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.     

Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm^?1, assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

Determination of oil wells productivity using multivariate FTIR data

Traditional methods for productivity surveillance of oil wells mainly are consisted of using test-separator units with expensive devices, protections, inspections, operations, facilities, infrastructures and repairing services. The objective of this work is to utilize a novel approach to predict the accurate productivity of oil wells using a single sample point at the line of blend oil. The present method is based upon performing multivariate regression of infrared spectra, which taken from the real samples of Iranian offshore oil wells. The experimental results revealed that the present approach is appropriate for precocious, quick and reliable surveillance of individual oil wells located in an oil field. The model has predicted the accurate productivity of real oil wells with respect to the current expensive techniques since 2010.     

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.     

Determination of intrinsic viscosity of poly(ethylene terephthalate) using infrared spectroscopy and multivariate calibration method

A methodology was developed to determine the intrinsic viscosity of poly(ethylene terephthalate) (PET) using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration (MVC) methods. Multivariate partial least squares calibration was applied to the spectra using mean centering and cross validation. The results were correlated to the intrinsic viscosities determined by the standard chemical method (ASTM D 4603-01) and a very good correlation for values in the range from 0.346 to 0.780 dL g⁻¹ (relative viscosity values ca. 1.185-1.449) was observed. The spectrophotometer detector sensitivity and the humidity of the samples did not influence the results. The methodology developed is interesting because it does not produce hazardous wastes, avoids the use of time-consuming chemical methods and can rapidly predict the intrinsic viscosity of PET samples over a large range of values, which includes those of recycled materials.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Fire impact on forest soils evaluated using near-infrared spectroscopy and multivariate calibration

The assessment of physico-chemical properties in forest soils affected by fires was evaluated using near infrared reflectance (NIR) spectroscopy coupled with chemometric methods. In order to describe the soil properties, measurements were taken of the total organic carbon on solid phase, the total nitrogen content, the organic carbon and the specific absorbences at 254 and 280 nm of humic substances, organic carbon in humic and fulvic acids, concentrations of NH₄⁺, Ca²⁺, Mg²⁺, K⁺ and phosphorus in addition to NIR spectra. Then, a fire recurrence index was defined and calculated according to the different fires extents affecting soils. This calculation includes the occurrence of fires as well as the time elapsed since the last fire. This study shows that NIR spectroscopy could be considered as a tool for soil monitoring, particularly for the quantitative prediction of the total organic carbon, total nitrogen content, organic carbon in humic substances, concentrations of phosphorus, Mg²⁺, Ca²⁺ and NH₄⁺ and humic substances UVSA₂₅₄. Further validation in this field is necessary however, to try and make successful predictions of K⁺, organic carbon in humic and fulvic acids and the humic substances UVSA₂₈₀. Moreover, NIR coupled with PLS can also be useful to predict the fire recurrence index in order to determine the spatial variability. Also this method can be used to map more or less burned areas and possibly to apply adequate rehabilitation techniques, like soil litter reconstitution with organic enrichments (industrial composts) or reforestation. Finally, the proposed recurrence index can be considered representative of the state of the soils.     

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 oleic acid in sunflower seeds by infrared spectroscopy and multivariate calibration method

A method for the determination of oleic acid in sunflower seeds is proposed. One hundred samples of sunflower seeds were analyzed by near-infrared diffuse reflectance spectroscopy (NIRDRS). The direct measures were realized in ground and sifted seeds. The PLS multivariate calibration model was obtained using first derivate absorbance values as response matrix, while the oleic acid concentration matrix was obtained analyzing the seed samples by gas chromatography with a flame ionization detector (GC-FID). The NIRDRS-PLS model was validated externally using unknown samples of sunflower seeds. The accuracy and precision of the method was evaluated using GC as reference method. The following figures of merit (FOM) were obtained: LOD = 3.4% (w/w); LOQ = 11.3% (w/w); SEN = 8 × 10⁻⁵; SEL = 0.15; analytical sensibility (γ) = 1.5 and linear range (LR) = 18.1-89.2% (w/w). This method is useful for the fast determination of oleic acid in sunflower seeds and for quality control of raw materials.     

Determination of acetonitrile and ethanol in water by guided microwave spectroscopy with multivariate calibration

The feasibility of using guided microwave spectroscopy (GMS) utilizing the frequency range 0.25-3.20 GHz, was combined with multivariate calibration for the determination of acetonitrile or ethanol concentration in water. A wide range of different concentrations was used (up to 30% v/v). Partial least squares (PLS) and weighted ridge regression (WRR) was applied to generate a model for prediction, based upon the microwave spectra. A high level of collinearity was observed in both of the sample data sets and this was reduced by background subtraction. The prediction ability for the two types of regression models were found to be comparable with the percentage error of prediction (PEP) being approximately 2.5% for the acetonitrile samples and 1.1% for ethanol samples.     

Determination of the content and identity of lidocaine solutions with UV-visible spectroscopy and multivariate calibration

A method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of ultraviolet-visible (UV-Vis) spectroscopy, orthogonal signal correction (OSC) and multivariate calibration with soft independent modelling of class analogy (SIMCA) classification and partial least squares (PLS) regression. The content was determined with PLS regression and the identity with PLS regression and SIMCA classification. The method was tested on the local anaesthetic compound lidocaine. For the validation, external test sets of both manufactured sample solutions and samples from a stability study were used. For comparison with this new method, liquid chromatography was used as a reference method. The results show that in respect of accuracy, precision and repeatability, the new method is comparable to the reference method. The main advantage over liquid chromatography is the much shorter time of analysis and the simpler analytical procedure. An estimate of the analysis time saved with the proposed method compared with using liquid chromatography, together with practical considerations, is given.     

Surface and depth-profile analysis using FTIR spectroscopy

Summary The introduction of Fourier Transform techniques and the increasing use of computers in infrared spectroscopy has made new techniques of investigation available to the spectroscopist, such as photoacoustic spectroscopy (PAS) and IR microscopy. These methods now complement the techniques of specular reflectance and attenuated total reflectance. Thin films on metals, sometimes with a thickness of much less than a micron, can be studied by various specular reflectance methods. The physical basis of the attenuated total reflectance technique (ATR) leads to a penetration of the radiation in the order of a few microns. It is, therefore, especially suitable for the investigation of surfaces and of layers close to the surface. By changing the modulation frequency of the IR radiation, i.e. the mirror velocity of the FTIR spectrometer, photoacoustic spectroscopy (PAS) can be employed to study layers at various depths below the surface of a sample. Therefore, this technique allows depth-profile analysis, so that PAS reveals itself as a complementary method to attenuated total reflectance spectroscopy. Samples with inhomogeneous profiles, e.g. laminated polymer films, can often be prepared as microtome slices perpendicular to the layered structure. Using infrared microscopy it is now possible to investigate different regions of the cross-section easily. The size of the regions that can be studied in this way may be as small as a few microns.

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Oberflächen- und Tiefenprofilanalyse mit Hilfe der FTIR-Spektroskopie

     

Multicomponent kinetic determinations using multivariate calibration techniques

Multivariate calibration techniques for use in multicomponent kinetic-based determinations are reviewed. Multivariate calibration is a chemometric tool that continues to grow in popularity among analytical chemists. Multicomponent kinetic methods depend on differences in rates of reactions or processes to distinguish among the components. Kinetic profiles or a combination of kinetic profiles and spectra are commonly used. Because of their ability to process large quantities of data, multivariate calibration techniques are well suited for kinetic-based determinations. The concepts and principles of multivariate calibration are discussed first. Classical least squares regression, principal component regression, partial least squares regression and artificial neural networks are the multivariate calibration techniques considered here in detail. Recent examples of the application of these techniques to multicomponent kinetic determinations are reviewed. Both single and multiwavelength kinetic data are considered.     

Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel

This paper evaluates analytical methods based on near infrared (NIR) and middle infrared (MIR) spectroscopy and multivariate calibration to monitor the stability of biodiesel. There was a focus on three parameters: oxidative stability index, acid number and water content. Ethylic and methylic biodiesel from different feedstocks were used in experiments of accelerated aging, in order to take into account the wide variety of oilseeds and feedstocks available in Brazil. Partial least squares (PLS) and multiple linear regression (MLR) models were developed. Different pre-processing techniques and spectral variable/regions selection algorithms were evaluated. For MLR models, the successive projection algorithm (SPA) was employed. Interval PLS (iPLS) and selection of variables taking into account the significant regression coefficients were used for PLS models. Results showed that both near and middle infrared regions, and all variable selection methods tested were efficient for predicting these three important quality parameters of B100, the root mean squares error of prediction (RMSEP) values being comparable to the reproducibility of the corresponding standard method for each property investigated.     

Qualitative and semi-quantitative analysis in ICP-AES using multivariate calibration

This paper describes a two step algorithm for qualitative and semiquantitative analysis in inductively coupled plasma—atomic emission spectroscopy (ICP-AES) including the preceding data acquisition. Sample and calibration spectra were recorded in 0.05 nm spectral windows centered about the most prominent lines of the 49 covered elements. In the first step of the analysis of an unknown sample spectrum it is decided which of these elements can be excluded from further operations because of the absence of their most prominent lines applying a multivariate criterion for peak detection. For the remaining elements quantitative analysis is performed via multivariate calibration taking into consideration the most prominent line of each element and possibly interfering lines of those elements which were not found absent during the first step. After the estimation of the element concentrations multivariate detection limits are used as criterion for the presence of an element. Results for six synthetic samples of increasing complexity prepared from standard solutions and three standard reference materials are given.     

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration

Extent-based kinetic identification is a kinetic modeling technique that uses concentration measurements to compute extents and identify reaction kinetics by the integral method of parameter estimation. This article considers the case where spectroscopic data are used together with a calibration model to predict concentrations. The calibration set is assumed to be constructed from reacting data that include pairs of concentration and spectral data. Alternatively, one can use the concentration- and spectral contributions of the reactions and mass transfers, which are obtained by pretreatment in reaction- and mass-transfer-variant form. The extent-based kinetic identification using concentrations predicted from spectroscopic data is illustrated through the simulation of both a homogeneous and a gas–liquid reaction system.     

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L⁻¹. Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV.Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.     

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42 μg ml⁻¹ albumin and 0-12.7 μg ml⁻¹ IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Δ60 nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.