Subspace Regression Ensemble Method Based on Variable Clustering for Near-Infrared Spectroscopic Calibration

Abstract

An ensemble approach, based on the combination of multiple linear regressions in subspace and variable clustering and therefore named VCS-MLR, was proposed for near-infrared spectroscopy (NIRS) calibration. By an experiment involving the determination of five components in tobacco samples, it was shown that VCS-MLR improved the performance by 61.4, 23.3, 10.2, 20.5, and 18, respectively, with respect to partial least squares regression (PLSR). The results confirmed that VCS-MLR can result in a more accurate calibration model but without the increase of computational burden. Moreover, the superiority of VCS-MLR was highlighted for small sample problems.     

Ensemble Multivariate Calibration Based on Mutual Information for Food Analysis Using Near-Infrared Spectroscopy

An ensemble multivariate calibration algorithm, termed as MISEPLS, is proposed. In MISEPLS, when constructing a member model, the variables that have mutual information (MI) with the response less than a threshold are eliminated; thus, the modeling can be performed in a subset of original variables and some problems arising from multi-collinearity can be avoided. Through experiments on three near-infrared (NIR) spectroscopic datasets from the food industry, MISEPLS proves to be superior to the single-model full-spectrum PLS and MIPLS (PLS combined with MI-induced variable selection). MISEPLS can improve the accuracy and robustness of a calibration model, without increasing its complexity.     

近红外光谱分析技术在快速分析上的应用

简要介绍了近红外光谱的原理、特点,综述了近红外光谱在农业、食品、制药、石油化工、高分子等领域快速分析上的研究及应用现状,并对近红外光谱的应用前景进行了展望．     

大豆蛋白的中红外和近红外光谱研究*

大豆蛋白在各领域的应用已得到广泛的关注,因此大豆蛋白及其改性材料在结构性能方面的研究显得越来越重要。中红外光谱(mid-infrared spectroscopy,MIR)和近红外光谱(near-infrared spectroscopy,NIR)正是对蛋白质进行定性定量分析的有力手段。中红外光谱可以有效地分析大豆蛋白在溶液和薄膜中的二级结构以及大豆衍生材料内蛋白质的结构变化情况。近红外光谱则在蛋白质定量分析方面有着独特的优势。本文介绍了运用这两种光谱技术进行研究的一些工作,这些实例表明了中红外和近红外光谱在大豆蛋白研究领域的重要应用价值。     

近红外光谱法快速测定豆粕中混入的玉米粉含量

通过对豆粕样品和混有部分玉米粉的豆粕样品的近红外光谱分析，结果表明：混合样品中淀粉的含量与样品在２０６０ｎｍ，２０９０ｎｍ和 ２１６６ｎｍ处的光谱数据具有高的相关系数，然后应用回归分析计算出线性方程，再根据玉米粉中的淀粉含是一计算混合样品中的玉米粉含量。     

Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy

Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.     

Nondestructive Discrimination of Pharmaceutical Preparations Using Near-Infrared Spectroscopy and Partial Least-Squares Discriminant Analysis

Metronidazole is a widely used antibacterial and amoebicide drug. The feasibility of the classification of metronidazole samples with respect to their brands was investigated by near-infrared (NIR) spectroscopy along with chemometrics. A total of 92 samples of different lots and four brands were collected for measurements. First, principal component analysis was conducted to visualize the difference between metronidazole samples of different brands. Then, based on an effective classifier-independent method, i.e., joint mutual information, only the 30 most important variables were selected for modeling. From the independent test set, the partial least-squares discriminant analysis model based on the reduced variable set was compared with the corresponding full-spectrum model using all variables, which indicates the model based on the reduced variable set outperforms the full-spectrum model. It appears that the combination of NIR spectroscopy, joint mutual information, and partial least-squares discriminant analysis is a potential method for the classification of metronidazole from different brands and can, therefore, be used in the screening of counterfeit pharmaceutical products.     

Quantitative Analysis of Uncoated Eszopiclone Tablets by Near-Infrared Spectroscopy

This study employed near-infrared (NIR) spectroscopy to analyze content uniformity, moisture content, compression force, tablet hardness, average particle size, and particle-size distribution. The content uniformity, moisture content, compression force, tablet hardness, and average particle size models yielded high correlation coefficients (R²) of 0.99582, 0.99725, 0.99620, 0.96294, and 0.98421, respectively, whereas the particle size distribution models showed good predictive ability. Conventional criteria such as R², root-mean-square error of calibration, and the root-mean-square error of prediction were used to evaluate the accuracy and precision of the model. To ensure the accuracy and predictability of the content model for low-dose tablets, additional validation and reliability evaluations were performed using 70%, 80%, 100%, 120%, and 130% drug concentrations as well as 90% and 110% active content formulations. Near-infrared spectroscopy with multivariate modeling is a rapid, nondestructive technique for the characterization of the manufacturing process.     

温控近红外光谱用于葡萄糖的高灵敏检测

温控近红外光谱在混合体系定量分析以及分子间相互作用分析中已得到应用.本工作利用互因子分析方法(MFA)探索了温控近红外光谱技术测定血清样品中低浓度葡萄糖含量的可行性.测量了不同温度下葡萄糖含量分别为1.0～15.0 mmol/L和0.0～1.0 mmol/L的两组血清溶液的近红外光谱,并利用MFA进行了定量计算.结果表明,利用该方法可以实现样品中微量葡萄糖的准确定量,MFA提取的标准信号(SS)的相对强度和葡萄糖浓度之间的线性相关系数R分别为0.9923和0.9895,预测均方根误差(RMSEP)分别为0.35和0.07 mmol/L.     

Nondestructive Quantitative Analysis of Analgini Powder Pharmaceutical by Near-Infrared Spectroscopy and Artifical Neural Network Technique

ABSTRACT

This paper demonstrates the usefulness of near-infrared (NIR) spectra and artificial neural network (ANN) in nondestructive quantitative analysis of pharmaceuticals. Real data sets from near-infrared reflectance spectra of analgini powder pharmaceutical were used to build up an artificial neural network to predict unknown samples. The parameters affecting the network were discussed. A new network evaluation criterion, the degree of approximation, was employed. The overfitting was discussed. Owing to the good nonlinear multivariate calibration nature of ANN, the predicted result was reliable and precise. The relative error of unknown samples was less than 2.5%     

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.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

用X-射线光电子能谱定性和定量分析钴的化学状态

在Ni-MH和Ni-Cd电池制造业中,对钴的价态确定是重要的。高自旋二价钴给出较强的Shake-up伴峰,而三价钴是反磁性的,没有伴峰,由此可以确定钴的价态,并进行定量分析。由实验结果得出下面的经验方程：X%=100（13-20C）/13（C 1）。式中X%是三价钴的百分含量,C是待测样品Co2p1/2Shake-up伴峰与主峰强度之比。此方程可以给出三价钴近似含量。我们从Cop1/2与Shake-up伴峰之间距离或者从伴峰与主峰强度之比,可以鉴别氢氧化亚钴与氧化亚钴。     

Determination of Chlorpyrifos Residue by Near-Infrared Spectroscopy in White Radish Based on Interval Partial Least Square (iPLS) Model

Abstract

This article presents a multivariate method of rapidly determining chlopyrifos residue in white radish, based on near-infrared spectroscopy and partial least squares (PLS) regression. Interval PLS (iPLS) was utilized to select the optimum wave number range. The number of PLS components and the number of intervals were optimized according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The result showed that the iPLS model was more reliable than the full model and that near-infrared spectroscopy with iPLS algorithm could be used successfully to analyze chlorpyrifos residue in white radish.     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

Investigation of Water Interaction with Polymer Matrices by Near-Infrared (NIR) Spectroscopy

The interaction of water with polymers is an intensively studied topic. Vibrational spectroscopy techniques, mid-infrared (MIR) and Raman, were often used to investigate the properties of water–polymer systems. On the other hand, relatively little attention has been given to the potential of using near-infrared (NIR) spectroscopy (12,500–4000 cm⁻¹; 800–2500 nm) for exploring this problem. NIR spectroscopy delivers exclusive opportunities for the investigation of molecular structure and interactions. This technique derives information from overtones and combination bands, which provide unique insights into molecular interactions. It is also very well suited for the investigation of aqueous systems, as both the bands of water and the polymer can be reliably acquired in a range of concentrations in a more straightforward manner than it is possible with MIR spectroscopy. In this study, we applied NIR spectroscopy to investigate interactions of water with polymers of varying hydrophobicity: polytetrafluoroethylene (PTFE), polypropylene (PP), polystyrene (PS), polyvinylchloride (PVC), polyoxymethylene (POM), polyamide 6 (PA), lignin (Lig), chitin (Chi) and cellulose (Cell). Polymer–water mixtures in the concentration range of water between 1–10%(w/w) were investigated. Spectra analysis and interpretation were performed with the use of difference spectroscopy, Principal Component Analysis (PCA), Median Linkage Clustering (MLC), Partial Least Squares Regression (PLSR), Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) and Two-Dimensional Correlation Spectroscopy (2D-COS). Additionally, from the obtained data, aquagrams were constructed and interpreted with aid of the conclusions drawn from the conventional approaches. We deepened insights into the problem of water bands obscuring compound-specific signals in the NIR spectrum, which is often a limiting factor in analytical applications. The study unveiled clearly visible trends in NIR spectra associated with the chemical nature of the polymer and its increasing hydrophilicity. We demonstrated that changes in the NIR spectrum of water are manifested even in the case of interaction with highly hydrophobic polymers (e.g., PTFE). Furthermore, the unveiled spectral patterns of water in the presence of different polymers were found to be dissimilar between the two major water bands in NIR spectrum (ν_s + ν_as and ν_as + δ).     

Identification of Colorectal Cancer Using Near-Infrared Spectroscopy and Adaboost with Decision Stump

Rapid and objective detection of cancer is crucial for successful treatment. Near-infrared (NIR) spectroscopy is a vibrational technique capable of optically probing molecular changes associated with disease. The purpose of this study was to explore NIR spectroscopy for discriminating cancer from normal colorectal tissues. A total of 110 tissue samples from patients who underwent operations were characterized in this study. The popular ensemble technique AdaBoost was used to construct the diagnostic model. A decision stump was used as the weak learning algorithm. Adaboost with decision stump, an ensemble of weak classifiers, was compared with the most suitable single model, a strong classifier. Only the 20 most significant variables were selected as inputs for the model based on measured defined variable importance. Using an independent test set, the single strong classifier provided diagnostic accuracy of 89.1%, sensitivity of 100%, and specificity of 78.6%, whereas the ensemble of weak stumps provided accuracy of 96.3%, sensitivity of 96.3%, and specificity of 96.3% for distinguishing cancer from normal colorectal tissues. Therefore, NIR spectroscopy in combination with AdaBoost with decision stumps has demonstrated potential for rapid and objective diagnosis of colorectal cancer.     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Characterization of Toad Skin for Traditional Chinese Medicine by Near-Infrared Spectroscopy and Chemometrics

Extraction is a widely used manufacturing protocol for medicinal materials. Near-infrared spectroscopy was implemented to simultaneously and nondestructively monitor toad skin extraction and to determine the ideal end point. A fiber optic probe was coupled to the extraction setup to collect real-time spectra. Four mathematical algorithms, including spectra dissimilarity, moving block standard deviation, mean square, and principal component analysis, were investigated to find a suitable method to determine the end point of extraction. The moving block standard deviation was the most effective method to evaluate the end point. Multivariate models based on partial least square regression were established to monitor changes. Four quantitative models for the indole alkaloid concentration and total solids in two extraction processes provided favorable statistics, and the determination coefficients of the calibration models exceeded 0.95. For indole alkaloids and total solids in the first and second extractions, the latent variables were 8, 5, 5, and 4, respectively; the root mean square error of cross-validation values were 8.26?mg?mL^?1, 0.18%, 7.50?mg?mL^?1, and 0.16%; and the root mean square error of prediction results were 11.10?mg?mL^?1, 0.27%, 4.89?mg?mL^?1, and 0.14%. The extraction monitoring system was able to ensure product quality and batch consistency while providing lower cost and faster analysis for pharmaceutical applications.