furthermore, results from a real example are also discussed.     

Optimisation of secondary electron detector for variable pressure SEM with Monte Carlo method

A computer programme for numerical modelling of electron flow in vacuum instruments is presented. The programme allows to simulate trajectories of charged particles in both high and low vacuum of the order of tens milibars. It combines a commercially available packet SIMION 3D v.7.0 destined for tracing trajectories of charged particles in electric and magnetic fields, and a Monte Carlo programme modelling phenomena accompanying electron collisions with gas molecules. The programme was applied for analysis and optimisation of a novel secondary electron detector for a variable-pressure scanning electron microscopy.     

Application of spectra cross-correlation for Type II outliers screening during multivariate near-infrared spectroscopic analysis of whole blood

In this study, a simple screening algorithm was developed to prevent the occurrence of Type II errors or samples with high prediction error that are not detected as outliers. The method is used to determine “good” and “bad” spectra and to prevent a false negative condition where poorly predicted samples appear to be within the calibration space, yet have inordinately large residual or prediction errors. The detection and elimination of this type of sample, which is a true outlier but not easily detected, is extremely important in medical decisions, since such erroneous data can lead to considerable mistakes in clinical analysis and medical diagnosis. The algorithm is based on a cross-correlation comparison between samples spectra measured over the region of 4160-4880 cm^− 1. The correlation values are converted using the Fisher's z-transform, while a z-test of the transformed values is performed to screen out the outlier spectra. This approach allows the use of a tuning parameter used to decrease the percentage of samples with high analytical (residual) errors. The algorithm was tested using a dataset with known reference values to determine the number of false negative and false positive samples. The cross-correlation algorithm performance was tested on several hundred blood samples prepared at different hematocrit (24 to 48%) and glucose (30 to 500 mg/dL) levels using blood component materials from thirteen healthy human volunteers. Experimental results illustrate the effectiveness of the proposed algorithm in finding and screening out Type II outliers in terms of sensitivity and specificity, and the ability to predict or estimate future or validation datasets ensuring lower error of prediction. To our knowledge this is the first paper to introduce a statistically useful screening method based on spectra cross-correlation to detect the occurrence of Type II outliers (false negative samples) for routine analysis in a clinically relevant application for medical diagnosis.     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

A semiparametric technique for the multi‐response optimization problem

Multi‐response optimization (MRO) in response surface methodology is quite common in applications. Before the optimization phase, appropriate fitted models for each response are required. A common problem is model misspecification and occurs when any of the models built for the responses are misspecified resulting in an erroneous optimal solution. The model robust regression (MRR) technique, a semiparametric method, has been shown to be more robust to misspecification than either parametric or nonparametric methods. In this study, we propose the use of MRR to improve the quality of model estimation and adapt its fits of each response to the desirability function approach, one of the most popular MRO techniques. A case study and simulation studies are presented to illustrate the procedure and to compare the semiparametric method with the parametric and nonparametric methods. The results show that MRR performs much better than the other two methods in terms of model comparison criteria in most situations during the modeling stage. In addition, the simulated optimization results for MRR are more reliable during the optimization stage. Copyright © 2010 John Wiley & Sons, Ltd.     

Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares

The need for automated quality surveillance of liquid hydrocarbon fuels has driven the development of rapid fuel property modeling from spectroscopic sensor data. The correlation of near-infrared (NIR) and Raman spectroscopic data with jet and diesel fuel properties can be improved by the deliberate selection of continuous wavelength sub-ranges. An automatic wavelength selection strategy would allow for the unsupervised construction of partial least squares (PLS) regression models of increased predictive utility when supervised model construction and maintenance is not feasible. Changeable size moving window partial least squares (CSMWPLS) is one of the most thorough operations suited for this task. Unfortunately, the necessarily large number of PLS model constructions required by an automated version of this procedure limits the evaluation of the predictive ability of the resulting models through full cross-validation results. Presented here is a novel restricted version of the CSMWPLS algorithm in which the initial spectral range selection is accomplished through multiple interval PLS (iPLS) analyses, where analysis windows for the refinement step no longer move, and size changes are limited to a series of symmetric attenuations. It is shown that the proposed algorithm can provide significant PLS model improvements during the course of a fully automated analysis of jet and diesel fuel spectra in less time than an automated CSMWPLS algorithm.     

Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on empirical mode decomposition

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source–detector pair.     

开阔水域下换能器指向性实船校准方法研究

随着换能器谐振频率的降低,其尺寸一般会增大,现有的测量方法对其指向性的测量越来越困难,而且换能器在实船安装前后性能会发生变化.文章提出了一种基于三维立体水听器阵列的开阔水域下换能器指向性实船校准方法.利用超短基线定位和坐标转换确定待校准换能器与水听器阵列的位置与方向,并通过水听器阵的接收声压数据绘制换能器的指向性图.在...     

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis–Hastings (M–H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.     

基于改进SCAM算法的结构物理参数识别贝叶斯方法

针对贝叶斯估计中逐分量自适应Metropolis(single component adaptive Metropolis,SCAM)算法易生成重复性样本,导致抽样效率低、结果误差大等问题,重新定义了提议分布方差的表达式,提出了改进的SCAM算法,使得抽样样本序列构成的马尔可夫链相对稳定.进而将贝叶斯理论与改进的SCA...     

A novel approach for development of a multivariate calibration model using a Doehlert experimental design: Application for prediction of key gasoline properties by Near-infrared Spectroscopy

Alternative methods for quality control in the petroleum industry have been obtained using Near-infrared Spectroscopy (NIRS) combined with multivariate techniques such as PLS (Partial Least-Square). The process of development and refinement of PLS models usually follows a nonsystematic and univariate procedure. The Standard Error of Cross Validation (SECV), the Standard Error of Prediction (SEP) and the determination coefficient (r²_regr.) are usually the only guides used in pursuit of the best model. In the present work, a novel approach was proposed using a Doehlert experimental design with three input variables (wavenumber range, preprocessing technique and regression/validation technique) varied at 5, 7 and 3 levels, respectively. Besides SECV, SEP and r²_regr., some additional response variables, such as the slope, r² and p_value from the external validation, as well as the number of PLS factors, were simultaneously assessed to find the optimum conditions for PLS modeling. The optimum setting for each input variable was simultaneously defined through a multivariate approach using a desirability function. With the proposed approach, the main and interaction effects could also be investigated. The methodology was successfully applied to obtain PLS models to monitor the gasoline quality through the process of product loading in trucks. To prevent product contamination or adulteration, fast prediction of key properties was obtained from FT-NIR spectra within the 7300-3900 cm^− 1 region with SECV in the range 0.04-0.63% w/w for composition (Aromatics, Saturates, Olefins and Benzene) and 0.0008 for Relative Density 20/4 °C. Each optimized PLS model was obtained with less than 40 modeling runs, demonstrating the efficiency of the proposed approach.     

A particle code for plasma technological applications

The 2D Direct Simulation Monte Carlo code LasVegas was originally developed and used for the simulation of atmospheric re-entry and the flow around plasma wind tunnel probes. This code is reviewed regarding its applicability to appropriate plasma technological applications. The main DSMC theory and the code structure, features and properties are described in order to classify the LasVegas code in regard to these applications. General restrictions, model uncertainties and geometrical considerations of the DSMC method itself as well as of the LasVegas code put the focus on the vacuum spray process. As proof of concept, results of a supersonic free stream simulation in a vacuum chamber with typical inflow properties are presented and discussed. Appropriate code extensions such as the flow-particle interaction, ionization model and parallel computation capability were identified, thus indicating future numerical works.     

基于FxLMS的汽车车内噪声变步长主动均衡算法

提出了一种可用于汽车车内噪声主动均衡控制的变步长主动噪声均衡（Active Noise Equalization,ANE）算法,与传统车内噪声主动抵消控制方法所采用的滤波x最小均方（Filtered-x Least Mean Square,FxLMS）算法相比具有更好的实用性。应用固定步长主动噪声均衡（Active Noise Equalization,ANE）算法、所提出变步长ANE算法和已有变步长ANE算法分别进行汽车车内噪声主动均衡控制。结果表明,所提出变步长ANE算法具有更快的收敛速度和较低的稳态误差,并且能进一步降低汽车车内噪声响度,为汽车车内声品质主动控制提供了一种新方法。     

误差非线性函数的变步长FxLMS算法研究

滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法是主动噪声控制的经典算法,其存在收敛速度与稳态误差不可兼得的问题,解决方法之一是采用变步长FxLMS算法.总结了现有的基于误差非线性函数的变步长模型,并将其应用于FxLMS算法以改善算法性能.用三种常见的噪声作为参考输入信号进行...