Complete quality analysis of commercial surface-active products by Fourier-transform near infrared spectroscopy

Using proper calibration data Fourier-transform near infrared spectroscopy is used for developing multivariate calibrations for different analytical determinations routinely used in the surfactants industry. Four products were studied: oleyl-cetyl alcohol polyethoxylated, cocamidopropyl betaine (CAPB), sodium lauryl sulfate (SLS) and nonylphenol polyethoxylated (NPEO). Calibrations for major as well as very low concentrated compounds were achieved and every model was validated through linearity, bias, accuracy and precision tests, showing good results and the viability of NIR spectroscopy as a full quality control method for this products. Duplicate and complete analysis on a single sample takes at most 3 min, requiring neither sample preparation nor the use of reagents. The analytical reference procedures involved in this work represent the typical ones used in the industry and the NIR method shows good results in the analysis of components with weight concentrations less than 1%.     

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.     

Quantitative analysis of topical gels and ointments by FT-Raman spectroscopy

A method for quantitative determination of ibuprofen (IBU), naproxen (NAP), methyl salicylate (MES) and menthol (MNT) in commercial topical gels and ointments using partial least squares (PLS) models based on FT-Raman spectra is described. The calculated relative standard errors of prediction (RSEP) were found to be in the range of 2.1–3.2% for the calibration and validation data sets. Two commercial topical gels containing 5.0% of IBU and 10% of NAP (w/w), as well as one ointment containing 15% of MES and 10% of MNT (w/w) as active pharmaceutical ingredients (APIs), were successfully quantified using the developed models with recoveries in the 99.2–101.5% range. The proposed procedure can be used as a fast, reliable and economic method for the quantification of APIs in topical gels and ointments.     

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.     

Chemometrics and near-infrared spectroscopy: Avoiding the pitfalls

The use of chemometrics in quantitative near-infrared (NIR) spectroscopy is reviewed from the standpoint of avoiding pitfalls that may lead to misleading or overly optimistic results. Using the NIR analysis of glucose in six-component mixture samples as an example, a set of guidelines is presented to help the analyst develop and implement a successful calibration.     

Quantification of albumin in urine using preconcentration and near-infrared diffuse reflectance spectroscopy

Urinary albumin is an important diagnostic and prognostic marker for cardiorenal disease. Recent studies have shown that elevation of albumin excretion even in normal concentration range is associated with increased cardiorenal risk. Therefore, accurate measurement of urinary albumin in normal concentration range is necessary for clinical diagnosis. In this work, thiourea-functionalized silica nanoparticles are prepared and used for preconcentration of albumin in urine. The adsorbent with the analyte was then used for near-infrared diffuse reflectance spectroscopy measurement directly and partial least squares model was established for quantitative prediction. Forty samples were taken as calibration set for establishing PLS model and 17 samples were used for validation of the method. The correlation coefficient and the root mean squared error of cross validation is 0.9986 and 0.43, respectively. Residual predictive deviation value of the model is as high as 18.8. The recoveries of the 17 validation samples in the concentration range of 3.39-24.39 mg/L are between 95.9%-113.1%. Therefore, the method may provide a candidate method to quantify albumin excretion in urine.     

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.     

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.     

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.     

Study of the temperature-dependent near-infrared spectra of water by two-dimensional correlation spectroscopy and principal components analysis

Near-infrared spectroscopy (NIR) is an important analytical tool in monitoring properties of systems for that water is a major constituent. For such objects of analysis a quality of information extracted from the NIR spectra depends essentially on used methods of analysis of a massive absorbance of water. Correctly chosen method should be able to identified rational number of overlapped components hidden under the broad band of water. The resolved components have to be justified on grounds of the structure of water and by relation to the properties a hydrogen-bonded network of water molecules. The interest in the correlation is imposed by a fact that hydrogen bonds of water around nonpolar group are significantly strengthened and weakened around polar groups. Intensity variations classified in this context could be valuable source of information on varying properties of the solute molecules embedded in water environment. Therefore, there is a big interest in methods that have a power for detailed analysis of the intensity changes in the broad NIR spectra. Two-dimensional correlation spectroscopy (2DCOS) and principal component analysis (PCA) are our proposition. In the analysis of the temperature-dependent NIR spectra of water by means of the two methods we have focused on the interpretation of the 2DCOS results through the concept of linear and nonlinear relationships. Moreover, a cascaded curve fitting procedure has been employed. Presented approach should be very instructive of how to interpret the features of the 2D results that frequently is not a straightforward task.     

Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration

A principal component regression (PCR) model is built for prediction of total antioxidant capacity in green tea using near-infrared (NIR) spectroscopy. The modelling procedures are systematically studied with the focus on outlier detection. Different outlier detection methods are used and compared. The root mean square error of prediction (RMSEP) of the final model is comparable to the precision of the reference method.     

Determination of water in lubricating oils by mid- and near-infrared spectroscopy

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy were used to determine water in lubricating oils with high additive contents that introduce large errors in determinations by the Karl-Fischer and hydride methods. MIR spectra were obtained in the attenuated total reflectance (ATR) mode and exhibited water specific band absorption in the region 3100–3700cm^–1, which facilitated calibration. Multivariate (partial least-squares regression, PLSR) and univariate calibration (based on peak height and band area as independent variables) were tested. Both led to errors of prediction less than 5%. NIRS determinations rely on absorbance and first-derivative spectra, in addition to two different types of multivariate calibration,viz. inverse multiple linear regression (MLR) and partial least-squares regression (PLSR). Both approaches gave similar results, with errors of prediction less than 2%.For none of the proposed approaches any sample pretreatment for recording spectra is required.     

Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry

Near-infrared (NIR) spectroscopy in conjunction with chemometric techniques allows on-line monitoring in real time, which can be of considerable use in industry. If it is to be correctly used in industrial applications, generally some basic considerations need to be taken into account, although this does not always apply. This study discusses some of the considerations that would help evaluate the possibility of applying multivariate calibration in combination with NIR to properties of industrial interest. Examples of these considerations are whether there is a relation between the NIR spectrum and the property of interest, what the calibration constraints are and how a sample-specific error of prediction can be quantified. Various strategies for maintaining a multivariate model after it has been installed are also presented and discussed.     

Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis

Laser-induced breakdown spectroscopy (LIBS) has been applied to the analysis of three chromium-doped soils. Two chemometric techniques, principal components analysis (PCA) and neural networks analysis (NNA), were used to discriminate the soils on the basis of their LIBS spectra. An excellent rate of correct classification was achieved and a better ability of neural networks to cope with real-world, noisy spectra was demonstrated. Neural networks were then used for measuring chromium concentration in one of the soils. We performed a detailed optimization of the inputs of the network so as to improve its predictive performances and we studied the effect of the presence of matrix-specific information in the inputs examined. Finally the inputs of the network—the spectral intensities—were replaced by the line areas. This provided the best results with a prediction accuracy and precision of about 5% in the determination of chromium concentration and a significant reduction of the data, too. Awarded a poster prize on the occasion of the Euro-Mediterranean Symposium on Laser-induced Breakdown Spectroscopy (EMSLIBS 2005), Aachen, Germany, 6–9 September 2005.     

ASTM clustering for improving coal analysis by near-infrared spectroscopy

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.     

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting–PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.     

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.     

Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method.     

Qualitative and quantitative analysis in quality control of traditional Chinese medicines

Separation techniques with high efficiency and sensitive detection have been widely used for quality control of traditional Chinese medicines (TCMs). High-performance liquid chromatography, gas chromatography, and capillary electrophoresis are commonly used to separate various components in TCMs. Ultraviolet detection, fluorescence detection, evaporative light-scattering detection, mass spectrometry and nuclear magnetic resonance can be applied to separation techniques for qualitative and quantitative analysis of TCMs. The development of quality control for TCMs based on quantitative and qualitative analysis from 2000 to 2007 are reviewed; the fingerprint technique is also discussed due to its broad application in the quality control of TCMs. Prospects for further research based on our primary results are also discussed.     

Quality control of cosmetic mixtures by NIR spectroscopy

Cosmetic preparations typically consist of mixtures of various compounds of natural origin or their derivatives. Their analysis is made rather difficult by their usually high complexity and is utterly impossible with a single analytical method; also, there is usually little to be gained by determining every individual component of the mixture. Rather, analyses are aimed at ensuring a proper balance between the contents of each component and thus require the use of methods capable of delivering global information. The combined use of near-infrared (NIR) spectroscopy and multivariate spectral processing chemometric techniques has enabled the development of effective methods for establishing the composition of complex samples with acceptable levels of analytical properties, such as accuracy, precision and throughput. In this work, we developed partial least squares calibration models for the determination of each component in a cosmetic mixture, and global indices (viz. the hydroxyl value), simply from the NIR spectrum of the sample. The models thus obtained are accurate enough for use in quality control analyses of cosmetic preparations and provide an effective alternative to existing conventional global methods. Experimental setup for measurement