小波域高斯混合模型方差估计近红外降噪方法

针对抑制近红外光谱噪声与保留光谱信号细节的矛盾,提出一种基于噪声方差估计的小波域降噪方法.该法对光谱信号小波域高频系数建立了两状态高斯混合模型,用EM算法估计模型系数,推证模型对噪声方差准确估计特性,将估计得到的噪声方差建立了阈值降噪模型.实验建立黄酒近红外光谱快速预测酒精度偏最小二乘模型,对比分析Penalty阈值、...     

光谱预处理对近红外光谱快速检测黄酒酒精度的影响

本文以黄酒近红外透射光谱为研究对象,探讨光谱预处理方法对黄酒酒精度快速定量检测模型的影响,对比分析了采用平滑、一阶导数、二阶导数、多元散射校正、标准正态变换结合去势等五种传统方法和小波变换、傅里叶变换、正交信号校正等三种新方法预处理后光谱的偏最小二乘建模效果.结果表明,正交信号校正处理后,模型交叉验证R和RMSECV分...     

基于LP与小波包的复杂机电系统监测序列混沌降噪方法

针对流程工业生产系统监测数据存在强噪声和混沌性的特点,提出了一种局部投影方法(Local Projection Method)与小波包方法相结合的信号降噪方法。该方法先利用局部投影方法从动力学系统嵌入流形的角度进行多次迭代降噪,并根据关联维数来判定迭代终止;再利用小波包方法从频率的角度进行降噪,抑制高频噪声的干扰,取得了较好的降噪效果。用Lorenz时间序列进行仿真验证,对仿真时间序列加入不同程度的噪声,对比分析小波包、局部投影与该方法降噪后的相空间、SNR值和最大Lyapunov指数,证明了该方法对于中高强度噪声具有更好的降噪效果。并将该方法用于某压缩机组的实际监测数据降噪,评估三种方法的降噪效果,进一步验证了该方法的优越性。     

近红外漫反射光谱结合神经网络检测小麦蛋白质含量

为了实现对小麦蛋白质含量的快速检测,提出了基于近红外光谱结合神经网络的小麦蛋白质检测方法.以160个小麦样品为对象,采集其近红外漫反射光谱,并以国标法分析小麦样品蛋白质含量,作为参考值.样品随机分成预测样品集和定标样品集,其光谱经标准归一化、去趋势等预处理后,采用BP神经网络和偏最小二乘法分别建立蛋白质含量定标模型.BP神经网络模型的预测相关系数和预测均方根误差分别为0.98和0.270 4%.而偏最小二乘法模型的预测相关性系数和预测均方根误差分别为0.98和0.303 8%.结果表明,两种方法建立的模型都具有较好的预测相关性和预测效果,其中BP神经网络模型优于偏最小二乘法模型.用非线性BP神经网络结合相应算法建立模型检测小麦蛋白质含量的定标模型可以提高检测准确性.     

光谱预处理对棉涤混纺面料近红外定量模型的影响

以46个棉涤混纺面料样品为研究对象,采集样品的近红外漫反射光谱,光谱范围为12 000~4 000 cm-1,利用偏最小二乘法建立定量校正模型,并用交叉检验法对模型进行检验,以交叉验证均方差RMSECV和决定系数R2作为判断模型优劣的标准.对利用无光谱预处理、一阶导数法、二阶导数法、多元散射校正和矢量归一化五种不同预处理方法所建的模型进行了比较,发现对光谱进行矢量归一化预处理所建模型最优;此外还分析了建立纺织布料的近红外光谱定量分析模型时主要的误差来源及近红外光谱分析技术用于纺织面料定量分析的可行性.     

基于可见/近红外光谱分析技术的水性油墨黏度 预测模型

针对水性油墨黏度测量方法存在操作复杂、主观性强等问题,利用可见/近红外光谱分析技术结合化学计量学方法,建立水性油墨黏度预测模型,实现水性油墨黏度的快速无损检测。首先,利用微型光纤光谱仪采集水性油墨样本的反射光谱;再通过比较不同预处理方法对原始光谱数据的预处理效果,分别基于原始全光谱及预处理后的光谱数据构建水性油墨黏度的偏最小二乘回归（PLSR）和主成分回归（PCR）预测模型;最后,将预处理后的光谱数据采用连续投影算法（SPA）和竞争性自适应重加权算法（CARS）提取特征波长,并基于特征波长的光谱数据建立水性油墨黏度的PLS预测回归模型。结果表明,采用SPA算法从全光谱中只提取了4个特征波长,不仅显著简化了模型,提升了模型的运算效率,建立的SNV-SPA-PLS模型还具有最佳的预测性能（Rp2=0.9992, RMSEP=0.0732）。该研究结果表明应用光谱分析技术实现对水性油墨黏度检测是有效可行的,为进一步通过光谱分析技术进行水性油墨在线黏度检测提供了新方法,为提高印刷品质量稳定性提供了技术基础。     

投影寻踪方法在设备预知维护中的应用研究EI北大核心CSCD

提出了一种融合投影寻踪的自回归分析方法实现设备的预知维护。该预知维护方法是从设备关键部件处提取振动信号,经分析和计算得出24种特征指标用以描述设备运行状态;对24种特征指标分别提取一个时间序列并各自进行自回归分析,得到各自对应的预测因子;利用投影寻踪将前述预测因子投影到二维空间,然后分别建立预测因子投影值与相对应的特征指标值的拟合函数,进而推算出24种特征指标的未来值;再通过对24种特征指标的未来值在最佳投影方向矩阵下进行投影,根据投影值的分布情况判断设备未来运行状态是否存在异常,从而实现设备的预知维护。最后利用美国西储大学轴承数据中心网站公开发布的轴承探伤测试数据集中的内圈故障数据和山西省某洗煤厂主井皮带机的减速器故障数据进行了验证。     

基于近红外光谱技术定量分析导热油的研究

导热油在设备热传递领域应用广泛,但目前市场上导热油的品质良莠不齐,优质导热油掺杂劣质导热油会导致一系列安全问题。常用的导热油鉴定手段耗时长,检测复杂。本实验借助近红外光谱技术结合偏最小二乘回归法(Partial Least Squares Regression,PLSR)用于鉴定导热油品质。结果表明:鉴定导热油品质的模型,经标准正态变量(standard normal variate,SNV)预处理后的定量分析模型的拟合程度最佳,预测相关系数(regression coefficient of prediction,Rp)达到0.9999,预测均方根误差(the root mean square error of prediction,RMSEP)小于0.02。因此,近红外光谱技术结合PLSR可为导热油的品质鉴定提供一种快速、便捷的方法。     

基于近红外光谱分析技术的液态水性油墨印刷品 颜色预测模型

针对印刷品颜色离线检测存在滞后、检测不精准等问题,提出基于近红外光谱分析技术的液态水性油墨印刷品颜色预测模型。用多元散射校正（MSC） 、标准正态变换（SNV）和卷积平滑滤波器（SG）对原始光谱数据进行预处理,将原始光谱数据及预处理后的光谱数据分别与印刷品的Lab值建立偏最小二乘回归（PLSR）和主成分回归（PCR）两种预测模型。结果表明,基于MSC预处理的PLSR预测模型的预测精度最高,L、a、b值的R2分别高达0.9885, 0.9879和0.9938,预测颜色的平均色差约为0.71。液态水性油墨的近红外光谱可以精确预测印刷品颜色,为印刷品的在线检测提供了新思路。     

投影寻踪方法在设备预知维护中的应用研究

提出了一种融合投影寻踪的自回归分析方法实现设备的预知维护。该预知维护方法是从设备关键部件处提取振动信号,经分析和计算得出24种特征指标用以描述设备运行状态;对24种特征指标分别提取一个时间序列并各自进行自回归分析,得到各自对应的预测因子;利用投影寻踪将前述预测因子投影到二维空间,然后分别建立预测因子投影值与相对应的特征指标值的拟合函数,进而推算出24种特征指标的未来值;再通过对24种特征指标的未来值在最佳投影方向矩阵下进行投影,根据投影值的分布情况判断设备未来运行状态是否存在异常,从而实现设备的预知维护。最后利用美国西储大学轴承数据中心网站公开发布的轴承探伤测试数据集中的内圈故障数据和山西省某洗煤厂主井皮带机的减速器故障数据进行了验证。     

基于PCA-SVMR快速测定复方氯丙那林和对乙酰氨基酚

基于主成分分析-支持向量机回归(PCA-SVMR)方法,利用近红外光谱技术研究了复方氯丙那林和复方对乙酰氨基酚两种模型制剂有效组分的快速同时测定,建立了它们的多元校正模型,并以传统的稳健方法偏最小二乘回归(PLSR)为基础考察了PCA-SVMR方法对于小样本药物体系的拟合能力、预测能力和模型稳定性。研究表明,PLSR的预测能力必须以强拟合能力为前提,PCA-SVMR则没有这样的要求,使前者对校正样本的依赖性远强于后者,从而在小样本药物体系中前者的稳定性大大弱于后者,该两种药物制剂的PCA-SVMR多元校正模型的测定准确度总体上优于PLSR。     

Bagging for robust non-linear multivariate calibration of spectroscopy

This paper presents the application of the bagging technique for non-linear regression models to obtain more accurate and robust calibration of spectroscopy. Bagging refers to the combination of multiple models obtained by bootstrap re-sampling with replacement into an ensemble model to reduce prediction errors. It is well suited to “non-robust” models, such as the non-linear calibration methods of artificial neural network (ANN) and Gaussian process regression (GPR), in which small changes in data or model parameters can result in significant change in model predictions. A specific variant of bagging, based on sub-sampling without replacement and named subagging, is also investigated, since it has been reported to possess similar prediction capability to bagging but requires less computation. However, this work shows that the calibration performance of subagging is sensitive to the amount of sub-sampled data, which needs to be determined by computationally intensive cross-validation. Therefore, we suggest that bagging is preferred to subagging in practice. Application study on two near infrared datasets demonstrates the effectiveness of the presented approach.     

Extracting coal ash content from laser-induced breakdown spectroscopy (LIBS) spectra by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) analysis has been applied for the quantitative analysis of the ash content of coal in this paper. The multivariate analysis method was employed to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. In order to construct a rigorous partial least squares regression model and reduce the calculation time, different spectral range data were used to construct partial least squares regression models, and then the performances of these models were compared in terms of the correlation coefficients of calibration and validation and the root mean square errors of calibration and cross-validation. Afterwards, the prediction accuracy, reproducibility, and the limit of detection of the partial least squares regression model were validated with independent laser-induced breakdown spectroscopy measurements of four unknown samples. The results show that a good agreement is observed between the ash content provided by thermo-gravimetric analyzer and the LIBS measurements coupled to the PLS regression model for the unknown samples. The feasibility of extracting coal ash content from LIBS spectra is approved. It is also confirmed that this technique has good potential for quantitative analysis of the ash content of coal.     

The application of near infrared spectroscopy in the quality control analysis of glass/phenolic resin prepreg

During the manufacture of glass/phenolic resin prepreg cloth, the feasibility of near infrared (NIR) spectroscopy as a technique for the quality control analysis of the resin content, the volatile content and the resin pre-curing degree has been verified. The partial least square (PLS) regression was used to develop the calibration models by utilizing several different spectral pretreatments. The optimum models had determination coefficients (R ²) of 98.29 for the resin content, of 99.50 for the volatile content and of 97.66 for the pre-curing degree, respectively. The root mean square errors of prediction (RMSEP) for the resin content, the volatile content and the pre-curing degree were 0.376%, 0.169% and 0.105%, respectively. The results of the paired t-test revealed that there was no significant difference between the NIR method and the standard method. In the manufacture process of the prepreg cloth, the NIR on-line monitoring results were used to be the instructions for the quality control.     

人血清中血糖的近红外光谱快速检测

应用傅利叶变换近红外光谱透射技术结合偏最小二乘法 ( PLS) ,快速定量分析了人血清中血糖含量 .利用内部交叉验证和自动优化功能对预测模型进行了优化 ,确定了最优建模参数 .模型对人血清中葡萄糖定标样品集的实测含量与预测含量的相关系数 r=0 .91 48,内部校正均方差 RMSECV=0 .487mmol/L.     

基于近红外光谱建立大黄鱼新鲜度预测模型

目的 通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法 以菌落总数为鲜度评价指标,基于均值中心化、标准正态变量变换、趋近归一化法（Normalization by Closure, Ncl）、多元散射校正、一阶导数和二阶导数等预处理方法,运用偏最小二乘法（Partial Least Squares, PLS）建模,比较所建模型的定标集与验证集间的相关系数和标准偏差,构建大黄鱼冰藏期间菌落总数的定量模型,以期快速预测其新鲜度。结果 Ncl比其它预处理方法可以更好地消除光谱噪音,提高模型的预测能力。经Ncl光谱预处理,利用PLS建模,可达到最佳的建模效果,其定标集相关系数为0.9095,校正标准偏差相关系数为0.5872,验证集相关系数为0.8858,预测标准偏差为0.6615。模型相关系数>0.9;结论 表明该模型预测精度较好,在大黄鱼新鲜度检测和品质评价方面应用前景良好。     

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.     

On-line dissolution determination of Baicalin in solid dispersion based on near infrared spectroscopy and circulation dissolution system

Drug on-line circulation dissolution system with near infrared spectrophotometer for dissolution determination was reported in this paper and subsequently partial least squares (PLS) calibration model was established for concentration prediction of Baicalin in solid dispersion. When the main factor number in PLS calibration model was 6, the correlation coefficients of PLS calibration samples and prediction ones were all 0.9999 and the relative standard deviations were 0.69% and 1.10%, respectively, which showed good robustness and predictability. Combining drug circulation dissolution system with the PLS calibration model, dissolution of Baicalin in raw material drug and solid dispersion were obtained at different times. The results indicated that the dissolution property of Baicalin in solid dispersion (especially at the early time) had been significantly improved. The accumulated dissolution of Baicalin in the solid dispersion at 45 min reached nearly 40%, increasing by 15% compared with raw material drug (about 25%). The aforementioned PLS model associated with drug circulation dissolution system provided a simple, accurate and on-line support for dissolution determination of drug, especially at the early time of rapid dissolution.     

基于电磁检测的火车车轮残余应力定量预测

目的 为了突破火车车轮残余应力有损测试的局限性、实现车轮残余应力的准确定量预测,研究电磁参量特征值的遴选过程并建立相关模型。方法 对比分析单一电磁参量(磁巴克豪森噪声或增量磁导率)和电磁参量融合(磁巴克豪森噪声和增量磁导率)的检测方法,通过逐步回归对电磁参量的特征值进行遴选,利用多元线性回归方法构建残余应力的定量预测模型。结果 基于单一电磁参量建立的应力预测模型,其残余应力预测值与实际值的偏差超过±15 MPa的允差范围;基于电磁参量融合建立的应力预测模型,其残余应力预测值与实际值的偏差均在±15 MPa的允差范围之内。结论 采用电磁融合方法建立的多元线性模型,可以有效提高模型精度、实现火车车轮残余应力的定量预测。