Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy

The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.     

An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models

The particle size distribution of a solid product can be crucial parameter considering its application to different kinds of processes. The influence of particle size on near infrared (NIR) spectra has been used to develop effective alternative methods to traditional ones in order to determine this parameter. In this work, we used the chemometrical techniques partial least squares 2 (PLS2) and artificial neural networks (ANNs) to simultaneously predict several variables to the rapid construction of particle size distribution curves. The PLS2 algorithm relies on linear relations between variables, while the ANN technique can model non-linear systems.Samples were passed through sieves of different sieve opening in order to separate several size fractions that were used to construct two types of particle size distribution curves. The samples were recorded by NIR and their spectra were used with PLS2 and ANN to develop two calibration models for each. The correlation coefficients and relative standard errors of prediction (RSEP) have been used to assess the goodness of fit and accuracy of the results.The four calibration models studied provided statistically identical results based on RSEP values. Therefore, the combined use of NIR spectroscopy and PLS2 or ANN calibration models allows determining the particle size distributions accurately. The results obtained by ANN or PLS2 are statistically similar.     

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.     

Determination of ethanol in fuel ethanol and beverages by Fourier transform (FT)-near infrared and FT-Raman spectrometries

Fourier transform-near infrared (FT-NIR) and FT-Raman spectrometries have been used to design partial least squares (PLS) calibration models for the determination of the ethanol content of ethanol fuel and alcoholic beverages. In the FT-NIR measurements the spectra were obtained using air as reference, and the spectral region for PLS modeling were selected based on the spectral distribution of the relative standard deviation in concentration. In the FT-Raman measurements hexachloro-1,3-butadiene (HCBD) has been used as an external standard. In the PLS/FT-NIR modeling for ethanol fuel analysis 50 ethanol fuel standards (84.9-100% (w/w)) were used (25 in the calibration, 25 in the validation). In the PLS/FT-Raman modeling 25 standards were used (13 in the calibration, 12 in the validation). The PLS/FT-NIR and FT-Raman models for beverage analysis made use of 24 standards (0-100% (v/v)). Twelve of them contained sugars (1-5% (w/w)), one-half was used in the calibration and the other half in the validation. Different spectral pre-processing were used in the PLS modeling, depending on the type of sample investigated. In the ethanol fuel analysis the FT-NIR pre-processing was a 17 points smoothed first derivative and for beverages no spectral pre-processing was used. The FT-Raman spectra were pre-processed by vector normalization in the ethanol fuel analysis and by a second derivative (17 points smoothing) in the beverage analysis. The PLS models were used in the analysis of real ethanol fuel and beverage samples. A t-test has shown that the FT-NIR model has an accuracy equivalent to that of the reference method (ASTM D4052) in the analysis of ethanol fuel, while in the analysis of beverages, the FT-Raman model presents an accuracy equivalent to the reference method. The limits of detection for NIR and Raman calibration models were 0.05 and 0.2% (w/w), respectively. It has also been shown that both techniques, present better results than gas chromatography (GC) in evaluating the ethanol content of beverages.     

Determination of quality parameters of beers by the use of attenuated total reflectance-Fourier transform infrared spectroscopy

The estimation of important quality parameters of beers, such as original and real extracts and alcohol content, has been evaluated by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) using a partial least square (PLS) calibration approach. Two sample populations, one consisting of 24 samples and other of 21 samples, obtained from the Spanish market and covering different types of beer were used. The first set was used for building and validating the model, whereas the second, measured 6 months after, was used for evaluating its robustness. The spectral range and the size of the calibration set and its suitability for building the PLS model have been evaluated.Considering a calibration set comprised of 12 samples, selected via hierarchical cluster analysis, and a validation data set of 11 samples, the absolute mean difference (d_x−y) and standard deviation of mean differences (s_x−y) of the real extract, original extract and alcohol content were 0.009 and 0.069% (w/w), −0.021 and 0.20% (w/w) and −0.003 and 0.130% (v/v), respectively. The maximum error for the prediction of any of these three parameters for a new sample did not exceed 2.5%. These values were practically invariant for both tested data sets.The developed methodology favourably compares with the automatic reference methodology in terms of speed and reagent consumption and waste generation.     

傅里叶变换红外光声光谱法测定土壤中有效磷

以中国科学院封丘生态实验站长期定位实验区的土样为材料(68样),利用傅里叶转换红外光声光谱测定土壤有效磷:以Olsen-P为因变量,通过傅里转换红外光声光谱构建偏最小二乘法和人工神经网络模型,利用模型进行预测。结果表明,偏最小二乘法模型的相关系数(R2)为0.96,校正标准偏差为1.79mg/kg,验证标准偏差为5.25mg/kg;人工神经网络模型的校正系数为0.84,校正标准偏差为2.40mg/kg,验证标准偏差为5.43mg/kg。两种模型均可以用于土壤有效磷的预测,且偏最小二乘模型优于人工神经网络模型。该方法的特点是无需样品前处理,且测定对样品无破坏,为土壤有效磷的快速测定提供新的手段。     

Evaluation and comparison of mid-infrared,Raman and near-infrared spectroscopies for characterization and determination of the compositions in fully biodegradable poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) blends combined with chemometrics

In this work, mid-infrared (MIR), Raman and near-infrared (NIR) spectroscopies were evaluated and compared for characterization and determination of the compositions in poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) (PLA/PPC/PBAT) blends via chemometrics. Qualitative analysis of MIR, Raman, and NIR spectra of the three compositions was performed. Partial least squares (PLS) models were developed based on each spectroscopy for quantitative determination of the concentrations. The data suggested that MIR and Raman have an advantage over NIR in terms of qualitative recognition of the three compositions. The data also showed that Raman and NIR succeeded in determining the concentrations, while the concentration determined via MIR was inaccurate. Hence, Raman is the optimal analytical tool for qualitative characterization and quantitative determination of the compositions in fully biodegradable PLA/PPC/PBAT blends. The characteristic bands in the Raman spectra clearly identify PLA, PPC, and PBAT to be 392 cm^?1 (δ CCO), 948 cm^?1 (ν C?O?C) and 1600 cm^?1 (ν C ? C in benzene ring), respectively. The optimal calibration models based on Raman for PLA, PPC, and PBAT exhibited root mean square error of prediction (RMSEP) values of 3.140%, 3.576%, and 2.538%, respectively.     

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.     

Improving the sensitivity of NIR spectroscopy with an enrichment technique: determining a trace analyte of ethyl carbamate.

An exploration was made to develop a determination method of a low-concentration analyte by NIR spectroscopy. An absorber, silica gel was employed to extract and enrich a low-concentration analyte of ethyl carbamate. The solid absorber with the enriched analyte was measured by NIR spectroscopy in the range of 800 - 2500 nm. Afterwards, PLS regression was performed between the NIR spectra and the concentrations of the analyte for quantitative analysis of the low-concentration analyte. The spectra of 20 solid samples of analyte-absorbed silica gel showed a good correlation with the concentrations of ethyl carbamate in the samples. A leave-one-out cross validation was applied to evaluate the prediction ability of PLS models built with the full spectra, spectra in the region of 1920 - 1970 nm and the region of 2250 - 2430 nm, respectively. The values of the root-mean-square error of the cross validation (RMSECV) were about 0.1 mg L(-1) (0.1 ppm).     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Screening of humic and fulvic acids in estuarine sediments by near-infrared spectrometry

Diffuse reflectance near-infrared spectroscopy (NIR) combined with partial least squares (PLS) data treatment has been employed for the rapid and nondestructive determination of sedimentary humic substances. Forty one samples of surface estuarine sediments, taken during distinct seasonal periods from different locations across Ria de Arousa (northwest of Spain), were scanned at wavelengths from 833 to 2,976 nm (12,000 to 3,360 cm⁻¹). Twenty four samples were randomly selected, from previous hierarchical cluster analysis of their NIR spectra, for the calibration set, and the 17 remaining samples were assigned to the validation set. NIR spectra of calibration samples were correlated to measured values of humic acids (HAs) and fulvic acids (FAs), which ranged from 1.53 to 28.17 mg/g and from 0.37 to 2.45 mg/g, respectively, using PLS regression and multiplicative scattering correction on the raw and first-derivative NIR spectra, respectively. Low root mean square error of prediction values of 4.3 mg HA/g sediment and 0.25 mg FA/g sediment were obtained. Good residual prediction deviation values of 1.16 and 1.2 were obtained for HA and FA, respectively, allowing the PLS models built to be considered as appropriate tools for screening purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

独立分量分析预处理法提高苹果糖度模型预测精度研究

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

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.     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.     

Development of a PLS based method for determination of the quality of beers by use of NIR: spectral ranges and sample-introduction considerations

Near infrared spectroscopy (NIR) has been used to determine important indicators of the quality of beers, for example original and real extract and alcohol content, using a partial least squares (PLS) calibration approach. A population of 43 samples, obtained commercially in Spain and including different types of beer, was used. Cluster hierarchical analysis was used to select calibration and validation data sets. Absorbance sample spectra, in transmission mode, were obtained in triplicate by using a 1-mm pathlength quartz flow cell and glass chromatography vials of 6.5 mm internal diameter. The two methods of sample introduction were compared critically, on the basis of spectral reproducibility for triplicate measurements and after careful selection of the best spectral pre-processing and the spectral range for building the PLS model, to obtain the best predictive capability. For each mode of sample introduction two calibration sets were assayed, one based on the use of 15 samples and a second extended based on use of 30 samples, thus leaving 28 and 13 samples, respectively, for validation. The best results were obtained for 1 mm flow cell measurements. For this method original zero-order spectra data in the ranges 2220–2221 and 2250–2350 nm were chosen. For the real extract, original extract, and alcohol d_x-y and s_x-y values of –0.04 and 0.07% w/w, –0.01 and 0.13% w/w, and –0.01 and 0.1% v/v, respectively, were obtained. The maximum errors in the prediction of any of these three indicators for a new sample were 2.2, 1.2, and 1.9%, respectively. This method compares favorably with the automatic reference method in terms of speed, reagent consumed, and waste generated.     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

Determination of total nitrogen content, pH, density, refractive index, and brix in Thai fish sauces and their classification by near-infrared spectroscopy with searching combination moving window partial least squares

Near-infrared (NIR) transflectance spectra in the region of 1100-2500 nm were measured for 100 Thai fish sauces. Quantitative analyses of total nitrogen (TN) content, pH, refractive index, density and brix in the Thai fish sauces and their qualitative analyses were carried out by multivariate analyses with the aid of wavelength interval selection method named searching combination moving window partial least squares (SCMWPLS). The optimized informative region for TN selected by SCMWPLS was the region of 2264-2428 nm. A PLS calibration model, which used this region, yielded the lowest root mean square error of prediction (RMSEP) of 0.100% w/v for the PLS factor of 5. This prediction result is significantly better than those obtained by using the whole spectral region or informative regions selected by moving window partial least squares regression (MWPLSR). As for pH, density, refractive index and brix, the 1698-1722, and 2222-2258 nm regions, the 1358-1438 nm region, the 1774-1846, and 2078-2114 nm regions, and the 1322-1442, and 2000-2076 nm regions were selected by SCMWPLS as the optimized regions. The best prediction results were always obtained by use of the optimized regions selected by SCMWPLS. The lowest RMSEP for pH, density, refractive index and brix were 0.170, 0.007 g cm(-3), 0.0079 and 0.435 degrees Brix, respectively. Qualitative models were developed by using four supervised pattern recognitions, linear discriminant analysis (LDA), factor analysis-linear discriminant analysis (FA-LDA), soft independent modeling of class analog (SIMCA), and K neareat neighbors (KNN) for the optimized combination of informative regions of the NIR spectra of fish sauces to classify fish sauces into three groups based on TN. All the developed models can potentially classify the fish sauces with the correct classification rate of more than 82%, and the KNN classified model has the highest correct classification rate (95%). The present study has demonstrated that NIR spectroscopy combined with SCMWPLS is powerful for both the quantitative and qualitative analyses of Thai fish sauces.