Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection

This paper proposes a methodology for cigarette classification employing Near Infrared Reflectance spectrometry and variable selection. For this purpose, the Successive Projections Algorithm (SPA) is employed to choose an appropriate subset of wavenumbers for a Linear Discriminant Analysis (LDA) model. The proposed methodology is applied to a set of 210 cigarettes of four different brands. For comparison, Soft Independent Modelling of Class Analogy (SIMCA) is also employed for full-spectrum classification. The resulting SPA-LDA model successfully classified all test samples with respect to their brands using only two wavenumbers (5058 and 4903 cm⁻¹). In contrast, the SIMCA models were not able to achieve 100% of classification accuracy, regardless of the significance level adopted for the F-test. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for assessment of cigarette authenticity.     

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.     

Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach

A novel strategy for building and maintaining calibration models has been developed for use when the future boundaries of the sample set are unknown or likely to change. Such a strategy could have an impact on the economics and time required to obtain and maintain a calibration model for routine analysis. The strategy is based on both principal component analysis (PCA) and partial least squares (PLS) multivariate techniques. The principal action of the strategy is to define how “similar” a new sample is to the samples currently defining the calibration dataset. This step is performed by residuals analysis, following PCA. If the new sample is considered to have a spectrum “similar” to previously available spectra, then the model is assumed able to predict the analyte concentration. Conversely, if the new sample is considered “dissimilar”, then there is new information in this sample, which is unknown to the calibration model and the new sample is added automatically to the calibration set in order to improve the model. The strategy has been applied to a real industrial dataset provided by BP Amoco Chemicals. The data consists of spectra of 102 sequential samples of a raw material. The strategy produced an accurate calibration model for both target components starting with only the first four samples, and required a further 17 reference measurements to maintain the model for the whole sampling sequence, which was over a 1-year period.     

The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration

This study proposes an analytical method for the simultaneous near infrared (NIR) spectrometric determination of palmitic, oleic, linoleic and linolenic acids in sea buckthorn seed oil. For this purpose, four different combinations of multivariate calibration methods and variable selections were evaluated: partial least squares (PLS) with full spectrum; PLS with uninformative variables elimination (UVE); PLS with competitive adaptive reweighted sampling (CARS); and multiple linear regression (MLR) with uninformative variable elimination combined with successive projections algorithm (UVE-SPA). An independent set of samples was employed to evaluate the performance of the resulting models. The UVE-SPA-MLR model developed with a few spectral variables provided the best results for each parameter. The values of relative errors of prediction (REP) from the UVE-SPA-MLR model for palmitic, oleic, linoleic and linolenic acids are 1.77%, 1.20%, 1.02% and 1.40%, respectively. These results indicate that this method is a feasible and fast method for the determination of the fatty acid content of sea buckthorn seed oil.     

Determining the quality of insulating oils using near infrared spectroscopy and wavelength selection

This study presents an analytical method for determining interfacial tension and relative density in insulating oils using near infrared spectrometry (NIR). Five different strategies of regression were evaluated: partial least squares (PLS) with significant regression coefficients selected by jack-knife algorithm; interval PLS (iPLS); multiple linear regression (MLR) with variable selection by genetic algorithm (MLR/GA), successive projections algorithm (MLR/SPA) and stepwise strategy (SR/MLR). The overall results point to MLR/SPA as the best modeling strategy. The strategy is simpler and uses fewer spectral variables.     

Predicting percent composition of blends of biodiesel and conventional diesel using gas chromatography-mass spectrometry, comprehensive two-dimensional gas chromatography-mass spectrometry, and partial least squares analysis

The percent composition of blends of biodiesel and conventional diesel from a variety of retail sources were modeled and predicted using partial least squares (PLS) analysis applied to gas chromatography-total-ion-current mass spectrometry (GC-TIC), gas chromatography-mass spectrometry (GC-MS), comprehensive two-dimensional gas chromatography-total-ion-current mass spectrometry (GCxGC-TIC) and comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS) separations of the blends. In all four cases, the PLS predictions for a test set of chromatograms were plotted versus the actual blend percent composition. The GC-TIC plot produced a best-fit line with slope = 0.773 and y-intercept = 2.89, and the average percent error of prediction was 12.0%. The GC-MS plot produced a best-fit line with slope = 0.864 and y-intercept = 1.72, and the average percent error of prediction was improved to 6.89%. The GCxGC-TIC plot produced a best-fit line with slope = 0.983 and y-intercept = 0.680, and the average percent error was slightly improved to 6.16%. The GCxGC-MS plot produced a best-fit line with slope = 0.980 and y-intercept = 0.620, and the average percent error was 6.12%. The GCxGC models performed best presumably due to the multidimensional advantage of higher dimensional instrumentation providing more chemical selectivity. All the PLS models used 3 latent variables. The chemical components that differentiate the blend percent compositions are reported.     

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.     

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.     

近红外光谱法分析土壤中的有机质和氮素

应用近红外光谱技术测定土壤中的全氮、有机质、碱解氮,分别测定了2mm、0.15mm粒度的风干土在4000cm^-1～12000cm^-1波数范围的近红外光谱,用偏最小二乘法建立数学模型来进行含量预测,结果表明近红外光谱与土壤有机质、全氮、碱解氮具有良好的相关性,2mm风干土碱解氮建模的决定系数R^2为92.39,相对标准偏差为7.5%;2mm风干土全氮建模的决定系数R^2为88,相对标准偏差为8.2%;0.15mm的全氮建模的决定系数R2为89.86,相对标准偏差为7.2%;0.15mm风干土有机质建模的决定系数R^2为96.41,相对标准偏差为8.3%。因此,用近红外光普法测定土壤有机质、全氮、碱解氮的含量是可行的。     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

Quantitative analysis of biodiesel in blends of biodiesel and conventional diesel by comprehensive two-dimensional gas chromatography and multivariate curve resolution

In this paper, a method to determine the composition of blends of biodiesel with mineral diesel (BXX) by multivariate curve resolution with Alternating Least Squares (MRC-ALS) combined to comprehensive two-dimensional gas chromatography with Flame Ionization Detection (GC × GC-FID) is presented. Chromatographic profiles of BXX blends produced with biodiesels from different sources were used as input data. An initial evaluation carried out after multiway principal component analysis (MPCA) was used to reveal regions of the chromatograms were the signal was likely to be dependent on the concentration of biodiesel, regardless its vegetable source. After this preliminary step MCR-ALS modeling was carried out only using relevant parts of the chromatograms. The resulting procedure was able to predict accurately the concentration of biodiesel in the BXX samples regardless of its origin.     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.     

Application of near infrared spectroscopy and multivariate data analysis for the evaluation of glue lines of untreated and copper azole treated laminated timber before and after ageing

The necessity for inspection and assessment of glued laminated timber structures in service has raised interest in the evaluation of the glue lines. Glue line spectra were analysed and are discussed in detail with respect to spectral contributions from the adhesive, the hardener, the wood lamella below the adhesive, the curing temperature as well as ageing-related spectral changes. The combination of near infrared (NIR) spectroscopy and principal component analysis (PCA) allowed distinguishing between aged and non-aged samples and different copper azole preservative treatment levels of phenol-resorcinol-formaldehyde (PRF) glue lines. NIR-based partial least squares (PLS) regression modelling was performed for the glue line shear strength and for the curing temperature. These findings show that NIR spectroscopy is a fast and useful technique to evaluate the degradation on the PRF glue lines of untreated and copper azole treated laminated timber.     

Analysis and classification of bacteria by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and a chemometric approach

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a useful technique for the identification of bacteria on the basis of their characteristic protein mass spectrum fingerprint. Highly standardized instrumental analytical performance and bacterial culture conditions are required to achieve useful information. A chemometric approach based on multivariate analysis techniques was developed for the analysis of MALDI data of different bacteria to allow their identification from their fingerprint. Principal component analysis, linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were applied to the analysis of the MALDI MS mass spectra of two pathogenic bacteria, Escherichia coli O157:H7 and Yersinia enterocolitica, and the non-pathogenic E. coli MC1061. Spectra variability was assessed by growing bacteria in different media and analysing them at different culture growth times. After selection of the relevant variables, which allows the evaluation of an m/z value pattern with high discriminant power, the identification of bacteria by LDA and SIMCA was performed independently of the experimental conditions used. In order to better evaluate the analytical performance of the approach used, the ability to correctly classify different bacteria, six wild-type strains of E. coli O157:H7, was also studied and a combination of different chemometric techniques with a severe validation was developed. The analysis of spiked bovine meat samples and the agreement with an independent chemiluminescent enzyme immunoassay demonstrated the applicability of the method developed for the detection of bacteria in real samples. The easy automation of the MALDI method and the ability of multivariate techniques to reduce interlaboratory variability associated with bacterial growth time and conditions suggest the usefulness of the proposed MALDI MS approach for rapid routine food safety checks. Figure Workflow of the developed MALDI-TOF MS and chemometric approach for the analysis and classification of bacteria Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Strain and phase identification of the U.S. category B agent Coxiella burnetii by matrix assisted laser desorption/ionization time-of-flight mass spectrometry and multivariate pattern recognition

Accurate bacterial identification is important in diagnosing disease and in microbial forensics. Coxiella burnetii, a highly infective microorganism causative of the human disease Q fever, is now considered a U.S. category B potential bioterrorism agent. We report here an approach for the confirmatory identification of C. burnetii at the strain level which involves the combined use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and supervised pattern recognition via Partial Least Squares-Discriminant Analysis (PLS-DA). C. burnetii isolates investigated in this study included the following prototype strains from different geographical and/or historical origins and with different antigenic properties: Nine Mile I, Australian QD, M44, KAV, PAV, Henzerling, and Ohio. After culture and purification following standard protocols, linear MALDI-TOF mass spectra of pure bacterial cultures were acquired in positive ion mode. Mass spectral data were normalized, baseline-corrected, denoised, binarized and modeled by PLS-DA under crossvalidation conditions. Robustness with respect to uncontrolled variations in the sample preparation and MALDI analysis protocol was assessed by repeating the experiment on five different days spanning a period of 6 months. The method was validated by the prediction of unknown C. burnetii samples in an independent test set with 100% sensitivity and specificity for five out of six strain classes.