Novel Spectral Interval Selection Method Based on Synchronous Two-Dimensional Correlation Spectroscopy

Synchronous 2D correlation spectroscopy was first proposed to select informational spectral intervals in PLS calibration. The proposed method could extract the spectral intervals related to analyte. The results of its application to NIR/PLS determination of quercetin in extract of Ginkgo biloba leaves showed that the proposed method could find out an optimized region with which one could improve the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP), and comparing with the result obtained using whole spectra and interval PLS.     

Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination

This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of α-linolenic and linoleic acid in eight types of edible vegetable oils and their blending. For this purpose, a combination of spectral wavelength selection by wavelet transform (WT) and elimination of uninformative variables (UVE) was proposed to obtain simple partial least square (PLS) models based on a small subset of wavelengths. WT was firstly utilized to compress full NIR spectra which contain 1413 redundant variables, and 42 wavelet approximate coefficients were obtained. UVE was then carried out to further select the informative variables. Finally, 27 and 19 wavelet approximate coefficients were selected by UVE for α-linolenic and linoleic acid, respectively. The selected variables were used as inputs of PLS model. Due to original spectra were compressed, and irrelevant variables were eliminated, more parsimonious and efficient model based on WT-UVE was obtained compared with the conventional PLS model with full spectra data. The coefficient of determination (r²) and root mean square error prediction set (RMSEP) for prediction set were 0.9345 and 0.0123 for α-linolenic acid prediction by WT-UVE-PLS model. The r² and RMSEP were 0.9054, 0.0437 for linoleic acid prediction. The good performance showed a potential application using WT-UVE to select NIR effective variables. WT-UVE can both speed up the calculation and improve the predicted results. The results indicated that it was feasible to fast determine α-linolenic acid and linoleic acid content in edible oils using NIR spectroscopy.     

基于小波系数的近红外光谱局部建模方法与应用研究

局部建模方法使用与预测样本相似的样本建立模型,可解决光谱响应与浓度之间的非线性问题,扩大模型的适用范围,提高预测准确度。采用小波变换进行数据压缩并利用小波系数之间的欧氏距离作为光谱相似性的判据,实现了近红外光谱定量分析的局部建模方法,避免了样本之间的依赖性。将所建立的方法用于烟草样品中氯含量的测定,100次重复计算得到的预测集均方根误差(RMSEP)平均值为0.0665,标准偏差(σ)为0.0045,优于全局建模和基于主成分的局部建模方法。     

近红外光谱法测定端羟基环氧乙烷-四氢呋喃共聚醚的羟值

提出了用近红外光谱测定端羟基环氧乙烷-四氢呋喃共聚醚(PET)的羟值,结合主成分回归和偏最小二乘法建立了PET羟值与其近红外光谱之间的关联模型。结果表明,近红外光谱法与化学分析法的测定结果一致;近红外光谱法测定PET羟值的相对误差在5%以内;利用遗传算法选择部分波长建立校正可以降低模型的预测误差。     

粒子群算法结合支持向量机回归法用于近红外光谱建模

研究了最小二乘法支持向量机(LSSVM)应用于烟丝样品和小麦样品的近红外光谱建模,采用粒子群优化算法(PSO)优化LSSVM的参数。通过对烟草样品和小麦样品的近红外光谱建模和预测,并与常规的偏最小二乘法(PLS)比较发现,PSO-LSSVM法具有更好的预测效果和稳健性。     

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.     

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.     

A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples

Abstract

A consensus regression approach based on partial least square (PLS) regression, named as cPLS, for calibrating the NIR data was investigated. In this approach, multiple independent PLS models were developed and integrated into a single consensus model. The utility and merits of the cPLS method were demonstrated by comparing its results with those from a regular PLS method in predicting moisture, oil, protein, and starch contents of corn samples using the NIR spectral data. It was found that cPLS was superior to regular PLS with respect to prediction accuracy and robustness.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

激光诱导击穿光谱(LIBS)结合机器学习算法快速测定石油焦中微量元素

石油焦中微量元素对其作为预焙阳极的性能起着决定性的作用。首先,通过基于LIBS光谱构建用于石油焦中铁(Fe)和铜(Cu)定量分析的PLS校正模型。然后,考察了不同光谱预处理(归一化、多元散射校正、标准正态变换、一阶导数和二阶导数)以及变量选择算法(粒子群优化算法和变量重要性投影)对PLS校正模型预测性能的影响。建立了一种基于激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)结合偏最小二乘(Partial least squares, PLS)的石油焦中微量元素定量分析方法。结果显示,与其他PLS校正模型相比,基于二阶导数和变量重要性投影的PLS模型对Fe的预测性能最优,最优的交叉验证相关系数(R-squared cross validation,R²cv)为0.966 7,均方根误差(Root mean squared error cross validation, RMSEcv)为10.282 1 mg/kg,预测集的相关系数(R-squared prediction,R²p)为0.86...     

Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra.

Near-infrared (NIR) spectrometry will present a more promising tool for quantitative measurement if the robustness and predictive ability of the partial least square (PLS) model are improved. In order to achieve the purpose, we present a new algorithm for simultaneous wavelength selection and outlier detection; at the same time, the problems of background and noise in multivariate calibration are also solved. The strategy is a combination of continuous wavelet transform (CWT) and modified iterative predictors and objects weighting PLS (mIPOW-PLS). CWT is performed as a pretreatment tool for eliminating background and noise synchronously; then, mIPOW-PLS is proposed to remove both the useless wavelengths and the multiple outliers in CWT domain. After pretreatment with CWT-mIPOW-PLS, a PLS model is built finally for prediction. The results indicate that the combination of CWT and mIPOW-PLS produces robust and parsimonious regression models with very few wavelengths.     

Frequency self deconvolution in the quantitative analysis of near infrared spectra

In this paper a new model based on frequency self deconvolution (FSD) is proposed for the quantitative analysis of a near infrared (NIR) spectrum. The model couples FSD and partial least square regression (PLS). The grid search optimization method is used to select the optimal values of the full width at half height (FWHH) and the truncation point of the apodization function. The proposed FSD-PLS provides a significant improvement in the prediction ability of the PLS model. Furthermore, a modification of the new FSD-PLS method is introduced to enable the removal of the baseline variations from the NIR spectra. The proposed models were validated using absorbance spectra of mixtures composed from glucose, urea and triacetin in a phosphate buffer solution where the concentrations of the components are selected to be within their physiological range in blood. The whole experiments were carried out in a non-controlled environment to show that the model can suppress effectively most of the experimental variations. The results show that the standard error of prediction (SEP) decreases from 35.58 mg dL(-1) using 8 factors for the PLS model to 15.53 mg dL(-1) by using 12 factors for the modified FSD-PLS model. The proposed models are also shown to yield a slightly improved performance than a newly developed second derivative-PLS model without incurring the shortcoming associated with the derivative approach in not providing interpretable results and in degrading the SNR of the spectra at a faster rate.     

偏最小二乘回归用于近红外光谱分析的稳健策略

偏最小二乘法（PLS）在近红外光谱（NIR）定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS）方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS（RE-PLS）方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。     

基于灰狼算法的近红外光谱变量选择方法研究

基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.     

A strategy for enhancing the reliability of near-infrared spectral analysis

Near-infrared (NIR) spectroscopy will present a more promising tool for quantitative measurement if the reliability of the calibration model is further improved. To achieve this purpose, a new partial least squares (PLSs) technique based on Monte Carlo (MC) resampling is proposed, which is named as MCPLS. In this method, the outliers are firstly removed based on probability statistics. Then, the models without outliers are averaged and combined into a single prediction model as done in a consensus modeling, which can greatly enhance the reliability of PLS calibration. To validate the effectiveness and universality of the proposed method, it was applied to two different sets of NIR spectra. It was found that MCPLS could effectively avoid the swamping and masking effects caused by multiple outliers. The results show that the method is of value to enhance the reliability of PLS model involving complex NIR matrices with a small number of outliers.     

Near Infrared Spectral Similarity Combined with Variable Selection Method in the Quality Control of Flos Lonicerae: A Preliminary Study

This paper indicates the possibility to use near infrared (NIR) spectral similarity as a rapid method to estimate the quality of Flos Lonicerae. Variable selection together with modelling techniques is utilized to select representative variables that are used to calculate the similarity. NIR is used to build calibration models to predict the bacteriostatic activity of Flos Lonicerae. For the determination of the bacteriostatic activity, the in vitro experiment is used. Models are built for the Gram‐positive bacteria and also for the Gram‐negative bacteria. A genetic algorithm combined with partial least squares regression (GA‐PLS) is used to perform the calibration. The results of GA‐PLS models are compared to interval partial least squares (iPLS) models, full‐spectrum PLS and full‐spectrum principal component regression (PCR) models. Then, the variables in the two GA‐PLS models are combined and then used to calculate the NIR spectral similarity of samples. The similarity based on the characteristic variables and full spectrum is used for evaluating the fingerprints of Flos Lonicerae, respectively. The results show that the combination of variable selection method, modelling techniques and similarity analysis might be a powerful tool for quality control of traditional Chinese medicine (TCM).     

中药口服固体制剂原辅料近红外光谱数据库的构建及应用

建立了中药口服固体制剂原辅料近红外(NIR)光谱数据库,采用模式识别方法研究了NIR光谱数据在物料分类和物性预测中的应用。使用便携式近红外光谱仪快速测量149批原辅料粉末的NIR漫反射光谱数据,并录入iTCM数据库。利用主成分分析(PCA)法探究NIR光谱数据对已知结构物料的分类能力,采用偏最小二乘(PLS)法研究了NIR光谱对原辅料物性参数和直接压片片剂性能的预测能力。经标准正态变量变换(SNV)+Savitzky-Golay(SG)平滑+一阶导数处理后的NIR光谱数据对微晶纤维素、乳糖、乙基纤维素、交联聚维酮和羟丙基甲基纤维素这5类辅料的区分能力较好。NIR光谱数据与原辅料粉末粒径、密度和吸湿性的相关性较强。NIR光谱信息作为物料物理性质的补充,可提高粉末直接压片片剂性能预测模型的性能。NIR光谱数据是iTCM数据库物性参数数据的补充,物性参数与NIR光谱数据的结合能更全面地表征原辅料的性质。