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.     

Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

Hyperspectral images contain both spectral and spatial image information and were investigated to characterize the freshness of fish. However, most studies of this application have focused on spectral signals rather than image features. The goal of this work was to investigate the ability of spectral and image textural variables for predicting the chemical and physical qualities of fish, respectively, and to optimize the variables for the specific quality determination. The chemical (total volatile basic nitrogen, TVB-N) and physical (texture profile analysis, TPA) properties were investigated. Partial least square (PLS) was applied to develop fish quality prediction models with the spectral and textural variables from the hyperspectral images. The results showed that the TVB-N content of fish fillets was accurately predicted using the spectra. Meanwhile, the TPA parameters were determined through the image textural features with high accuracy, which indicated image textural features were highly related with the TPA parameters. Moreover, spectral and textural features were also extracted from fish eyes and gills and were further used to predict the intact fish quality, taking advantage of the freshness sensitivity of the eyes and gills. The results illustrate that spectra from fish eyes and gills are a potential tool to predict the TVB-N content and TPA parameters for intact fish.     

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.     

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。     

Quantitative Determination of the Components in Corn and Tobacco Samples by Using Near-Infrared Spectroscopy and Multiblock Partial Least Squares

Multiblock partial least squares (MB-PLS) are applied for determination of corn and tobacco samples by using near-infrared diffuse reflection spectroscopy. In the model, the spectra are separated into several sub-blocks along the wavenumber, and different latent variable number was used for each sub-block. Compared with ordinary PLS, the importance and the contribution of each sub-block can be balanced by super-weights and the usage of different latent variable numbers. Therefore, the prediction obtained by the MB-PLS model is superior to that of the ordinary PLS, especially for the large data sets of tobacco samples with a large number of variables.     

Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax^®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.     

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).     

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.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

Rapid Simultaneous Determination of Andrographolides in Andrographis paniculata by Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing.     

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).     

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。     

A Robust Near-Infrared Calibration Model for the Determination of Chlorophyll Concentration in Tree Leaves with a Calibration Transfer Method

A method based on piecewise direct standardization was developed to directly predict leaf chlorophyll concentrations by correction of near-infrared spectra to construct a robust calibration model. Chinar, camphor, and gingko leaves collected from two growth intervals were evaluated. Spectral pretreatment methods and wavelength selection were investigated. The first derivative combined with stability competitive adaptive reweighted sampling before piecewise direct standardization provided the best performance. Under the optimized parameters, the root mean square error of prediction was significantly reduced by using piecewise direct standardization. This study demonstrates that the calibration model may be used to rapidly characterize chlorophyll concentrations across species and growth intervals.     

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.     

Determination of Bovine Hemoglobin by Near-Infrared Diffuse Reflectance Spectroscopy with Novel Adsorption Preconcentration

A method for the quantitative determination of bovine hemoglobin in dilute solution was developed using adsorption preconcentration and near-infrared diffuse reflectance spectroscopy. An adsorbent, designated as multicarbonyl polymer-grafted silica particles, was prepared for the preconcentration of bovine hemoglobin in dilute solution. Under neutral conditions, the adsorption efficiency exceeded 98% within 20?min. After the preconcentration of bovine hemoglobin on the adsorbent, the near-infrared spectrum was measured in diffuse reflectance mode and a partial least squares model was constructed for quantitative prediction. Samples were analyzed in the presence of amino acid, albumin bovine V, D-glucose, and metal ions as potential interferences. The results show that bovine hemoglobin was selectively determined. The correlation coefficient between the predicted concentrations and the reference values was 0.9911, and the recoveries were from 86.4 to 111.2% for validation samples with concentrations between 2.1 and 30.0?mg?L^?1. Therefore, the determination of bovine hemoglobin was achieved by near-infrared diffuse reflectance spectroscopy combined with preconcentration and chemometric modeling.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Tensile deformation of polyamide (PA) 6 probed by rheo-optical near-infrared (NIR) spectroscopy

A rheo-optical near-infrared (NIR) spectroscopy, based on the combination of NIR spectroscopy and mechanical analysis, was applied to polyamide (PA) 6 samples consisting of bundled amorphous chains. Sets of strain-dependent NIR spectra as well as tensile stress of dried and wet treated PA 6 samples were collected during the mechanical elongation of the samples. The spectra were then subjected to two-dimensional (2D) correlation analysis to elucidate fine features of the spectral changes. An asynchronous correlation peak develops between the bands at 2355 and 2300 nm due to the combination modes of CH₂ groups arising from the rubbery amorphous chain and rigid crystalline lamella of the dried PA 6, respectively. It therefore indicates that during the tensile deformation, the orientation of the amorphous chain is induced first to cause the elastic deformation. Further elongation results in the rotation of the crystalline lamella connected with the amorphous chain. This correlation intensity apparently increases by the wet treatment, suggesting that water molecule in the PA 6 disrupts the H-bonding interaction between the adjacent polymer chains and thus makes the polymer more flexible. Accordingly, it is likely the H-bonding between the polymer chains works in a manner somewhat similar to cross-linked polymers, which substantially effects on the mechanical property of the PA 6.     

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.     

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.