Quantitative measurements of loss on ignition in iron ore using laser-induced breakdown spectroscopy and partial least squares regression analysis

Laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR) have been applied to perform quantitative measurements of a multiple-species parameter known as loss on ignition (LOI), in a combined set of run-of-mine (ROM) iron ore samples originating from five different iron ore deposits. Global calibration models based on 65 samples and their duplicates from all the deposits with LOI ranging from 0.5 to 10 wt% are shown to be successful for prediction of LOI content in pressed pellets as well as bulk ore samples. A global independent dataset comprising a further 60 samples was used to validate the model resulting in the best validation R(2) of 0.87 and root mean square error of prediction (RMSEP) of 1.1 wt% for bulk samples. A validation R(2) of 0.90 and an RMSEP of 1.0 wt% were demonstrated for pressed pellets. Data preprocessing is shown to improve the quality of the analysis. Spectra normalization options, automatic outlier removal and automatic continuum background correction, which were used to improve the performance of the PLSR method, are discussed in detail.     

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.     

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.     

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path Fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

The use of multiple calibration sets in partial least squares (PLS) regression was proposed to improve the quantitative determination of NH(3) over wide concentration ranges from open-path Fourier transform infrared (OP/FT-IR) spectra. The spectra were measured near animal farms, where the path-integrated concentration of NH(3) can fluctuate from nearly zero to as high as approximately 1000 ppm-m. PLS regression with a single calibration set did not cover such a large concentration range effectively, and the quantitative accuracy was degraded due to the nonlinear relationship between concentration and absorbance for spectra measured at low resolution (1 cm(-1) and poorer.) In PLS regression with multiple calibration sets, each calibration set covers a part of the entire concentration range, which significantly decreases the serious nonlinearity problem in PLS regression occurring when only a single calibration set is used. The relative error was reduced from approximately 6% to below 2%, and the best results were obtained with four calibration sets, each covering one quarter of the entire concentration range. It was also found that it was possible to build the multiple calibration sets easily and efficiently without extra measurements.     

Quantitative analysis of carbon content in fly ash using LIBS based on support vector regression

Laser-induced breakdown spectroscopy (LIBS) has been proved as an on-line detection technology to measure the carbon content in fly ash, which is beneficial for immediate assessment of the boiler combustion efficiency. Support vector regression (SVR) was adopted as the quantitative model for the carbon content measurement in fly ash in this study. Ash species was one of the key factors affecting quantitative accuracy. Experiments have proven that, the index of plasma temperature and the electron density among different species could be similar, while the partition function ratios and the temperature correction factor showed obvious differences among different ash species. Based on the partition function ratios, the Matrix Effect Correction Factor (MECF) was defined. SVR model was optimized by MECF and the analysis results showed that the correlation coefficient of calibration (R²) increased from 0.989 to 0.991, the root-mean-square error of calibration (RMSEC) decreased from 2.02% to 0.850%, the root-mean-square error of prediction (RMSEP) decreased from 2.13% to 1.07%, and averaged relative standard deviation (ARSD) decreased from 8.62% to 1.89%. The results showed that SVR combined with MECF was an effective method to improve the accuracy of LIBS quantitative analysis of the carbon content in fly ash.     

Effect of diet composition on the determination of ash and moisture content in solid cattle manure using visible and near-infrared spectroscopy

Visible and near-infrared (Vis-NIR, 350-2500 nm) diffuse reflection spectroscopy (DRS) models built from "as-collected" samples of solid cattle manure accurately predict concentrations of moisture and crude ash. Because different organic molecules emit different spectral signatures, variations in livestock diet composition may affect the predictive accuracy of these models. This study investigates how differences in livestock diet composition affect Vis-NIR DRS prediction of moisture and crude ash. Spectral signatures of solid manure samples (n = 216) from eighteen groups of cattle on six different diets were used to calibrate and validate partial least squares (PLS) regression models. Seven groups of PLS models were created and validated. In the first group, two-thirds of all samples were randomly selected as the calibration set and the remaining one-third were used for the validation set. In the remaining six groups, samples were grouped by livestock diet (ration). Each ration in turn was held out of calibrations and then used as a validation set. When predicting crude ash, the fully random calibration model produced a root mean square deviation (RMSD) of 2.5% on a dry basis (db), ratio of standard error of prediction to the root mean squared deviation (RPD) of 3.1, bias of 0.14% (db), and correlation coefficient r(2) of 0.90., When predicting moisture, an RMSD of 1.5% on a wet basis (wb), RPD of 4.3, bias of -0.09% (wb), and r(2) of 0.95 was achieved. Model accuracy and precision were not impaired by exclusion of any single ration from model calibration.     

Quality and statistical classification of Brazilian vegetable oils using mid-infrared and Raman spectroscopy

Palm oil, soy oil, sunflower oil, corn oil, castor oil, and rapeseed oil were analyzed with Fourier transform infrared (FT-IR) and FT-Raman spectroscopy. The quality of different oils was evaluated and statistically classified by principal component analysis (PCA) and a partial least squares (PLS) regression model. First, a calibration set of spectra was selected from one sampling batch. The qualitative variations in spectra are discussed with a prediction of oil composition (saturated, mono- and polyunsaturated fatty acids) from mid-infrared analysis and iodine value from FT-Raman analysis, based on ratioing the intensity of bands at given wavenumbers. A more robust and convincing oil classification is obtained from two-parameter statistical models. The statistical analysis of FT-Raman spectra favorably distinguishes according to the iodine value, while the mid-infrared spectra are most sensitive to hydroxyl moieties. Second, the models are validated with a set of spectra from another sampling batch, including the same oil types as-received and after different aging times together with a hydrogenated castor oil and high-oleic sunflower oil. There is very good agreement between the model predictions and the Raman measurements, but the statistical significance is lower for mid-infrared spectra. In the future, this calibration model will be used to check vegetable oil qualities before using them in polymerization processes.     

Increasing robustness against changes in the interferent structure by incorporating prior information in the augmented classical least-squares framework

A class of multivariate calibration methods called augmented classical least squares (ACLS) has been proposed which combines an explicit linear additive model with the predictive power of inverse models, such as principal component regression (PCR) and partial least squares (PLS). Because of its use of the explicit linear additive model, ACLS provides an interesting framework to incorporate different sources of prior information, such as measured pure component spectra, in the model. In this study, the predictive power of ACLS models incorporating different amounts of prior information has been compared to that of PCR and PLS using two examples, a designed experiment and one with biological samples. In both cases, the ACLS models showed predictive power comparable to PLS under idealized validation conditions. When a different interferent structure was present in the validation samples, the predictive power of the inverse models (PCR and PLS) dramatically decreased, with an increase in root-mean-squared error of prediction by a factor of 3.5 for the first example and a factor of 2 in the second example. The incorporation of prior information in the ACLS framework was found to considerably reduce or even completely remove these dramatic effects, especially when the pure component contributions for the interferents were taken into account.     

Development of an apparatus for on-line analysis of unburned carbon in fly ash using laser-induced breakdown spectroscopy (LIBS)

The level of unburned carbon in fly ash is an important criteria for evaluating the combustion efficiencies of boilers, as well as the commercial value of the produced fly ash. In this work, an automated prototype laser-induced breakdown spectroscopy (LIBS) apparatus comprising an isokinetic sampler, a sample preparation module, and a LIBS module has been developed for possible application to power plants for on-line analysis of unburned carbon in fly ash without being affected by the type of coal burned. Emphasis is placed on the structure and operation of the LIBS apparatus, the optimum suction capacity selection, the analytical methods for estimation of the exact C line intensity, and the proper calibration model established for minimizing the matrix effects, which enable the minimization of matrix effects and obtaining more accurate compositional measurements. Good agreement has been found between the laboratory measurement results from the LIBS method and those from the traditional method. The measurement accuracy presented here for unburned carbon analysis is estimated to be 0.26%, while the average relative error is 3.81%.     

Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using Fourier transform Raman spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.     

Cyclic subspace regression with analysis of wavelength-selection criteria

Common methods of building linear calibration models are principal component regression (PCR), partial least squares (PLS), and least squares (LS). Recently, the method of cyclic subspace regression (CSR) has been presented and shown to provide PCR, PLS, LS and other related intermediate regressions with one algorithm. When forming a linear model with spectral data for quantitative analysis, prediction results can be adversely affected by responses that do not conform well to the linear model proposed. Wavelength selection can be used to eliminate wavelengths where such problem responses occur. It has recently been reported that CSR regression vectors can be formed by summing weighted eigenvectors where weights are determined from the hat matrix, singular values, and eigenvectors characterizing the sample space. Investigation of these weights shows that wavelength selection based on loading vectors can be misleading. Specifically, by using CSR it is shown that a small weight for an eigenvector can annihilate a large peak in a loading vector. In this study, correlograms are used with CSR regression vectors and eigenvector weights as wavelength-selection criteria. It is demonstrated that even though a model generated by LS for a wavelength subset produces substantially reduced prediction errors relative to PCR and PLS, CSR weight plots show that the LS model overfits and should not be used. Simulated situations containing spectral regions with excess noise or nonlinear responses are examined to study the effectiveness of wavelength selection based on the previously listed criteria. Near infrared spectra of gasoline samples with several known properties are also studied.     

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.     

Correlation of limestone beds using laser-induced breakdown spectroscopy and chemometric analysis

Correlation of limestone beds is commonly based on a variety of features, including the age of the bed, the fossil assemblage, internal sedimentary structures, and the relationship to other units in the stratigraphy. This study uses laser-induced breakdown spectroscopy (LIBS) to correlate 16 limestone beds from Kansas, USA, using three multivariate techniques: (1) soft independent modeling of class analogy (SIMCA) classification, (2) a partial least squares regression, 1 variable (PLS-1) model in which the spectra are regressed against a matrix of the indicator variables 1 through 16, and (3) a matching algorithm that consists of a sequence of binary PLS-1 models. Each gravel-sized limestone particle was analyzed by one LIBS shot; ten spectra were averaged into a single spectrum for chemometric analysis. The entire spectrum (198-969 nm wavelength) is used for multivariate analysis; the only preprocessing is averaging. The SIMCA and PLS-1 models fail to discriminate among the beds, which are chemically similar. In contrast, the matching algorithm has a success rate of 95% to 96%, using half of the spectra to train the model and the other half of the spectra to validate it. However, 100% success can be accomplished by accepting the classification of the majority of spectra for a given bed as the correct classification. This study indicates that LIBS can be applied to complex geologic correlation problems and provide rapid, accurate results.     

Controlling sugar quality on the basis of fluorescence fingerprints using robust calibration

The aim of our study was to highlight the benefits of robust calibration in the context of process control. Two properties were monitored — the color and ash content of sugar samples. It was shown for the data being studied that robust models, constructed using the partial robust M-regression technique, have a better fit to the majority of the data and prediction properties than the classic partial least squares and N-way partial least squares models. In particular, the constructed calibration models were characterized by a root mean square errors improved by 1.60% and 1.82% and a root mean square errors of prediction (for independent test samples) improved by 2.39% and 1.11% compared to classic partial least squares models constructed for color and ash content, respectively.     

Effects of nonlinearities and uncorrelated or correlated errors in realistic simulated data on the prediction abilities of augmented classical least squares and partial least squares

Comparisons of prediction models from the new augmented classical least squares (ACLS) and partial least squares (PLS) multivariate spectral analysis methods were conducted using simulated data containing deviations from the idealized model. The simulated data were based on pure spectral components derived from real near-infrared spectra of multicomponent dilute aqueous solutions. Simulated uncorrelated concentration errors, uncorrelated and correlated spectral noise, and nonlinear spectral responses were included to evaluate the methods on situations representative of experimental data. The statistical significance of differences in prediction ability was evaluated using the Wilcoxon signed rank test. The prediction differences were found to be dependent on the type of noise added, the numbers of calibration samples, and the component being predicted. For analyses applied to simulated spectra with noise-free nonlinear response, PLS was shown to be statistically superior to ACLS for most of the cases. With added uncorrelated spectral noise, both methods performed comparably. Using 50 calibration samples with simulated correlated spectral noise, PLS showed an advantage in 3 out of 9 cases, but the advantage dropped to 1 out of 9 cases with 25 calibration samples. For cases with different noise distributions between calibration and validation, ACLS predictions were statistically better than PLS for two of the four components. Also, when experimentally derived correlated spectral error was added, ACLS gave better predictions that were statistically significant in 15 out of 24 cases simulated. On data sets with nonuniform noise, neither method was statistically better, although ACLS usually had smaller standard errors of prediction (SEPs). The varying results emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Even when the differences between the standard error of predictions were statistically significant, in most cases the differences in SEP were small. This study demonstrated that unlike CLS, ACLS is competitive with PLS in modeling nonlinearities in spectra without knowledge of all the component concentrations. This competitiveness is important when maintaining and transferring models for system drift, spectrometer differences, and unmodeled components, since ACLS models can be rapidly updated during prediction when used in conjunction with the prediction augmented classical least squares (PACLS) method, while PLS requires full recalibration.     

Feasibility of laser-induced breakdown spectroscopy (LIBS) for classification of sea salts

We have investigated the feasibility of laser-induced breakdown spectroscopy (LIBS) as a fast, reliable classification tool for sea salts. For 11 kinds of sea salts, potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al), concentrations were measured by inductively coupled plasma-atomic emission spectroscopy (ICP-AES), and the LIBS spectra were recorded in the narrow wavelength region between 760 and 800 nm where K (I), Mg (I), Ca (II), Al (I), and cyanide (CN) band emissions are observed. The ICP-AES measurements revealed that the K, Mg, Ca, and Al concentrations varied significantly with the provenance of each salt. The relative intensities of the K (I), Mg (I), Ca (II), and Al (I) peaks observed in the LIBS spectra are consistent with the results using ICP-AES. The principal component analysis of the LIBS spectra provided the score plot with quite a high degree of clustering. This indicates that classification of sea salts by chemometric analysis of LIBS spectra is very promising. Classification models were developed by partial least squares discriminant analysis (PLS-DA) and evaluated. In addition, the Al (I) peaks enabled us to discriminate between different production methods of the salts.     

Calibration models for the vinyl acetate concentration in ethylene-vinyl acetate copolymers and its on-line monitoring by near-infrared spectroscopy and chemometrics: use of band shifts associated with variations in the vinyl acetate concentration to improve the models

The present study investigates calibration models for the vinyl acetate (VA) concentration in ethylene-vinyl acetate (EVA) copolymers and its on-line monitoring by near-infrared (NIR) spectroscopy and chemometrics. The key point in the present study is to make use of band shifts associated with concentration changes in the vinyl acetate (VA) for the improvement of the models. NIR spectra of EVA in melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and a FT-NIR laboratory system. Some of the bands in the NIR spectra for both states show significant shifts with the variations in the VA concentration. The peak shifts induced by the VA concentration changes are larger in the solid-state EVA than those in the melt-state EVA. We have developed calibration models for the VA concentration in the solid-state EVA and investigated how to improve the calibration models. The factor analysis of partial least squares (PLS) regression has suggested that the wavenumber shifts caused by the VA concentration changes affect the calibration models for the VA concentration in EVA. From the analysis, it has been proposed that the wavenumbers in the spectrum of one sample in nine EVA samples (VA concentration range: 0-41.1%) are shifted for the improvement of the calibration models, and the effects of the proposed method have been confirmed by using the PLS calibration models for the VA concentration in the solid EVA samples. As the next step, the effects of the wavenumber shift method have been explored for the calibration models for the VA concentration in the melt-state EVA. After that, the discrimination method using the score plots of PLS and the application sequence for the on-line monitoring to use the proposed wavenumber shift method were studied. The simulation results using the discrimination and wavenumber shift methods have shown that those methods are very effective to improve the predicted values of the calibration models for the on-line monitoring of the VA concentration in the melt-state EVA.     

Kernel analysis of partial least squares (PLS) regression models

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Transfer of multivariate calibrations between four near-infrared spectrometers using orthogonal signal correction

The transfer of partial least squares (PLS) calibration models among four near-infrared spectrometers was investigated for the quantitative analysis of thermoset resin polymers. A comparative study of second derivatives, multiplicative scatter correction, finite impulse response filtering, slope and bias correction, model updating (MU), and orthogonal signal correction (OSC) was conducted to determine which processing methods achieved model transferability. It is shown that OSC and MU were superior to the other calibration transfer methods, leading to very robust PLS models with enhanced predictive ability. It is also shown that the transfer results obtained with OSC were not significantly different from those obtained with model updating.