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.     

近红外光谱技术检测烟叶中的氨基酸

研究应用傅里叶变换近红外光谱法快速测定烟叶中氨基酸含量的可行性,使用偏最小二乘法(PLS)为建模方法,选择3800~8000 cm-1谱段,采用二阶导数和Norris Derivative滤波法进行光谱预处理,建立了烟叶中氨基酸含量的近红外预测模型。采用留一(leave-one-out)交叉验证法进行建模,并以校正集样品的交叉验证相关系数(R)和均方差(RMSECV)为指标优化光谱预处理方法和模型参数,确定最佳预测模型。将近红外光谱技术与常规标准检测方法相比较,结果表明,近红外光谱技术可以较为准确的测定烟叶中氨基酸的含量。     

近红外漫反射光谱法快速检测火药吸收药混合液中黑索今组分含量

研究了采用近红外( NIR)漫反射光谱技术快速检测火药吸收药混合液中黑索今( RDX)组分含量的方法。将装有混合液样品的烧杯置于光谱仪主机光源窗口上方,直接采集样品光谱图。通过分析纯RDX和样本的近红外光谱,确定908~945 nm、1094~1253 nm和1577~1678 nm作为建模谱区。通过比较不同的光谱预处理办法的效果,选择标准正态变换( SNV )＋一阶导数＋谱线平滑对原始光谱进行预处理。采用偏最小二乘方法(PLS)对RDX组分建立了定量线性校正模型。结果表明：模型的交叉验证决定系数(R2cv)为0.9879,交叉验证均方根误差(RMSECV)为0.2420,预测均方根误差(RMSEP)为0.2127,预测结果的平均相对误差为0.5661％,25 s内完成样品分析。该方法可给改性双基火药的连续自动化生产提供技术支持。     

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。     

绿茶汤中茶多酚近红外定量分析的光程选择

研究了光程对近红外定量分析绿茶汤中茶多酚的影响.以不同光程(1 mm,2 mm,5 mm)的样品池采集50个绿茶汤样品的近红外透射光谱.采用偏最小二乘法(PLS)建立茶汤中茶多酚的定量分析模型,并验证模型的准确度和精密度.结果表明,选择1 mm光程光谱建立的茶多酚定量分析模型最优,模型的校正集相关系数R2和内部交叉验证均方根RMSECV分别为91.08%和0.009 3%;检验集相关系数R2和预测标准差SEP为91.53%和0.008 5%;实际值与预测值配对t检验值为0.224 9,差异不显著.10次重复测量相对标准偏差RSD为0.008 7%,表明方法检测重复性好.     

不同光照下的近红外光谱模型传递研究

针对室外光照对近红外光谱检测带来误差的问题,提出基于模型传递来减少检测误差的方法。以圆黄梨为样品,分析样品在室内、室外阴影下的近红外光谱,建立室内光谱的偏最小二乘(PLS)模型。采用直接校正(direct standardization,DS)算法,减小室内外光谱差距,使得室内PLS模型能预测室外光谱。结果表明:在室内建立的模型能预测经DS算法传递后的室外光谱,预测决定系数(r2p)和预测均方根误差(root mean square error of prediction,RMSEP)分别为0.71和0.374,能有效解决室外光照对光谱检测的影响。     

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...     

近红外光谱技术的恩施玉露原产地鲜叶收购价格评估

为科学客观地评估鲜叶收购价格,应用近红外光谱技术结合人工神经网络方法和联合区间偏最小二乘法,建立了三种鲜叶收购价格预测模型并比较了预测效果.应用联合区间偏最小二乘法筛选最佳光谱区间为5 750~6 000cm-1,7 750~8 000cm-1,8 250~8 500cm-1,8 500~8 750cm-1,9 500~9 750cm-1和9 750~10 000cm-1,并对上述光谱进行主成分分析.前5个主成分累计贡献率为99.87%,并以此为输入值建立收购价格人工神经网络预测模型(R2=0.968 7,RMSEP=4.625).模型预测结果优于全波长人工神经网络模型(R2=0.855 1,RMSEP=5.218)和联合区间偏最小二乘法模型(R2=0.581 6,RMSEP=25.433)的预测结果.近红外光谱技术结合人工神经网络和联合区间偏最小二乘法,能够快速、准确、客观的评估鲜叶收购价格,有利于统一鲜叶收购价格标准,有效地减少纠纷.     

近红外光谱模型优选应用于甘蔗清糖浆锤度快速分析

利用偏最小二乘法(PLS)和光谱Savitzky-Golay(SG)平滑方法,建立甘蔗清糖浆锤度近红外光谱分析的优化模型。SG平滑模式的扩展、SG平滑模式和PLS因子数的联合大范围筛选能够有效地应用于近红外光谱分析的模型优化。     

Evaluation of physical properties of human hair by diffuse reflectance near-infrared spectroscopy

The objective of the present study is to develop a novel nondestructive, simple, and quick method to evaluate the friction, twist, and gloss of human hair based on near-infrared diffuse reflectance (NIR-DR) spectroscopy and chemometrics. NIR-DR spectra were measured for human hair, which was collected from eleven Japanese women (age 5-44 years), by use of an optical fiber probe. Partial least squares (PLS) regression has been applied to the NIR-DR spectra of human hair after mean centering (MC), standard normal variate (SNV), and first derivative (1d) or second derivative (2d) analysis to develop calibration models that predict the friction, twist, and gloss of human hair. We identified the most suitable wavenumber region for the evaluation of each physical property. Correlation coefficients and standard errors of calibration of the PLS calibration models for the friction, twist, and gloss of hair were calculated to be 0.96 and 0.023, 0.81 and 3.27, and 0.90 and 0.36, respectively. Thus, the calibration models have high accuracy.     

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.     

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。     

Noninvasive in vivo measurement of venous blood pH during exercise using near-infrared reflectance spectroscopy

Blood pH is an important indicator of anaerobic metabolism in exercising muscle. This paper demonstrates multivariate calibration techniques that can be used to produce a general pH model that can be applied to spectra from any new subject without significant prediction error. Tissue spectra (725 approximately 880 nm) were acquired through the skin overlying the flexor digitorum profundus muscle on the forearms of eight healthy subjects during repetitive hand-grip exercise and referenced to the pH of venous blood drawn from a catheter placed in a vein close to the muscle. Calibration models were developed using multi-subject partial least squares (PLS) and validated using subject-out cross-validation after the subject-to-subject spectral variations were corrected by mathematical preprocessing methods. A combination of standard normal variate (SNV) scaling and principal component analysis loading correction (PCALC) successfully removed most of the subject-to-subject variations and provided the most accurate prediction results.     

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.     

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.     

Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods

Savitzky-Golay (SG) smoothing and moving window partial least square (MWPLS) methods were applied to the model optimization and the waveband selection for near-infrared (NIR) spectroscopy analysis of soil organic matter. The optimal single wavelength prediction bias (OSWPB) was used to evaluate the similarity of calibration set and prediction set, and a new division method for calibration set and prediction set was proposed. SG smoothing modes were expanded to 540 kinds. The specific computer algorithm platforms for optimization of SG smoothing mode combined with PLS factor and for MWPLS method with changeable parameters were built up. The optimal waveband for soil organic matter was 1926-2032 nm, the optimal smoothing mode was the 2nd order derivative, 6th degree polynomial, 45 smoothing points, the PLS factor, RMSEP and R_P were 8, 0.260 (%) and 0.877 respectively. The prediction effect was obviously better than that in the whole spectral collecting region. To get stable results, all the optimization processes were based on the average prediction effect on 50 different divisions of calibration set and prediction set.     

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.     

Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using Fourier transform Raman spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.