Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).     

Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy

Abstract

A rapid and accurate method is presented to determine CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃ in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer.     

Near-infrared spectra combined with partial least squares for pH determination of toothpaste of different brands

Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.     

Combining orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping spectra of three kinds of metal ions

A novel method named OSC-WPT-PLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. OSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C. Wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for total elements using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.     

Near-infrared reflection spectroscopy and partial least squares regression for determining the total carbon coverage of silica packings for liquid chromatography

Near-infrared reflection spectroscopy (NIRS) was used in combination with principal component analysis (PCA) and partial least squares (PLS) regression to determine the silica packing properties, whether endcapped (EC) or non-endcapped, and the amount of surface total carbon coverage (%). A preparation technique for the reproducible analysis of bulk materials is covered as well as spectral data pretreatments to enhance prediction accuracy of the PLS models. Especially derivatives and scatter correction methods turned out to be well suited. A standard error of prediction (SEP) of 0.57% C for the determination of the total carbon load of octadecylated (C₁₈) silica was found. In case of the C₁₈-EC silica a SEP of 0.51% carbon gives rise to a robust and accurate model. The presented method allows the simultaneous determination of several parameters, e.g. particle and pore size or surface modifications, from a single spectrum and is amenable to implementation for in-line or on-line analysis in the silica producing industry.     

近红外光谱法快速分析有机肥料中有机质、总养分和pH值

建立了近红外光谱法结合偏最小二乘(PLS)法测定126种有机肥料中有机质、总养分和p H值的快速方法。采用K–S法分类,选取S–G平滑、S–G导数、多元散射校正和均值平均化4种前处理方法对粉碎后样品的近红外光谱信息进行预处理,以PLS法建立定量分析模型。结果表明,有机肥料中总养分的RC,SEC,RP,SEP,RPD分别为0.990,1.272%,0.985,1.084%,5.9;p H值的RC,SEC,RP,SEP,RPD分别为0.910,0.344%,0.737,0.428%,2.9。有机质项目根据国标方法分为小于40%、小于55%和大于55%3种样品进行分析,3种样品的RP分别为1.000,0.989,1.000;RPD分别为18.9,17.5,8.8。对比国标方法,有机质和总养分的测定精度满足实验室精确分析要求,p H值测定法可用于定量分析。NIR–PLS法实现了对有机肥料进行无损快速的检测分析。     

Orthogonal projection to latent structures combined with artificial neural network for quantitative analysis of phenoxymethylpenicillin potassium powder

A method for quantitative analysis of phenoxymethylpenicillin potassium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). Being a preprocessing method, O-PLS can remove systematic orthogonal variation from a given data set X without disturbing the correlation between X and the response set y. In this paper, O-PLS method was applied to preprocess the original spectral data of phenoxymethylpenicillin potassium powder, and the filtered data was used to establish the ANN model. In this model, the concentration of phenoxymethylpenicillin potassium as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare with O-PLS-ANN model, the calibration models that use the original spectra and different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the spectra were also designed. Experimental results show that O-PLS-ANN model is the best.     

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...     

基于OSC–WT–PLS近红外光谱法测定烟草中芸香苷

采用正交信号校正(OSC)结合小波变换(WT)对烟草光谱进行光谱预处理,将预处理后的烟草光谱结合偏最小二乘法(PLS)建立了烟草光谱对芸香苷的预测模型。利用OSC滤除光谱中与芸香苷含量无关的光谱信息,确定OSC提取的最佳主成分数为7,再选择WT中的最佳小波基函数bior1.1对OSC预处理后的光谱进行压缩及进一步滤噪,然后进行PLS建模,OSC–WT–PLS所建模型决定系数r~2=0.874,校正标准偏差RMSEC=0.85,预测均方根误差RMSEP=0.743,交互验证系数Q_(ext)~2=0.887。结果表明,用OSC–WT–PLS可滤除光谱信息中与待测样品含量无关的信息、减少光谱数据量,降低建立模型的复杂度、提高建模速度及模型的预测能力、准确度。     

Near-infrared mass median particle size determination of lactose monohydrate, evaluating several chemometric approaches

The influence of particle size on near-infra red (NIR) spectra is typically considered a 'nuisance factor' which many scatter correction methods attempt to eliminate, e.g., multiplicative scatter correction. However, particle size is a key issue in the formulation of many pharmaceutical products and has a profound effect on the behaviour of both raw materials and drug substances during formulation. NIR has already been demonstrated as a potential alternative particle sizing technique to current accepted methodology. This investigation assessed several chemometric approaches that model this information, using lactose monohydrate as the raw material. A variety of modelling techniques were applied to both zero order and second derivative spectra namely multiple linear regression, partial least squares, principal component regression and artificial neural networks. One further data transformation evaluated was polar coordinates, although no statistical data were generated. Typically, cross-validation root mean square errors of calibration and cross-validation root mean square errors of prediction of approximately 5 microns were calculated for all of the modelling techniques. These values are comparable to those associated with the reference technique (laser diffractometry). Correlation coefficients of approximately 0.98 for all techniques were also calculated. The predictive abilities for models generated using second derivative spectra were found to be comparable to those obtained using zero order spectra.     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

Characterization of surfactant complex mixtures using Raman spectroscopy and signal extraction methods: Application to laundry detergent deformulation

This paper presents the analysis of surfactants in complex mixtures using Raman spectroscopy combined with signal extraction (SE) methods. Surfactants are the most important component in laundry detergents. Both their identification and quantification are required for quality control and regulation purposes. Several synthetic mixtures of four surfactants contained in an Ecolabel laundry detergent were prepared and analyzed by Raman spectroscopy. SE methods, Independent Component Analysis and Multivariate Curve Resolution, were then applied to spectral data for surfactant identification and quantification. The influence of several pre-processing treatments (normalization, baseline correction, scatter correction and smoothing) on SE performances were evaluated by experimental design. By using optimal pre-processing strategy, SE methods allowed satisfactorily both identifying and quantifying the four surfactants. When applied to the pre-processed Raman spectrum of the Ecolabel laundry detergent sample, SE models remained robust enough to predict the surfactant concentrations with sufficient precision for deformulation purpose. Comparatively, a supervised modeling technique (PLS regression) was very efficient to quantify the four surfactants in synthetic mixtures but appeared less effective than SE methods when applied to the Raman spectrum of the detergent sample. PLS seemed too sensitive to the other components contained in the laundry detergent while SE methods were more robust. The results obtained demonstrated the interest of SE methods in the context of deformulation.     

Sparse matrix transform based weight updating in partial least squares regression

Regression from high dimensional observation vectors is particularly difficult when training data is limited. Partial least squares (PLS) partly solves the high dimensional regression problem by projecting the data to latent variables space. The key issue in PLS is the computation of weight vector which describes the covariance between the responses and observations. For small-sample-size and high-dimensional regression problem, the covariance estimation is usually inaccurate and the correlated components in the predictors will distort the PLS weight. In this paper, we propose a sparse matrix transform (SMT) based PLS (SMT-PLS) method for high-dimensional spectroscopy regression. In SMT-PLS, the observation data is first decorrelated by SMT. Then, in the decorrelated data space, the PLS loading weight is computed by least squares regression. SMT technique provides an accurate data covariance estimation, which can overcome the effect of small-sample-size and benefit both the PLS weight computation and subsequent regression prediction. The proposed SMT-PLS method is compared, in terms of root mean square errors of prediction, to PLS, Power PLS and PLS with orthogonal scatter correction on four real spectroscopic data sets. Experimental results demonstrate the efficacy and effectiveness of our proposed method.     

Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology

This study aims to establish a rapid quantitative analysis method for biochar based on near infrared spectroscopy (NIRS) technology. Near infrared spectra of 163 samples in the 10000–3800 cm^–1 (1000–2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were also analyzed. A partial least square (PLS) model for FC, VM and Ash was established after the model spectral ranges were optimized, the optimal factors were determined, and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC + SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R²_p of actual values vs prediction values for FC, VM and ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square error of prediction (RMSEP) was 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model had good accuracy and precision for both of FC and VM, and could be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model need to improve the precision for Ash analysis according to RPD value. This method provides a fast and effective technical means for the quantitative analysis of biochar components.     

激光诱导击穿光谱结合CARS变量选择方法定量检测倍硫磷含量

利用双脉冲激光诱导击穿光谱(LIBS)技术对溶液中的倍硫磷含量进行定量检测。采用二通道高精度光谱仪采集不同浓度倍硫磷样品在206.28~481.77 nm波段的LIBS光谱,并对光谱进行多元散射校正(MSC)、标准正态变量变换(SNV)及3点平滑预处理,根据偏最小二乘(PLS)建模确定最优的预处理方法。在此基础上,利用竞争性自适应重加权算法(CARS)筛选与倍硫磷相关的重要变量,然后应用PLS回归建立溶液中倍硫磷含量的定量分析模型,并与单变量定量分析模型及未变量选择的PLS定量分析模型进行比较。结果表明,相比单变量定量分析模型及原始光谱PLS定量分析模型,CARS-PLS定量分析模型的性能更优,其模型的校正集和预测集的决定系数及平均相对误差分别为0.969 4、15.537%和0.995 9、5.016%。此外,与原始光谱PLS模型相比,CARS-PLS模型仅使用其中1.9%的波长变量,但预测集平均误差却由9.829%下降为5.016%。由此可见,LIBS技术检测溶液中的倍硫磷含量具有一定的可行性,且CARS方法能简化定量分析模型,提高模型的预测精度。     

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.     

Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy

It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at μg kg⁻¹ level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred μg kg⁻¹.     

近红外光谱结合膜富集技术测定饮料中微量邻苯二甲酸二异辛酯的含量

提出了一种基于在线膜富集的近红外漫反射光谱技术,对饮料中的微量塑化剂邻苯二甲酸二异辛酯(DEHP)进行快速检测。采用聚醚砜膜对饮料中的DEHP进行富集,将富集DEHP的膜直接进行近红外漫反射检测。参考DEHP的透射近红外光谱,对波数进行选择,以4 420～4 060、4 700～4 540、6 040～5 600cm-1作为建模的波数区间。通过比较原始光谱、多元散射校正、一阶求导、二阶求导及其组合,考察了光谱预处理方法对模型的影响,用去一交互验证法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对校正集样品进行了预测。结果表明,在选定的波数区间,当用一阶求导对校正集光谱进行预处理时,所建立的模型对校正集的预测效果最佳,在隐变量数为7时,对校正集所有样品的校正均方根误差(RMSEC)为0.188 7mg/L。用此模型对预测集样品进行预测时,DEHP的质量浓度在0.5～5.0 mg/L范围内,预测均方根误差(RMSEP)为0.232 4 mg/L,平均相对预测误差为6.29%。