基于拉曼光谱的预调鸡尾酒成分在线检测

基于激光拉曼光谱技术实现了鸡尾酒中果糖和乙醇的快速定量检测。根据果糖和乙醇的分子结构不同分析,两者的拉曼光谱有明显区别的波峰。分别采用了去基线(de-baseline)算法和去趋势(de-trending)算法进行光谱预处理,选取0～2000之间的光谱建立了线性偏最小二乘(PLS)回归法定量分析模型,并用13组成分已知的样品数据对模型进行外部交叉验证。实验结果显示:鸡尾酒中果糖和乙醇成分检测结果,经过去基线预处理光谱的PLS模型优于去趋势算法光谱预处理的PLS模型,其验证结果的相关系数R分别为0. 9981和0. 9998,均方根误差RMSEP分别为0. 0101和0. 0038。实验结果表明基于激光拉曼光谱技术快速定量检测鸡尾酒中果糖和乙醇的成分是可行的,并取得了较好的精度。     

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。     

基于k-means聚类挑选特征样品建立烟草近红外光谱模型

为突破建立近红外光谱模型需要大量实验数据这一瓶颈,实现快速建立烟草近红外光谱预测数学模型,本研究利用k-means聚类分析算法挑选出具有代表性的特征样品光谱,采用偏最小二乘(PLS)回归法建立了烟草6项常规化学指标的近红外光谱模型,并对模型进行了优化及外部验证。结果表明:烟草6项常规化学指标预测模型的相关系数(R)较大,在0.9356以上,交叉验证均方差(RMSECV)在0.867以内,外部验证均方差(RMSEP)小于1.71,预测值和标准值没有显著差异。同时,预测结果还与用大量样品建立的模型的预测结果进行了t检验方式的比较,其结果无显著性差异,说明用挑选特征样品建立的模型和使用大量样品建立的模型分析效果一致,可用于批量烟草样品常规化学指标的快速检测。     

基于shenk’算法的烟叶近红外光谱模型转移研究

面对近红外应用模型共享较难问题，研究采用shenk’s算法解决烟叶化学成分近红外光谱分析模型应用中的模型转移。为实现烟叶近红外光谱模型转移，以山东、云南、四川、广西四个产区的烟叶作为分析对象，采用ThermofisherNicoletAntrislI相同型号的两台仪器采集近红外光谱分别作为主机和子机，在主机上建模，用shenk’s和斜率／截距两种转移算法对子机进行模型转移对比分析。同时，使用ICCA国际标准和t检验两种方法评价模型的传递性。研究结果表明，利用shenk’s算法能够从根本上对模型进行转移，满足模型传递误差要求，且预测结果与主机不存在显著性差异，斜率／截距法没达到稳定的效果，所以shenk’s算法可有效的实现烟叶模型传递问题。     

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.     

基于遗传算法-阻容模型的建筑模型辨识及温度预测研究

为实现精确预测建筑室内温度,得到最优控制策略,基于不同的简化假设,设计不同结构的建筑热阻容模型(RC模型),推导表征模型的微分方程组,在仿真结果的基础上采用遗传算法对模型进行辨识,得到一种兼顾模型预测精度和模型复杂度的RC模型结构。结果表明,在4种模型中,模型3对训练集温度的预测误差为0.346℃,对测试集温度的预测均方根误差为0.419℃,且模型结构较为简单,能够对建筑室内温度实现准确、稳定、相对快速的预测。     

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。     

Gd2O2S:Tb3+X-射线纳米荧光粉的燃烧法合成

采用燃烧法,以稀土硝酸盐和二硫代乙二酰胺为反应物,在点燃温度为300-350℃时,制备了Gd2O2S:Tb3 X-射线纳米荧光粉.分别以X-射线衍射(XRD)、扫描电子显微镜(SEM)、光致发光(PL)光谱及X-射线激发的发光(XEL)光谱对样品进行了表征.XRD分析表明,当焙烧温度小于500℃时,可得到单一相的Gd2O2S:Tb3 X-射线纳米荧光粉;当温度为700℃时,样品完全转变为Gd2O2SO4相.SEM照片给出荧光粉具有疏松和多孔的块状及连续的三维的网络结构.PL和XEL光谱均呈现出激活剂Tb3 离子的较强的特征发射,但它们的峰位略有差别.此外,本实验Tb3 离子的最佳掺杂浓度是0.35mol%.     

近红外光谱结合模式识别算法溯源识别卷烟纸油污

为实现对油渍污染卷烟的快速识别及溯源。采集卷烟生产过程中使用的6种润滑油污染在4种卷烟纸上样品的近红外光谱,结合未污染样品的近红外光谱,优选出原始样品原始光谱及二阶导数光谱中共有的差异性光谱波段范围,采用模式识别算法(principal component analysis-mahalanobis distance, PCA-MD)分别建立污染及未污染样品模式识别模型和不同卷烟上6种润滑油的模式识别模型。结果表明:1)基于污染及未污染样品原始近红外光谱及二阶求导光谱差异性分析,优化出的模式识别模型建模波数范围是:6 000～5 300 cm~(–1)和4 500～4 000 cm~(–1);2)建立的污染及未污染模式识别模型前3个主成分累计得分贡献率97.826%,模型分类效果明显,建模集及外部验证集样品的识别准确率均为100%;3)分别建立的6种油渍在4种卷烟纸上的溯源类模型,前3个主成分得分累计贡献率均大于96%,模型分类效果明显,建模集及外部验证集样品的识别准确率均为100%。所建立的基于近红外光谱分析方法结合模式算法(PCA-MD)可实现卷烟生产过程出现的"黄斑烟"中油渍烟的快速识别及污染油渍溯源。     

微晶玻璃的制备与光谱特性

高温熔制摩尔组分为32CaO-12Y₂O₃-24Al₂O₃-31SiO₂-1Yb₂O₃的玻璃, 制得的玻璃于950、1050、1100℃三个不同温度进行热处理, 用XRD分析热处理后样品的相变, 用TEM观察1050℃热处理后的样品, 并研究了1050℃热处理前后样品的光谱特性. 研究结果表明: 玻璃在1050℃热处理后, 在玻璃中产生单一YAG相微晶颗粒; 热处理前后样品光谱特性的变化表明热处理后掺杂的Yb ³⁺离子择优进入到YAG晶格位, 制备得到了透明Yb:YAG微晶玻璃.     

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.     

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.     

Correction of temperature-induced spectral variation by continuous piecewise direct standardization

In process analytical applications it is not always possible to keep the measurement conditions constant. However, fluctuations in external variables such as temperature can have a strong influence on measurement results. For example, nonlinear temperature effects on near-infrared (NIR) spectra may lead to a strongly biased prediction result from multivariate calibration models such as PLS. A new method, called Continuous Piecewise Direct Standardization (CPDS) has been developed for the correction of such external influences. It represents a generalization of the discrete PDS calibration transfer method and is able to adjust for continuous nonlinear influences such as the temperature effects on spectra. It was applied to shortwave NIR spectra of ethanol/water/2-propanol mixtures measured at different temperatures in the range 30-70 degrees C. The method was able to remove, almost completely, the temperature effects on the spectra, and prediction of the mole fractions of the chemical components was close to the results obtained at constant temperature.     

Temperature-independent near-infrared analysis of lysozyme aqueous solutions

Digital Fourier filtering is used to produce a temperature-insensitive univariate calibration model for measuring lysozyme in aqueous solutions. Absorbance spectra over the 5000-4000 cm-1 spectral range are collected for lysozyme standards maintained at 14 degrees C. These spectra are used to compute the calibration model while a set of spectra collected at temperatures ranging from 4 to 24 degrees C are used to validate the accuracy of this model. The root-mean-square error of prediction (RMSEP) is 0.279 mg/mL over a tested lysozyme concentration range of 0.036-51.6 mg/mL. The detection limit is 0.68 mg/mL. In addition, multivariate calibration models based on partial least-squares regression (PLS) are evaluated and compared to the results from the univariate model. PLS outperforms the univariate model by providing a RMSEP of 0.090 mg/mL. Analysis of variance showed that both calibration methods effectively eliminate the adverse affects created by variations in solution temperature.     

Comparison of methods for transfer of calibration models in near-infared spectroscopy: a case study based on correcting path length differences using fiber-optic transmittance probes in in-line near-infrared spectroscopy

This article addresses problems related to transfer of calibration models due to variations in distance between the transmittance fiber-optic probes. The data have been generated using a mixture design and measured at five different probe distances. A number of techniques reported in the literature have been compared. These include multiplicative scatter correction (MSC), path length correction (PLC), finite impulse response (FIR), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and robust calibration. The quality of the predictions was expressed in terms of root mean square error of prediction (RMSEP). Robust calibration gave good calibration transfer results, while the other methods did not give acceptable results.     

Determination of the polymorphic forms of bicifadine hydrochloride by differential scanning calorimetry-thermogravimetric analysis, X-ray powder diffraction, attenuated total reflectance-infrared spectroscopy, and attenuated total reflectance-near-infrared spectroscopy

The pharmaceutical compound bicifadine hydrochloride, which has been found to crystallize in two polymorphic forms, has been characterized by thermal analysis, X-ray powder diffraction (XRPD), infrared (IR) spectroscopy, and near-infrared (NIR) spectroscopy. A series of 22 sample mixtures of polymorph 1 and polymorph 2 were prepared and calibration models for the quantitation of these binary mixtures have been developed for each of the XRPD, attenuated total reflectance (ATR)-IR, and ATR-NIR analytical techniques. The quantitative results were obtained using a partial least squares (PLS) algorithm, which predicted the concentration of polymorph 1 from the XRPD spectra with a root mean standard error of prediction (RMSEP) of 4.4%, from the IR spectra with a RMSEP of 3.8%, and from the NIR spectra with a RMSEP of 1.4%. The studies indicate that when analyses are carried out on equivalent sets of spectra, NIR spectroscopy offers significant advantages in quantitative accuracy as a tool for the determination of polymorphs in the solid state and is also more convenient to use than both the ATR-IR and XRPD methods. Density functional theory (DFT) B3LYP calculations and IR spectral simulation have been used to determine the nature of the vibrational modes that are the most sensitive in the analysis.     

The magnetic transfer process

The transfer of information from a magnetic surface master to a magnetic surface slave when they are brought in to intimate contact in the presence of an external magnetic field is discussed. The only effective component of the transfer field was found to be the component in the plane of transfer and collinear with the recorded magnetization. Steady fields were found to be only slightly inferior in transfer efficiency to alternating fields, and the optimum amplitude for both is equal to the remanence coercivity of the slave medium. The basic mechanism of the magnetic transfer process is shown to be the establishment of a relatively large susceptibility in the slave by the transfer field, thus rendering it a good receptor of the flux emanating from the magnetization gradients in the master. Under certain conditions it was found that digital information could be transferred from a high resolution master onto thick or low coercivity media with a significant improvement in resolution over what could be achieved by direct recording with conventional transducers on the same media.     

Determination of glucose in human aqueous humor using Raman spectroscopy and designed-solution calibration

Glucose concentrations of in vitro human aqueous humor (HAH) samples from cataract patients were determined using 785 nm Raman spectra and partial least squares (PLS) calibration. PLS models were created from spectra of prepared calibration solutions rather than aqueous humor samples. Spectra were obtained with an excitation energy (100 mW for 150 s), which was higher than can be applied in vivo, to decrease the models' contribution to prediction uncertainty. The solutions contained experimentally designed levels of glucose, bicarbonate, lactate, urea, and ascorbate. Multiplicative signal correction of spectra helped compensate for the +/-20% drift in laser power observed at the sample over six noncontiguous days of data collection. Seventeen HAH samples containing 38-775 mg/dL of glucose exhibited a root-mean-square error (RMSEP) of 22 mg/dL, coefficient of determination (r(2)) of 0.989, and bias of 6 mg/dL when predicted from lower energy (30 s) spectra collected contemporaneously with fifty calibration spectra. Similar results were obtained even when spectral data were gathered separately for human aqueous humor samples and calibration samples: 10 HAH samples, calibrated on 25 solutions measured 3.6 weeks earlier, exhibited an RMSEP of 23 mg/dL, r(2) of 0.992, and bias of 9 mg/dL. The results demonstrate progress toward the determination of glucose levels in patient-derived aqueous humor using laboratory-derived "artificial aqueous humor" calibration solutions.