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.     

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     

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.     

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%.     

Use of NIR spectroscopy in the production of modified industrial resins

Natural resins are scarcely used, but after appropriate modification processes they acquire characteristics of viscosity, point of softening, stability, etc. that facilitate their application in fields such as paintings, varnishes, cosmetic, etc. The complexity of resins makes it very difficult to monitor the reactions involved in their modification, the extent of which is usually determined via more experimentally accessible parameters. However, the methods typically used to determine such parameters are slow and produce environmentally unfriendly waste.In this work, we assessed the potential of NIR spectroscopy, as an alternative to the traditional analytical methods, for monitoring the industrial processes involved in the production of modified resins. To this end, we developed PLS calibration models that were used to quantify physical (viscosity and cloud point) and chemical parameters (acid and hydroxyl numbers), with a view to characterize the evolution of the resins during the reaction that take place throughout the fabrication process.Samples were withdrawn at different times stages of the process for analysis with the proposed quantitation models; the data thus obtained were compared with those provided by reference methods. Based on the results, NIR spectroscopy is an effective choice for the accurate, expeditious monitoring of industrial resin modification processes.     

Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks

Back-propagation artificial neural networks (BP-ANN) are applied for modeling hydroxyl number and acid value of a set of 62 samples of polyester resins from their near infrared (NIR) spectra. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR) and partial least squares (PLS). The set of available samples is split into: (i) a training set, for models calculation; (ii) a test set, for setting the correct number of latent variables in PCR and PLS and for selecting the end point of the training phase of BP-ANN; (iii) a “production set” of samples, which are predicted to evaluate the models predictive ability. This approach guarantees that the predictive ability of the models is evaluated by genuine predictions. BP-ANN resulted always better than the classical PCR and PLS, from the point of view of the predictive ability. The study of the breakdown number of experiments to include in the training set showed instead that this factor does influence PCR and PLS at a lesser degree than what happens for BP-ANN. The latter approach requires a larger number of experiments for obtaining good results. The choice of optimal training sets is efficiently performed by Kohonen self-organizing maps (SOMs). It can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for monitoring the polyesterification of dicarboxylic acids with diols by predicting the acid and hydroxyl numbers directly along the process line.     

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.     

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.     

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.     

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.     

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.     

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.     

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

The application of mobile near-infrared (NIR) spectrometers in field measurements is growing. Calibration transfer techniques offer simple solutions for enabling models constructed on benchtop instruments for use on mobile spectrometers. Since different types of spectrometers with different components, scanning ranges and resolutions cause great differences in the spectral response, calibration transfer is difficult to apply. In this paper, we focus on calibration transfer among benchtop, portable and handheld spectrometers by a method of calibration transfer based on canonical correlation analysis (CTCCA). Its capability was illustrated by the example of a group of NIR spectra dataset for predicting reducing sugars, total sugar, and nicotine contents in tobacco leaves. The experimental results showed that the transferability of CTCCA was superior to other conventional calibration transfer methods, including piecewise direct standardization, spectral space transformation, calibration transfer based on independent component analysis, and calibration transfer based on the weight matrix. Moreover, the best transfer results were obtained in the three cases by canonical correlation analysis method executing transfer while the spectra were not interpolated, which shows that this approach has the advantage of easy implementation for calibration transfer. Therefore, CTCCA without interpolation calculation offers a new and simple solution for transferring the spectra acquired by mobile spectrometers to the optimized spectral models built on benchtop devices to improve the accuracy of the results. Additionally, the results show that the benchtop spectrometer is more suitable as the master instrument for calibration transfer with more accurate prediction than using a portable device as the master.     

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.     

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.     

Simultaneous determination of antimony(III) and antimony(V) by UV-vis spectroscopy and partial least squares method (PLS)

This paper describes a procedure for the speciation of antimony by UV-vis spectroscopy using pyrogallol as complexing agent. A partial least squares (PLS) regression was performed to resolve highly overlapping spectrophotometric signals obtained from mixtures of Sb(III) and Sb(V). The relative error in absolute value was less than 5% when concentrations of several mixtures were calculated. The minimum concentration determined was 3.96 × 10⁻⁵ mol dm⁻³ and 3.98 × 10⁻⁵ mol dm⁻³ for Sb(V) and Sb(III), respectively. The analysis of the possible effect of the presence of foreign ions in the solution was performed and the procedure was successfully applied to the speciation of antimony in pharmaceutical preparations and aqueous samples.     

Comparison between different data pre-treatment methods in the analysis of forage samples using near-infrared diffuse reflectance spectroscopy and partial least-squares multivariate calibration method

Moisture and protein content of alfalfa samples from Catalonia (Spain) have been analyzed by near-infrared (NIR) diffuse reflectance spectroscopy and multivariate calibration methods. In order to remove systematic variation in experimental data, such as base-line and multiplicative scatter effects, the evaluation of different data pre-processing methods is performed. Different figures of merit are used for quality assessment and comparison of these pre-treatment methods.     

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

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

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.