浮动基准法无创血糖浓度检测技术

介绍了浮动基准法无创伤测量血糖浓度的原理,并模拟研究了其关键因素——径向检测基准位置的存在条件．在径向检测基准位置,漫反射光对葡萄糖浓度变化不敏感,因而可以作为内部基准来消除人体生理背景变化的干扰．通过设计不同背景条件下的Intralipid溶液测量实验,进行了浮动基准法的初步应用实验．实验结果表明,采用浮动基准法进行光谱数据处理后,有效地减小了测量环境变化及Intralipid样品背景变化的影响,极大地增强了葡萄糖浓度的分辨能力．     

基于光谱解混的选择性波段子集高光谱异常检测

由于高光谱图像具有高阶性和背景分布特性复杂的特点,这使得现有的算法在解决异常检测问题时存在一些不足。通过分析高光谱图像的光谱特性和空间特性,基于统计学习理论,利用光谱解混技术和子空间划分方法,提出了基于光谱解混的选择性波段子集高光谱图像异常检测算法。该算法首先利用光谱解混技术提取出对背景分布特性有严重影响的端元光谱,由此降低背景干扰突出异常目标信息;在此基础上,利用子空间划分方法将整个波段空间划分为大小不等的多个子空间,并在每个子空间内利用非高斯程度度量准则提取出富含异常目标信息的特征波段;最后,采用KRX算法作为异常检测算子完成异常目标检测。利用真实的高光谱图像对提出的算法进行实验验证,结果表明该算法是有效和合理的,具有良好的异常检测性能。     

基于相空间重构与平稳子空间分析的滚动轴承故障诊断

针对如何分离淹没在与转子转速相关的背景信号及其它噪声中的滚动轴承故障特征信号问题,提出将相空间重构与平稳子空间分析相结合的滚动轴承故障诊断方法。用相空间重构实现对滚动轴承故障振动信号升维;用平稳子空间对高维信号中平稳、非平稳源信号进行区分,并对峭度值最大的非平稳源信号进行最小熵解卷积降噪;对降噪信号进行包络谱分析提取轴承故障特征频率。仿真信号、故障诊断实例表明,诊断效果优于基于EMD的包络解调方法。     

水中目标辐射噪声调制信息提取方法研究

讨论了应用小波分析从辐射噪声中提取调制信息的方法。首先利用多尺度分析方法将噪声信号在不同层次的空间上进行分解，实现调制信号与辐射噪声的分离；然后，利用小波变换提取其包络，求取功率谱得到调制信息．在此基础上，对实测辐射噪声数据进行仿真研究．结果表明，该方法用于噪声中调制信息检测是一种有效的手段．     

机械故障特征提取的拓扑集分形稀疏字典

多尺度字典是实现机械动态信号中频域弱相干成分解耦的重要方法,但目前还欠缺围绕固定频率进行分辨率持续细化的系统性理论。基于近似解析小波变换构建了全新的拓扑集分形多分辨理论。提出了中心嵌套子空间簇,它随分析尺度深化实现了机械故障特征多目标同时追踪细化,在频域上表现为围绕一系列固定中心频率进行严格的二进细化。在数学上严格证明了:①任意中心嵌套子空间簇都是动态信号频域的拓扑空间;②各中心嵌套子空间簇按分析尺度具有严格自相似的分形特性。另外研究还揭示了二进制多分辨理论与拓扑集分形的深刻同构关联,即任意二进小波包唯一从属于某个中心嵌套子空间簇。其次,在扩展中心嵌套子空间诱导下,经典小波包变换可以化归成拓扑集分形的真子集。将拓扑集分形与旋转机械部件损伤动态模型结合提出了一种新的机械故障特征提取方法。该方法通过周期性成分稀疏测度优化和中心嵌套子空间搜索对冲击性机械故障特征的周期、瞬时动力学参数进行优化提取,从而提取物理意义更显著的单分量信息。将该方法应用于滚动轴承的故障诊断中,在含强噪声的振动信号中准确提取了表征轴承外圈剥落的多个单一模式分量。通过对比验证了所提出方法的噪声抑制能力显著优于以谱峭度和群组稀疏优化为代表的对主流机械故障特征提取技术。     

基于自适应子空间估计的DOA跟踪算法

运用特征子空间类高分辨方法的关键在于信号或噪声子空间的估计。实际上有些信号的统计特性通常随时间变化，为了得到参数的实时估计值，需要随时根据新的阵列接收数据对信号或噪声子空间进行更新?文中分析了一种自适应子空间估计算法，即MALASE(Maximum Likelihood Adaptive Subspaee Estimation)算法然后，把MALASE算法与最小范数(Mini—Norm)高分辨方位计算法相结合．并应用零点跟踪技术，提出了一种自适应Mini—Norm算法，可用于对时变的信号波达方向(DOA)进行跟踪估计。仿真结果验证了该算法具有较好的跟踪性能。     

基于双树复小波的遥测振动信号多尺度噪声调节随机共振分析

遥测振动信号包含大量反映飞行器试验过程中的状态特征信息,具有非线性、非平稳性、强噪声等特点,如何提取反映系统运行状态的微弱非线性特征直接关系到飞行状态监测和故障诊断的准确性,针对这一问题,提出一种基于双数复小波的多尺度噪声调节随机共振分析方法,充分考虑多尺度带限噪声对非线性随机共振的影响,利用多尺度噪声调节和樽海鞘群算法优化非线性随机共振,对遥测振动信号的微弱特征信息进行增强,仿真和实测数据实验结果表明该方法的有效性。     

奇异谱分解在超声速无人机声振试验数据处理中的应用

将相空间重构和奇异谱分解相结合对受强气动噪声影响的超声速飞行器测试数据进行滤波,以实现对于试验参数的精确识别。首先通过数值仿真论证该方法的可行性,然后针对某型超声速无人机的声振试验对试验采集数据进行相空间重构,并对重构后的轨迹矩阵进行奇异值分解,得到反映真实信号信息的信号子空间和反映噪声信息的噪声子空间。通过定义奇异值差分谱这一指标来判定真实信号信息子空间维数,并针对现有最大差分谱理论缺陷提出了优选差分谱峰值理论,利用奇异谱分解的逆过程对真实信号进行重构。重构结果表明,该方法适用于超声速飞行下的飞行器声振试验数据处理,为超声速飞行器飞行状态的精确描述提供了良好的思路。     

基于奇异值分解的信号特征提取方法研究

奇异值反映了信号中有用信号和噪声的能量分布情况,通过奇异值分解可以将隐含在噪声中的特征信号提取出来。本文提出了在强背景噪声中基于奇异值分解的特征提取方法。研究发现,随着信号信噪比的降低,奇异值的分布趋于直线,特征信号难以分离和提取。通过增加奇异值分解阶次,可以使反映噪声能量的奇异值的分布范围扩大,使得噪声的能量相对分散,凸显出了反映有用信号能量的奇异值,从而有利于特征信号的提取。仿真试验和故障分析实例都验证了该方法的可行性。     

近红外无创血糖测量中径向基准位置的模拟及验证

利用近红外光谱进行无创血糖检测时,人体自身状况的变化对测量结果的准确性造成非常大的影响.浮动基准法意在测量血糖的过程中寻找一个径向基准点,在这一位置处的漫反射光对葡萄糖浓度的变化不敏感,可以作为内部基准来消除各种背景变化的干扰.以intralipid溶液作为人体组织模拟液,用蒙特卡罗方法以及实验验证了径向基准位置的存在,并研究了不同光学参数对它的影响.模拟结果表明,1300nm波长下径向基准位置距光源1mm,且生物组织的散射特性对它的影响比吸收特性明显;设计的双光纤连续移动测量样品漫反射光能量的实验确认了模拟结果的正确性,证明了径向基准位置的存在.     

In vivo near-infrared spectroscopy of rat skin tissue with varying blood glucose levels

We have performed in vivo measurements of near-infrared rat skin absorption in the 4000-5000-cm(-1) spectral range (2.0-2.5-microm wavelength) during a glucose clamp experiment in order to identify the presence of glucose-specific spectral information. Spectra were collected during an initial 3-h period where the animal's blood glucose concentration was held at its normal value. The blood glucose level was then increased above 30 mM by venous infusion of glucose and held for 2 h, after which it was allowed to return to normal. Spectra were recorded continuously during the procedure and are analyzed to identify spectral changes associated with changes in glucose concentration. Because the change in absorbance due to an increase in glucose concentration is small compared to changes due to other variations (e.g., the thickness of the skin sample), a simple subtraction of absorbance spectra from the hyperglycemic and euglycemic phases is not instructive. Instead, a set of principal components is established from the euglycemic period where the glucose concentration is constant. We then examine the change in absorbance during the hyperglycemic period that is orthogonal to these principal components. We find that there are significant similarities between these orthogonal variations and the net analyte signal of glucose, which suggests that glucose spectral information is present. The analysis described here provides a procedure by which the analytical significance of a multivariate calibration can be evaluated.     

基于CEM的高光谱图像小目标检测算法

针对高光谱图像中小目标检测问题,提出了一种基于约束能量最小化(Constrained Energy Minimization,CEM)的目标检测算法.该算法首先对原始图像进行背景信息抑制从而抑制背景地物、突出低概率的小目标,用迭代误差分析的自动端元提取算法找出目标的端元光谱,然后把目标端元光谱代入CEM滤波器得到该目标的检测结果图.用高光谱数据进行了实验研究,并与CEM滤波器进行了比较.结果表明,其检测性能与直接采用CEM方法的检测性能相当,但是相对于CEM方法,该算法不需要目标的先验光谱信息,更具有实用性.     

Scattering and absorption effects in the determination of glucose in whole blood by near-infrared spectroscopy

Optical properties of whole bovine blood are examined under conditions of different glucose loadings. A strong dependency is established between the scattering properties of the whole blood matrix and the concentration of glucose. This dependency is explained in terms of variations in the refractive index mismatch between the scattering bodies (predominately red blood cells) and the surrounding plasma. Measurements in the presence of a well-known glucose transport inhibitor indicate that variations in refractive index mismatch are related to the penetration of glucose into the red blood cells and demonstrate that increased scattering involves the uptake of glucose by red blood cells. Finally, multivariate calibration models are presented for the measurement of glucose in a whole blood matrix. These models are based on near-infrared spectral data collected from 80 different samples prepared from a single whole blood matrix. Calibration studies are performed over the combination, first-overtone, and short-wavelength spectral regions. The best calibration model is generated from combination region spectra, providing a standard error of prediction (SEP) of less than 1 mM over the concentration range of 3-30 mM. The model based on the first-overtone region is slightly degraded but still provides acceptable performance (SEP = 1.20 mM). The model based on the short-wavelength region is further degraded (SEP = 2.53 mM). To rationalize these results, an analysis of the selectivity of the calibration models is performed by computing the glucose net analyte signal. It is established that the models based on the combination and first-overtone regions are dominated by glucose absorption information, while the model computed from the short-wavelength region is based primarily on scattering information. This result provides evidence that absorption information is needed in order to obtain a glucose calibration model with acceptable performance.     

Modified robust continuum regression by net analyte signal to improve prediction performance for data with outliers

Contaminated data exist in diverse situations, even in high quality surveys and experiments. If classical statistic models are blindly applied to data containing outliers, the results can be misleading at best. In this paper, a modified robust continuum regression (mRCR) method is proposed to improve prediction performance for data with outliers. The mRCR method constructs projection pursuit directions by using projection matrix for computing the net analyte signal (NAS) of the target analyte. This paper examines applications to the determination of glucose concentration by near-infrared (NIR) spectrometry, including aqueous solution with glucose experiment, plasma experiment in vitro, oral glucose tolerance test (OGTT) in vivo, to illustrate the advantages of mRCR for various kinds of outliers depending on the way of contamination. The results indicate that the mRCR method is entirely robust with respect to any type of outlying observations, and it yields smaller prediction errors for normal samples than other calibration methods.     

Temperature-insensitive near-infrared method for determination of protein concentration during protein crystal growth

A temperature-insensitive method for measuring protein concentration in aqueous solutions using near-infrared spectroscopy is described. The method, which is based on identification of the net analyte signal of single-beam spectra, can be calibrated using a single protein absorbance measurement and is thus well suited for crystallization monitoring where the quantity of protein is limited. The method is applied to measurements of glucose-isomerase concentration in a sodium phosphate buffer that is actively varied over the temperature range of 4-24 degrees C. The standard error of prediction using the optimized spectral range of 4670-4595 cm(-1) is 0.12 mg/mL with no systematic trend in the residuals with solution temperature. The method is also applied to previously collected spectra of hen egg-white lysozyme and yields a standard error of prediction of 0.14 mg/mL. Spectra sampled at discrete wavelengths can also be used for calibration and prediction with performance comparable to that obtained with spectral bands. A set of four wavelengths are identified that can be used to predict concentrations of both proteins with a standard error less than 0.14 mg/mL.     

Multivariate sensitivity for the interpretation of the effect of spectral pretreatment methods on near-infrared calibration model predictions

Predictions obtained from a multivariate calibration model are sensitive to variations in the spectra such as baseline shifts, multiplicative effects, etc. Many spectral pretreatment methods have been developed to reduce these distortions, and the best method is usually the one that minimizes the prediction error for an independent test set. This paper shows how multivariate sensitivity can be used to interpret spectral pretreatment results. Understanding why a particular pretreatment method gives good or bad results is important for ruling out chance effects in the conventional process of "trial and error", thus obtaining more confidence in the finally selected model. The principles are exemplified using the transmission near-infrared spectroscopic prediction of oxygenates in ampules of the standard reference material gasoline. The pretreatment methods compared are the multiplicative signal correction, first-derivative method, and second-derivative method. It is shown that for this application the first- and second-derivative methods are successful in removing the background. However, differentiating the spectra substantially reduces multivariate net analyte signal (in the worst case by a factor of 21). Consequently, a significantly smaller multivariate sensitivity is obtained which leads to increased spectral error propagation resulting in a larger uncertainty in the regression vector estimate and larger prediction errors. Differentiating spectra also increases the spectral noise (each time by a factor 2(1/2)) but this effect, which is well-known, is of minor importance for the current application.     

Efficient computation of net analyte signal vector in inverse multivariate calibration models

The net analyte signal vector has been defined by Lorber as the part of a mixture spectrum that is unique for the analyte of interest; i.e., it is orthogonal to the spectra of the interferences. It plays a key role in the development of multivariate analytical figures of merit. Applications have been reported that imply its utility for spectroscopic wavelength selection as well as calibration method comparison. Currently available methods for computing the net analyte signal vector in inverse multivariate calibration models are based on the evaluation of projection matrices. Due to the size of these matrices (p × p, with p the number of wavelengths) the computation may be highly memory- and time-consuming. This paper shows that the net analyte signal vector can be obtained in a highly efficient manner by a suitable scaling of the regression vector. Computing the scaling factor only requires the evaluation of an inner product (p multiplications and additions). The mathematical form of the newly derived expression is discussed, and the generalization to multiway calibration models is briefly outlined.     

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal preprocessing. The SE indicator performs better than the two other indicators, and it is also applicable to other situations where the Beer-Lambert law is valid.     

Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

A novel procedure is proposed as a method to characterize the chemical basis of selectivity for multivariate calibration models. This procedure involves submitting pure component spectra of both the target analyte and suspected interferences to the calibration model in question. The resulting model output is analyzed and interpreted in terms of the relative contribution of each component to the predicted analyte concentration. The utility of this method is illustrated by an analysis of calibration models for glucose, sucrose, and maltose. Near-infrared spectra are collected over the 5000-4000-cm(-)(1) spectral range for a set of ternary mixtures of these sugars. Partial least-squares (PLS) calibration models are generated for each component, and these models provide selective responses for the targeted analytes with standard errors of prediction ranging from 0.2 to 0.7 mM over the concentration range of 0.5-50 mM. The concept of the proposed pure component selectivity analysis is illustrated with these models. Results indicate that the net analyte signal is solely responsible for the selectivity of each individual model. Despite strong spectral overlap for these simple carbohydrates, calibration models based on the PLS algorithm provide sufficient selectivity to distinguish these commonly used sugars. The proposed procedure demonstrates conclusively that no component of the sucrose or maltose spectrum contributes to the selective measurement of glucose. Analogous conclusions are possible for the sucrose and maltose calibration models.     

Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

Rank deficiency is the major problem associated with the chemometrics modeling of the second-order chemical reactions of the form of A + B → C. In this article, the application of the hard-soft-net analyte signal (HS-NAS), as a newly proposed method in our research group, is described for modeling of second-order reactions. This combined hard-soft method is based on the net analyte signal (NAS) concept, which is defined as a part of total signal that is directly related to the concentration of the component of interest. Therefore, concentration changes versus time can be obtained by calculating NAS for any chemical species involved in the reaction without requiring any pure component spectra or extra runs. The power of the method was verified by applying it to the resolution of simulated data sets containing noises added at different levels. The resolution of the second-order reaction between amoxicillin and 1, 2-naphthoqoinone was also tested as a real chemical system.