近红外光谱快速测定复方磺胺甲噁唑药效成分

基于近红外漫反射光谱技术,利用偏最小二乘多元校正方法建立了复方磺胺甲噁唑片中的两个有效成分磺胺甲噁唑(SMZ)和甲氧苄啶(TMP)含量的快速同时测定方法。对于SMZ和TMP定量分析模型,相关系数分别为99.969%与99.938%,校正集残差分别为0.217与0.159,而预测根均方差分别为0.310和0.418。该方法具有简单、快捷、两组分同时准确测定以及样品不经任何预处理等特点。     

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.     

Near-Infrared Spectroscopy Analytical Model Using Ensemble Partial Least Squares Regression

A novel ensemble-based feature selection method was developed which is designated as ensemble partial least squares regression coeffientents (EPRC). It was composed of two steps: generating a series of different single feature selectors and aggregating them to reach a consensus. Specifically, the bootstrap resampling approach was used to generate a diversity of single feature selectors, and the absolute values of the regression coefficients of the partial least squares (PLS) model were used to rank the features. Next, these feature rankings out of single feature selectors were aggregated by the weighted-sum approach. Finally, coupled with the regression model, the features selected by EPRC were evaluated through cross validation and an independent test set. By experiments of constructing the spectroscopy analysis model on three near infrared spectroscopy (NIRS) datasets, it was shown that the EPRC located key wavelengths, gave a promotion to regression performance, and was more stable and interpretable to the domain experts.     

Multi-way partial least squares modeling of water quality data

A 10 years surface water quality data set pertaining to a polluted river was analyzed using partial least squares (PLS) regression models. Both the unfold-PLS and N-PLS (tri-PLS and quadri-PLS) models were calibrated through leave-one out cross-validation method. These were applied to the multivariate, multi-way data array with a view to assess and compare their predictive capabilities for biochemical oxygen demand (BOD) of river water in terms of their relative mean squares error of cross-validation, prediction and variance captured. The sum of squares of residuals and leverages were computed and analyzed to identify the sites, variables, years and months which may have influence on the constructed model. Both the tri- and quadri-PLS models yielded relatively low validation error as compared to unfold-PLS and captured high variance in model. Moreover, both of these methods produced acceptable model precision and accuracy. In case of tri-PLS the root mean squares errors were 1.65 and 2.17 for calibration and prediction, respectively; whereas these were 2.58 and 1.09 for quadri-PLS. At a preliminary level it seems that BOD can be predicted but a different data arrangement is needed. Moreover, analysis of the scores and loadings plots of the N-PLS models could provide information on time evolution of the river water quality.     

Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

New approach for chemometrics algorithm named region orthogonal signal correction (ROSC) has been introduced to improve the predictive ability of PLS models for biomedical components in blood serum developed from their NIR spectra in the 1280-1849 nm region. Firstly, a moving window partial least squares regression (MWPLSR) method was employed to locate the region due to water as a region of interference signals and to find the informative regions of glucose, albumin, cholesterol and triglyceride from NIR spectra of bovine serum samples. Next, a novel chemometrics method named searching combination moving window partial least squares (SCMWPLS) was used to optimize those informative regions. Then, the specific regions that contained the information of water, glucose, albumin, cholesterol and triglyceride were obtained. When an interested component in the bovine serum solution, such as glucose, albumin, cholesterol or triglyceride is being an analyte, the other three interests and water are considered as the interference factors. Thus, new approach for ROSC has employed for each specific region of interference signal to calculate the orthogonal components to the concentrations of analyte that were removed specifically from the NIR spectra of bovine serum in the region of 1280-1849 nm and the highest interference signal for model of analyte will be revealed. The comparison of PLS results for glucose, albumin, cholesterol and triglyceride built by using the whole region of original spectra and those developed by using the optimized regions suggested by SCMWPLS of original spectra, spectra treated OSC for orthogonal components of 1-3 and spectra treated ROSC using selected removing the highest interference signals from the spectra for orthogonal components of 1-3 are reported. It has been found that new approach of ROSC to remove the highest interference signal located by SCMWPLS improves of the performance of PLS modeling, yielding the lower RMSECV and smaller number of PLS factors.     

Stacked partial least squares regression analysis for spectral calibration and prediction

Two novel algorithms which employ the idea of stacked generalization or stacked regression, stacked partial least squares (SPLS) and stacked moving‐window partial least squares (SMWPLS) are reported in the present paper. The new algorithms establish parallel, conventional PLS models based on all intervals of a set of spectra to take advantage of the information from the whole spectrum by incorporating parallel models in a way to emphasize intervals highly related to the target property. It is theoretically and experimentally illustrated that the predictive ability of these two stacked methods combining all subsets or intervals of the whole spectrum is never poorer than that of a PLS model based only on the best interval. These two stacking algorithms generate more parsimonious regression models with better predictive power than conventional PLS, and perform best when the spectral information is neither isolated to a single, small region, nor spread uniformly over the response. A simulation data set is employed in this work not only to demonstrate this improvement, but also to demonstrate that stacked regressions have the potential capability of predicting property information from an outlier spectrum in the prediction set. Moisture, oil, protein and starch in Cargill corn samples have been successfully predicted by these new algorithms, as well as hydroxyl number for different instruments of terpolymer samples including and excluding an outlier spectrum. Copyright © 2009 John Wiley & Sons, Ltd.     

Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation

An outlier detection method is proposed for near-infrared spectral analysis. The underlying philosophy of the method is that,in random test(Monte Carlo) cross-validation,the probability of outliers presenting in good models with smaller prediction residual error sum of squares(PRESS) or in bad models with larger PRESS should be obviously different from normal samples. The method builds a large number of PLS models by using random test cross-validation at first,then the models are sorted by the PRESS,and at last the outliers are recognized according to the accumulative probability of each sample in the sorted models. For validation of the proposed method,four data sets,including three published data sets and a large data set of tobacco lamina,were investigated. The proposed method was proved to be highly efficient and veracious compared with the conventional leave-one-out(LOO) cross validation method.     

Near‐infrared spectroscopy for the quantitative determination of mass transfer and water absorption kinetics by a polymer solution

This article reports a new method to quantify the water absorption kinetics and the mass transfer in a polymer solution by using near‐infrared (NIR) spectroscopy and partial least‐squares (PLS) models, while it is exposed to a humid atmosphere. Polymer solutions used in this study were made with highly polar solvents exhibiting both a high affinity for water and a low volatility such as dimethylformamide, dimethylacetamide, and N‐methylpyrrolidone. Poly(ethersulfone) and poly(etherimide) were chosen as polymer models as the method could provide useful information for coating process and membrane fabrication monitoring. Whereas gravimetric kinetics yield data on the overall mass transfer, including both water absorption and solvent evaporation, in situ analyses using NIR can quantify separately the solvent and nonsolvent concentration change in the polymer solution. Quantitative models were developed using PLS regression to predict the local water, polymer, and solvent weight fractions in the polymer solution. The method was proved to be suitable for the different studied systems and allowed to infer mass transfers until the onset of the phase separation process. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 48: 1960–1969, 2010     

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.     

Algorithms for multi‐group PLS

Several approaches of investigation of the relationships between two datasets where the individuals are structured into groups are discussed. These strategies fit within the framework of partial least squares (PLS) regression. Each strategy of analysis is introduced on the basis of a maximization criterion, which involves the covariances between components associated with the groups of individuals in each dataset. Thereafter, algorithms are proposed to solve these maximization problems. The strategies of analysis can be considered as extensions of multi‐group principal components analysis to the context of PLS regression. Copyright © 2014 John Wiley & Sons, Ltd.     

近红外光谱快速测定高浓度烟酰胺

利用烟酰胺在乙醇溶液中波段范围为9001-8060cm^-1和7443-7144cm^-1的近红外一阶导数吸收光谱，经过中心化、矢量归一化预处理，应用偏最小二乘法回归来消除溶剂乙醇的近红外吸收干扰，建立了快速高浓度烟酰胺的方法。54个样本作为校正集，PLS最佳回归因子数为4时，决定系数等于0.997；线性范围为0.13-0.70mol/L。本方法应用于9个待测样品，预测相对偏差小于2.9%，结果令人满意，同时还讨论了一些影响回归精度的因素。     

Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.     

Quantitative determination by temperature dependent near-infrared spectra

Near-infrared (NIR) spectra are sensitive to the variation of experimental conditions, such as temperature. In this work, the relationship between NIR absorption spectra and temperature was quantitatively analyzed and applied to the quantitative determination of the compositions in mixtures. It was found that, for the solvents such as water and ethanol, a quantitative spectra-temperature relationship (QSTR) model between NIR spectra and temperature can be established by using partial least squares (PLS) regression. Therefore, the temperature of a solution can be predicted by using the model and NIR spectrum. Furthermore, it was also found that the difference between the predicted results of different solutions is a quantitative reflection of concentration. The variation of intercept in the relationship of the predicted and measured temperature can be used to determine the concentration of the compositions. The mixtures of water, methanol, ethanol and ethylenediamine in a concentration range of 5-80% (v/v) were studied. The calibration curves are found to be reliable with the correlation coefficients (R) higher than 0.99. Both the QSTR and calibration model may extend the application of NIR spectroscopy and provide novel techniques for analytical chemistry.     

偏最小二乘法测定复方乙酰水杨酸片中的有效成分

将偏最小二乘法(PLS)与近红外漫反射光谱法相结合,对复方乙酰水杨酸片进行无损非破坏定量分析．建立了最佳的数学校正模型,比较了样品中3种有效成分(乙酰水杨酸、非那西丁和咖啡因)同时测定和单独测定时的主成分数对PLS定量预测能力的影响,预测了未知样品。3种有效成分同时测定和单独测定建立的PLS模型具有相同的主成分数,PLS预报浓度与参考浓度具有相近的标准偏差,说明用PLS法同时测定3种组分的含量是可行的。     

茶叶浸出液中农药残留表面拉曼光谱检测与初步定量分析

选取甲基对硫磷和水胺硫磷为研究对象,改良了传统的QuEChERS前处理工艺,以自制纳米金溶胶为增强基底,利用表面增强拉曼光谱(SERS)技术,对茶叶浸出液中的农药残留进行检测。通过比对两种有机磷农药的拉曼特征峰进行定性分析。同时,选取570,1034,1107和1202 cm^-1等拉曼位移附近的特征峰光谱数据,利用微分等数学手段,结合偏最小二乘法(PLSR)建立回归方程,预测样品中农药残留含量。所得预测数值与气相色谱-质谱联用(GC-MS)法检测值对比,验证本方法的可行性与可信度。结果表明:基于SERS技术对上述两种有机磷农药的检出限可达0.05 mg/L;通过数学模型分析建立回归方程,其线性相关系数范围为0.9077~0.9824,预测均方根误差(RMSEP)范围为0.77%~2.68%;利用回归方程得到的预测值与GC-MS检测结果基本接近,相对误差范围-5.16%~9.03%,回收率为81.4%~115.1%,说明可以用SERS技术对茶叶浸出液中的有机磷农药残留进行定性和初步定量分析。     

基于迭代缩减窗口自助软收缩算法的近红外光谱变量选择方法研究

该文针对近红外光谱因冗余变量导致的标定模型预测性能差的问题,提出了一种迭代缩减窗口自助软收缩（ISWBOSS）算法。该方法使用窗口对变量进行划分,随机抽取窗口并利用其中的变量建立子模型,计算窗口内变量回归系数的归一化并作为权重继续进行加权采样,从而逐步实现变量空间的软收缩。同时在迭代过程中不断缩减窗口大小对特征变量进行精确搜索。通过在玉米数据集上进行验证,并与全谱法、遗传算法、竞争自适应重加权采样法和自助软收缩法建立的偏最小二乘模型对比,结果表明,新方法不论在准确性还是稳定性上都具有显著优势。以玉米蛋白质含量预测为例,与自助软收缩算法相比,ISWBOSS的预测均方根误差从0.041 8降至0.010 3,且达到最优模型所需的迭代次数更少,运算效率更高。该方法对提高近红外光谱标定模型的性能具有一定的指导意义。     

A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations

The analytical determination of aminoglycosides in pharmaceutical formulations is very difficult due to the lack of chromophores or fluorophores. Several analytical methods have been developed along the years mainly based on derivatization reactions. The European Pharmacopeia (EP) and the United States Pharmacopeia (USP) describe a microbiological assay to the quantification of aminoglycosides. Near infrared spectroscopy (NIRS) can be used alternatively to analyse aminoglycosides without the need of derivatization reactions or other type of sample processing. A new NIRS based method was developed for the analysis of the aminoglycoside antibiotic neomycin. The method was developed with samples based on a commercial formulation containing neomycin sulphate and three excipients: lactose, talc and magnesium stearate. Synthetic and doped samples were manufactured for this purpose. Three lots of a commercial solid formulation were also used to assess the validity of the method to quantify neomycin sulphate in the industrial pharmaceutical product. The method proposes measurements in reflectance mode using a Fourier-transform near infrared (FT-NIR) spectrometer. Partial least squares regression was the multivariate method adopted to calibrate the NIR spectra with the neomycin sulphate mass fraction. The concentration of neomycin sulphate present in the commercial samples was confirmed by HPLC with pre-column derivatization with phenylisocyanate. Results show that neomycin sulphate was determined successfully in the commercial samples using the method calibrated with the doped samples (mass fraction error of 6.6%). Moreover, the synthetic samples were found to be unqualified to develop the method, producing a biased calibration.     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

Rapid quality assessment of Gentianae Macrophyllae Radix based on near-infrared spectroscopy and capillary electrophoresis

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near-infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside, and roburic acid were considered quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside, and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM.