A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Partial least square analysis of lysozyme near-infrared spectra

Proteins possess strong absorption features in the combination range (5000-4000 cm⁻¹) of the near infrared (NIR) spectrum. These features can be used for quantitative analysis. Partial least squares (PLS) regression was used to analyze NIR spectra of lysozyme with the leave-one-out, full cross-validation method. A strategy for spectral range optimization with cross-validation PLS calibration was presented. A five-factor PLS model based on the spectral range between 4720 and 4540 cm⁻¹ provided the best calibration model for lysozyme in aqueous solutions. For 47 samples ranging from 0.01 to 10 mg/mL, the root mean square error of prediction was 0.076 mg/mL. This result was compared with values reported in the literature for protein measurements by NIR absorption spectroscopy in human serum and animal cell culture supernatants.     

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.     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

复杂样品近红外光谱定量分析模型的构建方法

针对复杂样品近红外光谱分析中校正集的设计问题, 探讨了标准样品参与复杂样品建模的可行性. 通过标准样品和复杂基质样品共同构建的偏最小二乘(PLS)模型, 考察了波段筛选和建模参数对预测结果的影响. 结果表明, 采用PLS方法建立定量模型时, 校正集样品性质应该尽量与预测集样品相似, 当样品的性质相差较大时, 适当增加校正集样品的差异性可使模型具有更强的预测能力. 同时, 波段优选对提高预测结果的准确性具有重要的意义.     

LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate

A novel near infrared (NIR) modeling method—Laplacian regularized least squares regression (LapRLSR) was presented, which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable.     

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

利用近红外光谱技术对食用植物油中反式脂肪酸(Trans fatty acids,TFA)含量进行快速定量检测,并通过波段选择、预处理方法、变量筛选及建模方法对TFA含量预测模型进行优化.采用AntarisⅡ傅里叶变换近红外光谱仪在4000~10000 cm-1光谱范围采集98个食用植物油样本的近红外透射光谱,然后采用气相色谱法测定TFA的真实含量.首先,对样本原始光谱进行波段、预处理方法优选;在此基础上,采用竞争自适应重加权法(Competitive adaptive reweighted sampling,CARS)筛选TFA相关的重要变量,最后应用主成分回归、偏最小二乘和最小二乘支持向量机方法分别建立食用植物油中TFA含量的预测模型.研究结果表明,近红外光谱技术检测食用植物油中的TFA含量是可行的,优化后的最佳预测模型的校正集和预测集R2分别为0.992和0.989,RMSEC和RMSEP分别为0.071%和0.075%.最佳预测模型所用的变量仅26个,占全波段变量的0.854%.此外,与全波段偏最小二乘预测模型相比,其预测集R2由0.904上升为0.989,RMSEP由0.230%下降为0.075%.由此表明,模型优化非常必要,CARS能有效筛选TFA相关的重要变量,极大减少建模变量数,从而简化预测模型,并较大提高预测模型的精度和稳定性.     

近红外光谱技术定量分析连作滁菊土壤中的阿魏酸含量

应用近红外光谱(NIRS)技术定量分析连作滁菊土壤样品中阿魏酸的含量.通过标准杠杆值、学生残差和马氏距离判断异常光谱,经二阶导数和Norris平滑滤噪预处理后,在6000~4000 cm-1范围,最佳因子数为7,采用偏最小二乘法(PLS)构建数学模型.结果表明,模型校正集和验证集与高效液相色谱仪(HPLC)测定的参考值之间均呈现良好相关关系,校正相关系数Rc为0.9914,交叉验证相关系数Rcv为0.9935,校正集误差均方根(RMSEC)为0.484,预测误差均方根(RMSEP)为0.539,交叉验证误差均方根(RMSECV)为0.615.研究结果表明,NIRS分析技术能够实现连作土壤中阿魏酸的快速检测,结果准确可靠.     

Determination of organic additives in mortars by near-IR spectroscopy. A novel approach to designing a sample set with high-variability components

Industrial mortars consist primarily of a mixture of cement and an aggregate plus a small amount of additives that are used to modify specific properties. Using too high or too low additive rates usually results in the loss of desirable properties in the end product. This entails carefully controlling the amounts of additives added to mortar in order to ensure correct dosing and/or adequate homogeneity in the final mixture. Near-IR (NIR) spectroscopy has proved effective for this purpose as it requires no sample pretreatment and affords expeditious analyses. The purpose of this work was to determine two organic additives (viz. Ad1 and Ad2) in mortars by using partial least squares regression multivariate calibration models constructed from NIR spectroscopic data. The additives are used to expedite setting and increase cohesion between particles in the mortar. In order to ensure that the sample set contained natural variability in the samples, we used a methodology based on experimental design to construct a representative set of samples. This novel design is based on a hexagonal antiprism that encompasses the concentration ranges spanned by the analytes and the variability inherent in each additive. The D-optimality criterion was used to obtain various combinations between Ad1 and Ad2 additive classes. The partial least squares calibration models thus constructed for each additive provided accurate predictions: the intercept and the slope of the plots of predicted values versus reference values for each additive were close to 0 and 1, respectively, and their confidence ranges included the respective value. The ensuing analytical methods were validated by using an external sample set.     

Optimisation of partial least squares regression calibration models in near-infrared spectroscopy: a novel algorithm for wavelength selection

A novel optimisation algorithm is presented for full spectrum calibration models in near-infrared (NIR) spectroscopy. The algorithm is used to investigate the affect of removing continuous spectral regions on parameters critical to the validity of the model (e.g. explained variance, bias etc.) and ultimately identify and remove problem areas of the spectrum. As an example of its application, this paper shows how to optimise partial least squares regression (PLSR) calibration models for predicting moisture content within an intact pharmaceutical product and how problems due to changes in the nature of samples since setting up the original model may be eliminated. On application of two validated calibration models to a new set of samples unacceptable results were obtained for bias (-0.26 and -0.21% m/m moisture content) between the NIR predicted values and the true values (Karl Fischer analysis). The optimisation algorithm identified small regions of the spectrum, which if included in development of the models contributed significant bias to the final prediction. On removal of these problem regions the calibration models were found to be equally accurate and precise, but with the added advantage of robustness to a variable region of the sample spectrum (bias reduced to -0.05 and -0.09% m/m).     

Determination of indomethacin polymorphic contents by chemometric near-infrared spectroscopy and conventional powder X-ray diffractometry

A chemoinfometric method for the quantitative determination of the crystal content of indomethacin (IMC) polymorphs, based on Fourier-transform near-infrared (FT-NIR) spectroscopy, was established. A direct comparison of the data with those collected using the conventional powder X-ray diffraction method was performed. Pure alpha and gamma forms of IMC were prepared using published methods. Powder X-ray diffraction profiles and NIR spectra were recorded for six kinds of standard material with various contents of the gamma form of IMC. Principal component regression (PCR) analyses were performed on the basis of the normalized NIR spectral sets of standard samples with known contents of the gamma form of IMC. A calibration equation was determined to minimize the root mean square error of the prediction. The predicted gamma form contents were reproducible and had a relatively small standard deviation. The values of the gamma form contents predicted by the two methods were in close agreement. The results indicated that NIR spectroscopy provides an accurate quantitative analysis of crystallinity in polymorphs compared with the results obtained by conventional powder X-ray diffractometry.     

Measurement of the percentage volume particle size distribution of powdered microcrystalline cellulose using reflectance near-infrared spectroscopy

This is the first reported method for determining the percentage volume particle size distribution of a powder (microcrystalline cellulose) by near-infrared (NIR) reflectance spectroscopy. A total of 113 samples of powdered microcrystalline cellulose were used from six different commercially available grades, with different moisture contents (range: 0.9-4.8% m/m). NIR reflectance measurements of these samples were made in narrow soda glass vials. Reference particle size data for the samples were acquired by laser diffraction. The NIR data were then calibrated to measure particle size by partial least squares regression. The effects of a range of different NIR data pre-treatments on calibration and prediction precision were investigated. Overall, simple absorbance data were found to produce regression models with the best predictive ability (root mean square error of prediction = 0.90%). The method was also found to be insensitive to moisture content.     

Improving the performance of hollow waveguide-based infrared gas sensors via tailored chemometrics

The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.     

Inverse Optimal Filtering Method for the Instrumental Spreading Correction in Size Exclusion Chromatography

Abstract

The Kalman filter based techniques are adapted to solve the most general form of Tung's integral formula, i. e. when a non-uniform, non-symmetric calibration model is employed to correct chromatograms obtained in size exclusion chromatography from instrumental broadening errors. Through this method, the inverse smoothing of a chromatogram contaminated with measurement noise of known statistics is optimally performed by minimizing the estimation error variance. The method is numerically very “robust”, improves the signal to noise ratio, provides good validation checks, and does not involve any previous parameter estimation procedure.     

Simple and fast near-infrared spectroscopic analysis of hydroxyl number of polyol using a disposable glass vial

Hydroxyl (OH) number of polyol was measured using near-infrared (NIR) spectroscopy with the use of a disposable glass vial as a sample container. Polyols are viscous, so disposable vials are advantageous when spectroscopic methods are employed. Due to the curvature of the vial walls, a narrow aperture was used to minimize the spectroscopic deviations. The narrow aperture attenuated the NIR radiation and increased the spectral noise in the collected polyol spectra. Wavelet transformation (WT) was employed to reduce this noise and partial least squares (PLS) calibration model was developed. The overall prediction results compare well with those from conventional wet analysis that requires time (1–3 h) and large amounts of chemical reagents. NIR spectroscopy with the use of disposable vials can be utilized for a simple and fast quality assurance of polyol in actual industrial settings.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

Quantitative Analysis of Catalpol in Chinese Patent Medicine Lixin Pill by Near-infrared Diffuse Reflectance Spectroscopy

Lixin pill is a typical Chinese patent medicine with anti-rheumatic heart disease activity that has been widely used in clinical practice. Therefore it is very important to detect the concentration of catalpol, as the main component of the active ingredient. Near-infrared reflectance（NIR） spectroscopy was used to study the content of catalpol in the unprocessed Chinese patent medicine of Lixin pills. NIR is applied to quantitatively analyze 77 sam- ples, which were randomly divided into a calibration set containing 61 samples and a prediction set containing 16 samples. To get a satisfying result, partial least squares（PLS） regression was utilized to establish quantitative models. In PLS regression, the values of coefficient of determination（R2） and root mean square error of cross-validation （RMSECV） of PLS regression are 0.9419 and 0.0216, respectively. The process of establishing model, parameters of model, and prediction results were also discussed in detail（root mean square error of prediction is 0.0164）. The over- all results show that NIR spectroscopy can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in the Chinese patent medicine of Lixin pills. The prediction set suggests that this quantitative analysis model has excellent generalization ability and prediction precision. Accordingly, the result can provide tech- nical support for the further analysis of catalpol in unprocessed Lixin pill. Moreover, this study supplied technical support for the further analysis of other Chinese patent medicine samples.