A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters

A study of the statistic characteristics of the multidetermination of several enological parameters - namely, alcoholic degree, volumic mass, total acidity, glycerol, total polyphenol index, lactic acid and total sulphur dioxide - depending on the spectroscopic zone employed, was carried out. The two techniques used were near infrared spectroscopy (NIRS) and Fourier transform mid infrared spectroscopy (FT-MIRS). The combination of these two regions (sum of their spectra) was also studied. NIRS yielded better results, but the use of both zones improved the determination of glycerol and total sulphur dioxide. The training and validation sets used for developing general equations were built with samples from different apellation d’origine, different wine types, etc. Partial least squares regression was used for multivariate calibration, using systematic cross validation in the calibration stage and external validation in the testing stage. Sample preparation was not required.     

Use of indirect multivariate calibration for quality control of agricultural products by near infrared spectroscopy

In the near infrared (NIR) region, (=800–2500 nm) of the spectrum, most organic molecules have weak but reproducibly measurable absorption bands. This phenomenon has been increasingly exploited for the rapid, quantitative analysis of major constituents of agricultural products. As there is strong spectral overlap and an interaction of constituents in NIR, the calibration of instrumentation has to be performed indirectly with a training set of samples applying multivariate methods. A software package for IBM compatible personal computers includes programs to select samples to be used for calibration, to compute multiple linear regression equations, to cross-validate regression equations and to detect unusual samples. These methods are applied to rapidly determine the oil content of over a thousand rapeseed samples of the harvest of 1986. A considerable saving of time, labour, and costs can be achieved, the agreement with the standard laboratory method is very satisfactory.     

Standardization of near infrared spectra measured on multi-instrument

Calibration model transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. An approach for calibration transfer based on alternating trilinear decomposition (ATLD) algorithm is proposed in this work. From the three-way spectral matrix measured on different instruments, the relative intensity of concentration, spectrum and instrument is obtained using trilinear decomposition. Because the relative intensity of instrument is a reflection of the spectral difference between instruments, the spectra measured on different instruments can be standardized by a correction of the coefficients in the relative intensity. Two NIR datasets of corn and tobacco leaf samples measured with three instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra measured on one instrument can be correctly predicted using the partial least squares (PLS) models built with the spectra measured on the other instruments.     

Rapid determination of Paeoniae Radix using near infrared spectroscopy

A rapid and nondestructive near infrared spectroscopy (NIRS) was used to differentiate different geographical Paeoniae Radix and quantitatively predict the content of main active components. Paeoniflorin, albiflorin and benzoylalbiflorin were analyzed simultaneously with an Agilent Zorbax SB-C₁₈ column by gradient elution under high-performance liquid chromatography-UV detection (HPLC-UV). Multiplicative scatter correction (MSC), first derivative and Savitsky-Golay were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra in order to give a better correlation with the results obtained by HPLC-UV. Multiplicative regression methods were discussed. The spectra calibration equations produced highest correlation coefficient values (R²) and lowest root mean square error of prediction (RMSEP) were used for the determination of paeoniflorin, albiflorin and benzoylalbiflorin. The RMSEP of paeoniflorin, albiflorin and benzoylabiflorin were 0.866 mg/g, 0.369 mg/g and 0.084 mg/g, respectively, and the R² of cross validation were 0.986, 0.939 and 0.971, respectively. Furthermore with the use of principle component analysis (PCA), Paeoniae Radix was clustered according to different cultivation area. The results indicated that the NIRS method could be used for the quality control of Chinese herbal medicine.     

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

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

Control production of polyester resins by NIR spectroscopy

The control of the esterification reaction for production of polyester saturated resins is followed usually by determination of the acid value (AV) and hydroxyl value (OHV).These parameters are determined by titrimetry, but these methods are slow, intensity working and produce waste. In this paper an alternative methodology is proposed, based in the construction of multivariate models on NIR spectroscopic data and different models are constructed in order to apply to different steps of the production process. The ensuing methodology provides models of good predictive ability and constitute an advantageous alternative to existing titrimetric reference methods as regards expeditiousness and environmentally compatible. The multivariate calibration models established were also used with a different instrument; to this end, the spectra recorded with the original equipment were subjected to Piecewise Direct Standardization (PDS) in order to make them equivalent to those provided by the new equipment. Also, PLS calibration was reproduced by using the same samples, spectral treatment, wavenumber range and number of factors as in the original model, and the AV and OHV results thus obtained were similarly good.     

复方氯丙那林质量检控的近红外光谱快速分析

基于近红外光纤漫反射技术,利用偏最小二乘法分别建立了复方氯丙那林胶囊的三种药效成分盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的快速同时测定方法。所建立的盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的定量分析多元校正模型的相关系数分别为99.7%、99.4%和99.0%,校正集的均方根残差分别为0.028、0.145和0.250,预测均方根误差分别为0.055、0.120和0.210。由于该方法是在不经任何预处理的情况下的光纤快速同时分析,因此可用于复方氯丙那林的过程质量控制。     

Coffee varietal differentiation based on near infrared spectroscopy

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.     

Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

Near infrared (NIR) spectroscopy was used to simultaneously predict the concentrations of malvidin-3-glucoside (M3G), pigmented polymers (PP) and tannins (T) in red wine. A total of 495 samples from 32 commercial scale red wine fermentations over two vintages using two grape varieties (Cabernet Sauvignon and Shiraz), and also including as additional variables two types of fermenters, two different yeasts, and three fermentation temperatures were used. Samples were scanned in transmission mode (400-2500 nm) using a monochromator instrument (NIRSystems6500). Calibration equations were developed from high performance liquid chromatography (HPLC) and NIR data using partial least squares (PLS) regression with internal cross validation. Using PLS regression, very good calibration statistics (R_cal²>0.80) were obtained for the prediction of M3G, PP and T with standard deviation (S.D.)/standard error in cross validation (SECV) ratio (residual predictive deviation, RPD)) ranging from 1.8 to 5.8. It was concluded that near infrared spectroscopy could be used as rapid alternative method for the prediction of the concentration of phenolic compounds in red wine fermentations.     

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

In the present work, we explored the possibility of using near-infrared spectroscopy in order to quantify the degree of adulteration of durum wheat flour with common bread wheat flour. The multivariate calibration techniques adopted to this aim were PLS and a wavelet-based calibration algorithm, recently developed by some of us, called WILMA. Both techniques provided satisfactory results, the percentage of adulterant present in the samples being quantified with an uncertainty lower than that associated to the Italian official method. In particular the WILMA algorithm, by performing feature selection, allowed the signal pretreatment to be avoided and obtaining more parsimonious models.     

Analytical control of an esterification batch reaction between glycerine and fatty acids by near-infrared spectroscopy

Near-infrared spectroscopy was used to control an esterification reaction between glycerine and middle- or long-chain fatty acids performed in a laboratory-scale reactor. The process involves the initial formation of monoglycerides, which is followed by that of di- and triglycerides as well as transesterification. Establishing the end point of the process is critical with a view to ensuring that the end product will have the composition required for its intended use. PLS calibration was applied to industrial and laboratory-scale batch samples, and laboratory samples were additionally used to extend calibration ranges and avoid correlation between the concentration of the batch samples. In this way, PLS calibration models for glycerine, fatty acids, water, and mono-, di- and triglycerides, were constructed. The proposed method allows the reaction to be monitored in real time, thereby avoiding long analysis times, excessive reagent consumption and the obtainment of out-of-specification products.     

Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)

This paper indicates the possibility to use near infrared spectroscopy (NIR) combined with PLS as a rapid method to estimate the quality of green tea. NIR is used to build calibration models to predict the content of caffeine, epigallocatechin gallate (EGCG) and epicatechin (EC) and for the prediction of the total antioxidant capacity of green tea. For the determination of the total antioxidant capacity, the trolox equivalent antioxidant capacity (TEAC) method is used. Until now, the prediction of the antioxidant capacity as such by use of NIR has not been reported. For caffeine and TEAC, models are build for the whole green tea leaves and also for the ground leaves. For the polyphenols (EGCG and EC), only models for the whole leaves are investigated. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV) for the training set is chosen. The correlation coefficient (r) between the predicted and the reference results for the test set is used as an evaluation parameter for the models: for the TEAC results r=0.90 for the model with the whole leaves, r=0.86 for the model with the powdered leaves are obtained. The caffeine prediction model has a correlation coefficient r=0.96 for the whole leaves and r=0.93 for the ground leaves. The correlation coefficient for the EGCG and the EC content models are, respectively 0.83 and 0.44.     

Identification of the rosasite group minerals--an application of near infrared spectroscopy

The ability of near infrared reflectance spectroscopy to classify the rosasite group minerals from spectral characteristics is demonstrated. NIR spectroscopy can be regarded as an alternative tool for structure analysis. The spectra show that rosasite group minerals with different cations can be distinguished. Ni2+ in nullaginite [Ni2(CO3)(OH)2] is conspicuous through a single broad band absorption feature at 8525 cm-1, extended from 11,000 to 7000 cm-1. The effect of Ni on Cu is seen in the spectrum of glaukosphaerite [(Cu, Ni)2(CO3)(OH)2] both by a red shift of the spectrum and reduction in intensity of bands with variable positions of band maxima for Cu2+ at 6995 cm-1 and Ni2+ at 7865 cm-1. The spectrum of rosasite [(Cu, Zn)2(CO)3(OH)2] is characterised by Cu2+ band at 7535 cm-1. Kolwezite [(Cu, Co)2(CO)3(OH)2] is a spectral mixture of Cu and Co but optically separated by Co2+ and Cu2+ peaks at 8385 and 7520 cm-1. Vibrational spectra of carbonates show a number of bands in the 7000-4000 cm-1 region attributable to overtones, combination of OH stretching and deformation modes. They appear to be uniform in nature since the structure of rosasite group minerals is identical. The complexity of these features varies between samples because of the variation in composition and hence is useful for discriminating different hydrous carbonates.     

Comparing near infrared and Raman spectroscopy for on-line monitoring of emulsion copolymerization reactions

In this work, Raman and Near InfraRed (NIR) spectroscopies are evaluated for the monitoring of different semicontinuous emulsion homo- and co-polymerization reactions. Important process variables, namely monomer concentrations and average particle sizes, were monitored by both techniques under realistic conditions that would be found in an industrial environment (e.g. low signal/noise ratio, probe placed in the reaction medium). Results suggest that Raman and NIR are suitable for on-line monitoring of emulsion polymerization reactions and that the success of their application is mainly related to representative calibration models used for the estimation of the properties of interest.     

Speciation of adsorbates on surface of solids by infrared spectroscopy and chemometrics

Speciation, i.e. identification and quantification, of surface species on heterogeneous surfaces by infrared spectroscopy is important in many fields but remains a challenging task when facing strongly overlapped spectra of multiple adspecies. Here, we propose a new methodology, combining state of the art instrumental developments for quantitative infrared spectroscopy of adspecies and chemometrics tools, mainly a novel data processing algorithm, called SORB-MCR (SOft modeling by Recursive Based-Multivariate Curve Resolution) and multivariate calibration. After formal transposition of the general linear mixture model to adsorption spectral data, the main issues, i.e. validity of Beer–Lambert law and rank deficiency problems, are theoretically discussed. Then, the methodology is exposed through application to two case studies, each of them characterized by a specific type of rank deficiency: (i) speciation of physisorbed water species over a hydrated silica surface, and (ii) speciation (chemisorption and physisorption) of a silane probe molecule over a dehydrated silica surface. In both cases, we demonstrate the relevance of this approach which leads to a thorough surface speciation based on comprehensive and fully interpretable multivariate quantitative models. Limitations and drawbacks of the methodology are also underlined.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.