主成分分析-二阶导数在中红外光谱定量分析中的应用

介绍了运用最小二乘法建立傅立叶变换中红外光谱定量分析模型的原理和方法。以苯甲酸和邻苯二甲酸氢钾为实验材料获取红外吸收光谱,采用MATLAB工程语言编程,分别以吸收光谱和二阶导数光谱为校正集样本,采用主成分分析方法对样本进行优选压缩,以建立了中红外光谱定量分析模型。用此模型预测混合物的含量,预测值与实际值的相对误差小于5.0%。     

近红外光谱小波投影寻踪回归定量分析方法研究

为改进近红外光谱结构特征与定量回归模型的非线性拟合度和充分利用光谱中的非线性特征,提出了一种光谱小波投影寻踪定量分析方法。该方法对光谱进行小波分解后,用高斯混合模型噪声估计法降噪,对降噪后的小波系数向最优投影方向降维,用多项式岭函数拟合定量回归关系。建立黄酒近红外光谱快速预测酒精度小波投影寻踪回归模型,其相关系数R2和交叉检验标准差RMSECV分别为0.957和0.37838,该法比分析多元线性回归和偏最小二乘回归定量分析2种常规定量分析方法具有更优的预测效果,能更为有效地应用于近红外光谱快速定量分析检测。     

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。     

近红外光谱法测定茶多酚中总儿茶素含量

以高效液相色谱(HPLC)分析结果为参考值,建立了快速测量茶多酚中总儿茶素含量的近红外光谱定标模型.将48份茶多酚样品组成定标样品集,在1000～2500nm(4000～10000cm-1)的近红外漫反射光谱为定标波长范围内,光谱经一阶导数(Firstderivative)、二阶导数(Secondderivative)、标准归一化(Stan-dardnormalvariate,SNV)和多元散射校正(multiplicativesignalcorrection,MSC)处理后结合偏最小二乘回归(PLS)定标.经内部交叉验证表明,光谱经SNV处理后建模结果最佳.模型的相关系数Corr.Coeff=0.997,校正均方根RMSEC=1.71%.比较了经典最小二乘法(CLS)、偏最小二乘法(PLS)和主成分回归(PCR)等方法建模结果,以偏最小二乘回归建模效果最好.     

医疗资源配置的动态生成系数灰色预测模型北大核心CSSCI

针对人口密集型都市的医疗资源配置缺乏准确性预测作为实时决策依据的难题,利用灰色理论的小样本建模优势进行预测方法拓展优化,建立等维递补灰色预测方法以提高灰区间白色度和淡化灰平面灰度;挖掘灰色生成系数与外部影响因素间的内在映射关联,提出了动态生成系数优化的灰色理论医疗需求预测方法;动态拟合人口总量变化与灰色生成系数以实现预测模型实时重构,解决了传统灰色预测方法的纯样本序列建模局限,显著提高了预测算法的准确性,输出的医疗需求趋势可有力支撑医疗资源配置决策。     

医疗资源配置的动态生成系数灰色预测模型

针对人口密集型都市的医疗资源配置缺乏准确性预测作为实时决策依据的难题,利用灰色理论的小样本建模优势进行预测方法拓展优化,建立等维递补灰色预测方法以提高灰区间白色度和淡化灰平面灰度;挖掘灰色生成系数与外部影响因素间的内在映射关联,提出了动态生成系数优化的灰色理论医疗需求预测方法;动态拟合人口总量变化与灰色生成系数以实现预测模型实时重构,解决了传统灰色预测方法的纯样本序列建模局限,显著提高了预测算法的准确性,输出的医疗需求趋势可有力支撑医疗资源配置决策。     

基于粗糙集与最小二乘支持向量回归的汽轮机主蒸汽流量预测

针对传统主蒸汽流量计算方法的不足,提出了一种新的主蒸汽流量预测方法,该方法综合了粗糙集理论与最小二乘支持向量回归算法的优点,利用ROSETTA V1.4.41研究实验平台中的遗传约简算法对输入变量的属性进行约简,再利用最小二乘支持向量回归算法建立主蒸汽流量的预测模型。实验表明,与未经粗糙集理论处理过的BP神经网络、支持向量回归算法和最小二乘支持向量回归算法所建模型相比,该方法具有更好的预测精度和泛化能力,且建模速度显著提高。     

基于局部均值分解多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断

提出了一种基于局部均值分解多尺度模糊熵和灰色相似关联度相结合的滚动轴承故障诊断方法。该方法将故障信号自适应地分解为若干乘积函数,并从中选取包含主要故障信息的PF分量计算多尺度模糊熵作为特征向量,通过计算待识别样本与标准故障模式的灰色相似关联度,对滚动轴承故障类型和损伤程度进行判断。将该方法与LMD模糊熵和灰色相似关联度相结合的方法进行了对比,实验表明,基于LMD多尺度模糊熵和灰色相似关联度的滚动轴承故障诊断方法,能够有效地识别滚动轴承运行状态,实现对滚动轴承的故障诊断。     

时延系统中T-LSSVR动态软测量建模方法研究

针对工业过程中由于系统存在延时导致软测量模型难以建立、模型精度偏低等问题,提出将系统延时(T)与最小二乘支持向量回归机(LSSVR)相结合,构建一种基于T-LSSVR的动态软测量建模方法;该方法在建模过程中利用互相关函数与一阶广义差分算法辨识得到“静态响应延时”和“动态响应延时”,通过软测量手段对变量进行预测以实现辅助变量对主导变量的最佳估计。对某化工企业具有此类双延时性质的系统进行实验,实验结果表明该建模方法在动态和稳态数据预测方面都有良好的预测效果。     

单基发射药生产过程中驱水棉水分含量的快速检测方法?

由于驱水棉的水分含量对单基发射药成型工艺有着较大的影响,采用近红外光谱分析技术对驱水棉水分含量进行快速检测。通过对比分析纯水、硝化棉(NC)、乙醇以及驱水棉的光谱图,确定了水分含量检测建模区域为5 015.6~5 224.8 cm~(-1)和6 525.9~7 008.7 cm~(-1)。比较不同光谱预处理方法,发现标准正态变量校正(SNV)、一阶导数和平滑的组合方法对驱水棉的光谱进行预处理效果最好。采用偏最小二乘法对水分含量建立定量校正模型,并对预测集样本进行预测和对模型进行重复性验证。试验结果表明:校正集和交互验证相关系数R2分别为0.995 4和0.994 4,预测均方根误差RMSEP值为0.039 0,对预测集的药料样本预测的平均相对误差为0.997%,模型的重复性良好,检测时间小于20 s,能满足单基发射药连续自动化生产工艺的要求。     

Importance of prediction outlier diagnostics in determining a successful inter-vendor multivariate calibration model transfer

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.     

Multivariate calibration and instrument standardization for the rapid detection of diethylene glycol in glycerin by Raman spectroscopy

The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.     

Transfer of near-infrared calibration model for determining fiber content in flax: effects of transfer samples and standardization procedure

The transfer of a calibration model for determining fiber content in flax stem was accomplished between two near-infrared spectrometers, which are the same brand but which require a standardization. In this paper, three factors, including transfer sample set, spectral type, and standardization method, were investigated to obtain the best standardization result. Twelve standardization files were produced from two sets of the transfer sample (sealed reference standards and a subset of the prediction set), two types of the transfer sample spectra (raw and preprocessed spectra), and three standardization methods (direct standardization (DS), piecewise direct standardization (PDS), and double window piecewise direct standardization (DWPDS)). The efficacy of the model transfer was evaluated based on the root mean square error of prediction, calculated using the independent prediction samples. Results indicated that the standardization using the sealed reference standards was unacceptable, but the standardization using the prediction subset was adequate. The use of the preprocessed spectra of the transfer samples led to the calibration transfers that were successful, especially for the PDS and the DWPDS correction. Finally, standardization using the prediction subset and their preprocessed spectra with DWPDS correction proved to be the best method for transferring the model.     

改进的快速跟踪回声状态网络及PM2.5预测

针对递归最小二乘回声状态网络在噪声环境中预测精度不高的问题,提出了一种改进的快速跟踪回声状态网络。首先在递归最小二乘回声状态网络结构的基础上,将自适应调节的可变遗忘因子加入其代价函数中,用改进的递归最小二乘法对网络输出权值进行训练,得到快速跟踪回声状态网络;然后利用经典Lorenz混沌系统验证快速跟踪回声状态网络的有效性;最后利用灰关联法分析各相关变量与PM2.5的关联度,建立PM2.5浓度值辅助变量集合,将辅助变量集合输入到快速跟踪回声状态网络进行PM2.5浓度值预测。实验表明,与传统回声状态网络、递归最小二乘回声状态网络预测效果相比,快速跟踪回声状态网络的预测方法精度佳,抗噪声能力强。     

Cyclic subspace regression with analysis of wavelength-selection criteria

Common methods of building linear calibration models are principal component regression (PCR), partial least squares (PLS), and least squares (LS). Recently, the method of cyclic subspace regression (CSR) has been presented and shown to provide PCR, PLS, LS and other related intermediate regressions with one algorithm. When forming a linear model with spectral data for quantitative analysis, prediction results can be adversely affected by responses that do not conform well to the linear model proposed. Wavelength selection can be used to eliminate wavelengths where such problem responses occur. It has recently been reported that CSR regression vectors can be formed by summing weighted eigenvectors where weights are determined from the hat matrix, singular values, and eigenvectors characterizing the sample space. Investigation of these weights shows that wavelength selection based on loading vectors can be misleading. Specifically, by using CSR it is shown that a small weight for an eigenvector can annihilate a large peak in a loading vector. In this study, correlograms are used with CSR regression vectors and eigenvector weights as wavelength-selection criteria. It is demonstrated that even though a model generated by LS for a wavelength subset produces substantially reduced prediction errors relative to PCR and PLS, CSR weight plots show that the LS model overfits and should not be used. Simulated situations containing spectral regions with excess noise or nonlinear responses are examined to study the effectiveness of wavelength selection based on the previously listed criteria. Near infrared spectra of gasoline samples with several known properties are also studied.     

Determination of glucose in whole blood samples by mid-infrared spectroscopy

We have determined the glucose concentration of whole blood from mid-infrared spectra without sample preparation or use of chemical reagents. We selected 1119-1022 cm(-1) as the optimal wavelength range for our measurement by making a first-loading vector analysis based on partial least-squares regression. We examined the influence of hemoglobin on samples by using different calibration and prediction sets. The accuracy of glucose prediction depended on the hemoglobin level in the calibration model; the sample set should represent the entire range of hemoglobin concentration. We obtained an accuracy of 5.9% in glucose prediction, and this value is well within a clinically acceptable range.     

Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: comparison of a new calibration transfer method with a slope/bias correction method

This paper reports the prediction of the ethylene content (C2 content) in random polypropylene (RPP) and block polypropylene (BPP) in the melt state by near-infrared (NIR) spectroscopy and chemometrics. NIR spectra of RPP and BPP in the melt states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system. The NIR spectra of RPP and BPP were compared. Partial least-squares (PLS) regression calibration models predicting the ethylene (C2) content that were developed by using each RPP or BPP spectra set separately yielded good results (SECV (standard error of cross validation): RPP, 0.16%; BPP, 0.31%; correlation coefficient: RPP, 0.998; BPP, 0.996). We also built a common PLS calibration model by using both the RPP and the BPP spectra set. The results showed that the common calibration model has larger SECV values than the models based on the RPP or the BPP spectra sets individually and is not practical for the prediction of the C2 content. We further investigated whether a calibration model developed by using the BPP spectra set can predict the C2 contents in the RPP sample set. If this is possible, it can save a significant amount of work and cost. The results showed that the use of the BPP model for the RPP sample set is difficult, and vice versa, because there are some differences in the molar absorption coefficients between the RPP and BPP spectra. To solve this problem, a transfer method from one sample spectra (BPP) set to the other spectra (RPP) set was studied. A difference spectrum between an RPP spectrum and a BPP spectrum was used to transfer from the BPP calibration set to the RPP calibration set. The prediction result (SEP (standard error of prediction), 0.23%, correlation coefficient, 0.994) of RPP samples by the transferred calibration set and model showed that it is possible to transfer from the BPP calibration set to the RPP calibration set. We also studied the transfer from the RPP calibration set (the range of C2 content: 0-4.3%) to the BPP calibration set. The prediction result of C2 content (the range of C2 contents: 0-7.7%) in BPP by use of the calibration model based on the transferred BPP spectra from the RPP spectra showed that the transfer method is only effective for the interpolation of the C2 content range by the nonlinear change in the peak intensities with the C2 content.     

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.     

Measurement of pesticide residues in food based on diffuse reflectance IR spectroscopy

This paper describes nondestructive pesticide measurement of agricultural products based on Fourier transform infrared diffuse reflectance spectroscopy (FT-IR-DRS). Both FT-IR-DRS spectra and the concentration of the pesticide residues are measured for real lettuce samples. Thereafter, the calibration models to estimate the residual concentration of the pesticides are derived by the partial least square regression of the spectra. Cross validations of the calibration models are also carried out. By using this method, it takes two minutes to measure the multi-elements of pesticide residues in a sample lettuce head. Food safety inspection could be enhanced based on FT-IR-DRS.     

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.