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.     

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.     

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

Abstract

American Petroleum Institute (API) gravity is an important parameter in the crude oil industry and the nitrogen compounds are related to the toxic effects of the oil in refineries and the environment. In this paper, 194 crude oil samples with API gravities ranging from 11.4 to 57.5 were used for the purpose of estimating the physicochemical properties: API gravity, total nitrogen content (TNC) and basic nitrogen content (BNC). Initially, infrared spectra in the mid and near regions (MIR and NIR) were collected, then full-spectral partial least squares (PLS) and the orthogonal projections to latent structures (OPLS) chemometric models were developed and validated, as well as models using interval PLS (iPLS), synergy interval PLS (siPLS) and competitive adaptive reweighted sampling PLS (CARSPLS) as variable selection tools. For API gravity and TNC, the best calibration technique is the NIR CARSPLS with a root mean square error of prediction (RMSEP) values of 0.9 and 0.0275?wt%, respectively. For BNC, the best technique is MIR siPLS with a prediction error of 0.0134?wt%. The results were validated based on the evaluation of the figures of merit, a statistical evaluation of the accuracy, characterization of the systematic error and measurement for errors in the residues. The results were satisfactory considering the high variability of the data and the diversity of the samples, demonstrating suitable applicability for practical analysis.     

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.     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

药物水溶解度的QSAR研究和VolSurf参数

药物的水溶解度与其吸收密切相关。本文利用一种新的计算方法,VolSurf,预测药物的水溶解度并测定有利于药物水溶解度的主要分子特征。被测化合物包括26个结构不同的药物,通过偏最小二乘分析法,对药物水溶解度实验值与分子特征进行相关,得到较好的模型(r2=0.90,q2=0.77)。将化合物分为训练集和预测集进行相关分析,结果表明以18个化合物所建立的训练集模型对其余8个化合物有较好的预测能力,预测的标准偏差(SDEP)为0.59。参数分析表明分子与水相互作用的3个局部能量最小值越小,且它们之间的距离越大,对其水溶解度越有利;亲水性占主导因素的分子有高的水溶解度;分子的疏水性越强,在水中的溶解性越弱;大分子的溶解度较小分子溶解度低。     

QSAR Study and VolSurf Characterization of Human Intestinal Absorption of Druge

The prediction of human intestinal absorption is a major goal in the design,optimization,and selection of candidates for the develoment of oral drugs.In this study,a computerized method(VolSurf with GRID) was used as a novel tool for predicting human intestinal absorption of test compound,and for determining the critical molecular properties needed for human intestinal absorption.The tested molecules consisted of 20 diverse drug-like compounds.Partial least squares(PLS) discriminant analysis was used to correlate the experimental data with the theoretical molecular properties of human intestinal absorption.A good correlation(r^2=0.95,q^2=0.86) between the molecular modeling results and the experimental data demonstrated that human intestinal absorption could be predicted from the three-dimensional(3D) molecular structure of a compound .Favorable structureal properties identified for the potent intestinal absorption of drugs included strong imbalance between the center of mass of a molecule and the barycentre of its hydrophilic and hydrophobic regions and a definitive hydrophobic region as well as less hydrogen bonding donors and acceptors in the molecule.     

烟草灰分、总挥发酸和总挥发碱的近红外光谱分析

应用偏最小二乘法(PLS)结合近红外光谱(NIR)对烟草灰分(ash)、总挥发酸(TVA)和总挥发碱(TVB)建立校正模型。烟草灰分、总挥发酸和总挥发碱模型相关系数分别为0.97312、0.96220和0.98050;均方预测残差(RMSECV)分别为0.41227、0.00688和0.09790;预测范围分别为1.74~31.31、0.0570~0.2336和0.042~1.136;通过对模型进行t-检验,在显著性水平大于0.05的条件下,其预测结果与行业标准方法的测定结果对比,结果令人满意。     

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.     

Liver functional magnetic resonance imaging analysis using a latent variables approach

The liver is a highly vascular organ with a dual blood supply, and it performs a remarkable number of vital functions. Here, we show, through measurement of blood oxygen level‐dependent (BOLD) signal, that liver arterial and hepatic portal blood supplies can be modulated through hyperoxia exposure and by consumption of a standardized meal, respectively. As such, we suggest that hyperoxia modulates the hepatic arterial BOLD signal, whereas a controlled meal changes predominantly the hepatic portal BOLD signal. The hemodynamics of the dual liver blood supplies in response to the aforementioned challenges are complex and variable across subjects, making a general linear model‐based analysis difficult. Therefore, we present the application of two local (at each voxel) hemodynamic response‐independent techniques—principal component analysis and partial least squares—to observe the hypothesized reduction in BOLD contrast during cycles of hyperoxic breathing, when comparing preprandial versus postprandial states in a normally functioning liver. We illustrate the ability of our techniques to differentiate between healthy and diseased livers with an analysis of 17 subjects—11 with normal livers and 6 with liver disease (hepatitis or cirrhosis). Our local analysis can correctly classify all of the subjects. Copyright © 2012 John Wiley & Sons, Ltd.     

Discrimination of Scrophulariae Radix according to geographical origin and determination of active constituents by near infrared spectroscopy (NIRS)

The potential of near infrared reflectance spectroscopy (NIR) was investigated for its ability to non-destructively discriminate the geographic origins of Scrophularia spp., Andong, Uisung and China. Application of principal component analysis to NIR spectra leads to a clear separation of Andong sample from the others. Moreover, the contents of two neuroprotective constituents of Scrophularia spp., 8-O-(E-p-methoxycinnamoyl)-harpagide (HG), and E-p-methoxycinnamic acid (MCA), were determined by HPLC-DAD. Partial least squares (PLS) regression of NIR spectra combined with these analytical reference data yield the development of calibration models for the contents of the two constituents. The correlation coefficients of prediction models for HG and MCA were > 0.87. These outcomes indicated that the NIRS could be useful for the discrimination of Scrophularia spp.     

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.     

Rapid Classification and Recognition Method of the Species and Chemotypes of Essential Oils by ATR-FTIR Spectroscopy Coupled with Chemometrics

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.     

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).     

Determination of Nitrogen in Rapeseed by Fourier Transform Infrared Photoacoustic Spectroscopy and Independent Component Analysis

Fourier-transform mid-infrared photoacoustic spectroscopy was utilized for rapid and nondestructive determination of nitrogen in rapeseeds. Rapeseed spectra were characterized by independent component analysis for quantitative calibration. A calibration model was built by using independent components as the input for partial least squares. Compared to full-spectrum partial least squares, the combined model achieved higher prediction accuracy with a residual predictive deviation of 2.06. Moreover, a genetic algorithm coupled with partial least squares was adopted to optimize the independent components for partial least square modeling and provide a further refined model with the highest residual predictive deviation of 2.12. A t-test verified a high congruence between results obtained by calibration models and the reference Kjeldahl method. This study demonstrated the promise of Fourier-transform mid-infrared photoacoustic spectroscopy for the determination of nitrogen in rapeseeds and the applicability of independent components for multivariate calibration.     

Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: case study for the analysis of an etching solution containing hydrogen peroxide

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH₄OH, H₂O₂ and H₂O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm⁻¹) were used to build PLS models. The selective determination of H₂O₂ without influences from NH₄OH and H₂O was a key issue since its molecular structure is similar to that of H₂O and NH₄OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH₄OH and H₂O₂ were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH₄OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H₂O₂ was broadly overlapping and much less distinct than that from NH₄OH, the selectivity and prediction performance for the H₂O₂ calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H₂O₂ calibration. A robust H₂O₂ calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.     

Elucidation of Analytical–Compositional Fingerprinting of Three Different Species of Chili Pepper by Using Headspace Solid-Phase Microextraction Coupled with Gas Chromatography–Mass Spectrometry Analysis,and Sensory Profile Evaluation

The aim of the present study was to determine the volatile compounds of three different species of chili peppers, using solid-phase microextraction (SPME) methods in combination with gas chromatography–mass spectrometry (GC-MS). The detection of marker aroma compounds could be used as a parameter to differentiate between species of chili peppers for their detection and traceability in chili pepper food. The sensorial contribution was also investigated to identify the predominant notes in each species and to evaluate how they can influence the overall aroma. Three different pepper species belonging to the Capsicum genus were analyzed: Chinense, Annuum, and Baccatum. A total of 269 volatile compounds were identified in these species of chili peppers. The Capsicum annum species were characterized by a high number of acids and ketones, while the Capsicum chinense and Capsicum baccatum were characterized by esters and aldehydes, respectively. The volatile profile of extra virgin olive oils (EVOOs) flavored with chili peppers was also investigated, and principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the volatile profiles were demonstrated to be a powerful analytical strategy for building a model that highlights the potential of a volatile characterization approach for use in evaluating food traceability and authenticity.     

Analysis of Volatile Compounds in Sea Bass (Lateolabrax japonicus) Resulting from Different Slaughter Methods Using Electronic-Nose (E-Nose) and Gas Chromatography-Ion Mobility Spectrometry

Sea bass (Lateolabrax japonicus) is known for its unique flavor and high nutritional value. In this study, the influence of slaughter methods on the volatile compounds (VOCs) in sea bass was investigated using electronic nose (E-nose) technology and gas chromatography-ion mobility spectrometry (GC-IMS). VOCs in raw and cooked sea bass resulting from different slaughter methods were effectively distinguished using both techniques. Aldehydes, ketones, and alcohols were associated with the basic flavor of sea bass, whereas esters, organic acids, and furans enriched the aroma. In raw sea bass, the fishy odor was the strongest in the HSD group (head shot control death), followed by that in the IFD (ice faint to death) and BDS (bloodletting to death) groups. The VOC content increased and stabilized after steaming, enhancing pleasant odors such as fatty and fruity aromas. In cooked sea bass, the content of diacetyl and ethanol was the highest in the EAD group (eugenol anesthesia to death), which may be a residue of eugenol, imparting a distinct irritating chemical odor. Furthermore, abundant (E)-2-octenal, 2-heptanone, benzaldehyde, and esters in the BDS group imparted a strong, pleasant aroma. The findings indicate that heart puncture and bloodletting is the preferred slaughter method to maintain sea bass quality, providing new insights into the volatile changes in sea bass induced by different slaughter methods.     

Genetic Algorithm Based Potential Selection in Simultaneous Voltammetric Determination of Isoniazid and Hydrazine by Using Partial Least Squares (PLS) and Artificial Neural Networks (ANNs)

The voltammetric behavior of isoniazid and hydrazine at an overoxidized polypyrrole modified glassy carbon electrode has been investigated. The obtained cyclic voltammograms showed that their oxidation peaks were overlapped and it is difficult to determine them individually from a mixture without separation. To overcome this limitation, a procedure was proposed for resolution of overlapped voltammetric signals from mixtures of isoniazid and hydrazine. In this procedure, genetic algorithm was used for the selection of potentials for partial least squares. A feed forward artificial neural network with back propagation error algorithm was used to process the nonlinear relationship between currents and concentrations of hydrazine and isoniazid. The proposed method was suitable for determination of isoniazid in pharmaceutical tablets and detection of hydrazine impurities in the same samples.     

Evaluation of doped and undoped poly (o‐anisidine) as sensing materials for a sensor array for volatile organic compounds

Poly (o‐anisidine) (PoANI) and PoANI doped with nickel oxide and zinc oxide were evaluated as sensing materials for four gas analytes (methanol, ethanol, acetone, and benzene). The sensing materials had high sensitivity (showing an affinity towards the target analytes even at low concentrations, in the range of 1‐5 ppm), but rather poor selectivity, especially when the gas analytes were in a mixture. To exploit the poor selectivity, the three sensing materials were combined into a sensor array using principal component analysis (PCA) as a sensing algorithm. It was found that using a sensor array, the four individual gases could be separated. However, when all four gases were present (in analyte mixtures), there was too much overlap in the responses to distinguish between individual gas analytes and their related mixtures.