Wavelength Selection for Simultaneous Spectroscopic Analysis. Experimental and Theoretical Study

Simultaneous Content Determination of Mono-, Di-, and Fructo-oligosaccharides in Citrus Fruit Juices Using an FTIR-PLS Method Based on Selected Absorption Bands

A quantification method was developed to determine the sugar components, either following addition or enzymatic treatment, in citrus fruit juices containing additional fructo-oligosaccharides using midinfrared spectroscopy. For the quantification, we compared the results obtained by applying the simultaneous equation method, which uses very little wavenumber information, and the partial least squares (PLS) regression method, which requires a lot of wavenumber information. In order to prevent overfitting in the PLS method, we concentrated on reducing the amount of spectral data used in the analysis. The corresponding FTIR-PLS method led to an accurate quantification of the sugar contents, even in enzymatically treated orange juices with complicated compositions. The spectral data used for model calibration were significantly reduced by focusing on the absorption and assignment information of the sugar components. The RMSEs of Glc, Fru, Suc, GF2, and GF3 in enzyme-treated orange juice before and after spectral data reduction were 0.50, 0.46, 0.61, 0.74, and 0.61 g/L and 0.51, 0.49, 0.73, 0.86, and 0.61 g/L, respectively. The developed method could be easily implemented for practical applications, using a simple measuring instrument since only absorption information at the limited absorption bands is required.

NIR robustness model of variable selection investigation of critical quality attributes coupled with different simulate noises by prediction capability and reproducibility

variable selection is critical to select characteristic variables of critical quality attributes to improve model performance and interpret the identified variables in multivariate calibration. However, classical variable selection methods were developed and optimized by the prediction error. It is rare for the robustness evaluation of variable selection methods. In this study, the robustness of four different variable selection methods was investigated by adding different types of simulate noises to validation set and calibration and validation sets, respectively. The reproducibility as well as root mean squared error of prediction (RMSEP) were used together as common measure in assessing the robustness of different variable selection methods. The robustness of four variable selection methods method was investigated using two near infrared (NIR) datasets including open-source dataset of corn and Chinese herbal medicine (CHM) dataset. The result illustrated that variable importance in projection (VIP) was substantially more robust to additive noise, with smaller RMSEP value and high reproducibility. This provides a novel strategy for the reliability evaluation of variable selection methods in NIR model of critical quality attributes.

Determination of Rare Earth Elements in Geological Samples Using Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) was used to detect rare earth elements (REEs) in natural geological samples. Low and high intensity emission lines of Ce, La, Nd, Y, Pr, Sm, Eu, Gd, and Dy were identified in the spectra recorded from the samples to claim the presence of these REEs. Multivariate analysis was executed by developing partial least squares regression (PLS-R) models for the quantification of Ce, La, and Nd. Analysis of unknown samples indicated that the prediction results of these samples were found comparable to those obtained by inductively coupled plasma mass spectrometry analysis. Data support that LIBS has potential to quantify REEs in geological minerals/ores.

Analysis of charcoal blast furnace slags by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was used for the analysis of charcoal blast furnace slags. Plasma was generated by an application of a 1064 nm wavelength Nd:YAG laser beam to the surface of pellets created from the slags. The presence of Al, Ca, Fe, K, Mg, Mn, and Si was determined by identifying their characteristic spectral signatures. Multivariate analysis was performed for the quantification of these elements. The predicted LIBS results were found in agreement with the inductively coupled plasma optical emission spectrometry analysis. The limit of detection for Al, Ca, Fe, K, Mg, Mn, and Si was calculated to be 0.10%, 0.22%, 0.02%, 0.01%, 0.01%, 0.005%, and 0.18%, respectively.

Univariate and multivariate analyses of rare earth elements by laser-induced breakdown spectroscopy

Univariate and multivariate analyses of six rare earth elements [cerium (Ce), europium (Eu), gadolinium (Gd), neodymium (Nd), samarium (Sm), and yttrium (Y)] have been performed using data from laser-induced breakdown spectroscopy (LIBS). Binary mixtures of oxide forms of each rare earth element in an Al₂O₃ matrix with their concentrations varying from 1% to 10% by weight in powder form were used as working samples for univariate analysis. For multivariate analysis, complex mixtures of oxides of all these six rare earth elements and Al₂O₃ in powder form, where the concentration of each element oxide was varied from 1% to 50% by weight one by one, were used to record LIBS spectra. Optimum values of gate delay, gate width, and laser energy were used to get spectra from these samples and spectra were used to develop calibration models. The limits of detection for Ce, Eu, Gd, Nd, Sm, and Y were calculated to be 0.098%, 0.052%, 0.077%, 0.047%, 0.250%, and 0.036%, respectively, from the calibration curves.

High-Speed Scanning for the Quantitative Evaluation of Glycogen Concentration in Bioethanol Feedstock Synechocystis sp. PCC6803 Using a Near-Infrared Hyperspectral Imaging System with a New Near-Infrared Spectral Camera

In the present study, the high-speed quantitative evaluation of glycogen concentration accumulated in bioethanol feedstock Synechocystis sp. PCC6803 was performed using a near-infrared (NIR) imaging system with a hyperspectral NIR spectral camera named Compovision. The NIR imaging system has a feature for high-speed and wide area monitoring and the two-dimensional scanning speed is almost 100 times faster than the general NIR imaging systems for the same pixel size. For the quantitative analysis of glycogen concentration, partial least squares regression (PLSR) and moving window PLSR (MWPLSR) were performed with the information of glycogen concentration measured by high performance liquid chromatography (HPLC) and the calibration curves for the concentration within the Synechocystis sp. PCC6803 cell were constructed. The results had high accuracy for the quantitative estimation of glycogen concentration as the best squared correlation coefficient R² was bigger than 0.99 and a root mean square error (RMSE) was less than 2.9%. The present results proved not only the potential for the applicability of NIR spectroscopy to the high-speed quantitative evaluation of glycogen concentration in the bioethanol feedstock but also the expansivity of the NIR imaging instrument to in-line or on-line product evaluation on a factory production line of bioethanol in the future.

A green method for the quantification of polysaccharides in Dendrobium officinale

We report a NIR spectroscopy method for the quantification of polysaccharides inDendrobium officinaleusing a PLS calibration model.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration

Wavelength selection is a critical step for producing better prediction performance when applied to spectral data. Considering the fact that the vibrational and rotational spectra have continuous features of spectral bands, we propose a novel method of wavelength interval selection based on random frog, called interval random frog (iRF). To obtain all the possible continuous intervals, spectra are first divided into intervals by moving window of a fix width over the whole spectra. These overlapping intervals are ranked applying random frog coupled with PLS and the optimal ones are chosen. This method has been applied to two near-infrared spectral datasets displaying higher efficiency in wavelength interval selection than others. The source code of iRF can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

Predictive-property-ranked variable reduction with final complexity adapted models in partial least squares modeling for multiple responses

For partial least-squares regression with one response (PLS1), many variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative variables. Many variable-reduction methods are based on various PLS-model-related parameters, called predictor-variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new predictor-variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future.

Determination of figures of merit for near-infrared and Raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

Process analytical technology has elevated the role of sensors in pharmaceutical manufacturing. Often the ideal technology must be selected from many suitable candidates based on limited data. Net analyte signal (NAS) theory provides an effective platform for method characterization based on multivariate figures of merit (FOM). The objective of this work was to demonstrate that these tools can be used to characterize the performance of 2 dissimilar analyzers based on different underlying spectroscopic principles for the analysis of pharmaceutical compacts. A fully balanced, 4-constituent mixture design composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and starch was generated; it consisted of 29 design points. Six 13-mm tablets were produced from each mixture at 5 compaction levels and were analyzed by near-infrared and Raman spectroscopy. Partial least squares regression and NAS analyses were performed for each component, which allowed for the computation of FOM. Based on the calibration error statistics, both instruments were capable of accurately modeling all constituents. The results of this work indicate that these statistical tools are a suitable platform for comparing dissimilar analyzers and illustrate the complexity of technology selection.

How to avoid over-fitting in multivariate calibration—The conventional validation approach and an alternative

This paper critically reviews the problem of over-fitting in multivariate calibration and the conventional validation-based approach to avoid it. It proposes a randomization test that enables one to assess the statistical significance of each component that enters the model. This alternative is compared with cross-validation and independent test set validation for the calibration of a near-infrared spectral data set using partial least squares (PLS) regression. The results indicate that the alternative approach is more objective, since, unlike the validation-based approach, it does not require the use of 'soft' decision rules. The alternative approach therefore appears to be a useful addition to the chemometrician's toolbox.

A comparative study between PCR and PLS in simultaneous spectrophotometric determination of diphenylamine, aniline, and phenol: Effect of wavelength selection

Partial least squares (PLS) and principal component regression (PCR) have received considerable attention in the chemometrics for multicomponent analysis where superiority of one over another is a challenging problem yet. Considering the effect of wavelength selection, a comparison was made between PCR and PLS methods by application those to simultaneous spectrophotometric determination of diphenylamine (DPA), a compound from the third European Union list of priority pollutants, and its environmentally related products aniline and phenol. The UV absorbance spectra of the methanolic solutions of the analytes were measured in the concentration ranges of 1.0-10.0 microg mL(-1) and then subjected to PCR and PLS. The models refinement procedure and validation was performed by cross-validation. A modified changeable size moving windows strategy, where optimized the intervals between the sensors in a selected windows, was also proposed to select the more informative spectral regions for each of the analytes. It was found that wavelength selection improved the quality of predictions for both regression methods whereas more reliable results were obtained by removing of the highly collinear neighboring wavelengths. The resultant data explained that PLS produced more or less better results when whole spectral data were used but in the case of selected wavelength regions both methods produced similar results and no comments could be given about the superiority of one against another. The major difference was obtaining the higher number of factors for PCR, which is not a significant problem.

PLS and first derivative of ratio spectra methods for determination of hydrochlorothiazide and propranolol hydrochloride in tablets

Two new analytical methods have been developed as convenient and useful alternatives for simultaneous determination of hydrochlorothiazide (HCT) and propranolol hydrochloride (PRO) in pharmaceutical formulations. The methods are based on the first derivative of ratio spectra (DRS) and on partial least squares (PLS) analysis of the ultraviolet absorption spectra of the samples in the 250-350-nm region. The methods were calibrated between 8.7 and 16.0 mg L(-1) for HCT and between 14.0 and 51.5 mg L(-1) for PRO. An asymmetric full-factorial design and wavelength selection (277-294 nm for HCT and 297-319 for PRO) were used for the PLS method and signal intensities at 276 and 322 nm were used in the DRS method for HCT and PRO, respectively. Performance characteristics of the analytical methods were evaluated by use of validation samples and both methods showed to be accurate and precise, furnishing near quantitative analyte recoveries (100.4 and 99.3% for HCT and PRO by use of PLS) and relative standard deviations below 2%. For PLS the lower limits of quantification were 0.37 and 0.66 mg L(-1) for HCT and PRO, respectively, whereas for DRS they were 1.15 and 3.05 mg L(-1) for HCT and PRO, respectively. The methods were used for quantification of HCT and PRO in synthetic mixtures and in two commercial tablet preparations containing different proportions of the analytes. The results of the drug content assay and the tablet dissolution test were in statistical agreement (p < 0.05) with those furnished by the official procedures of the USP 29. Preparation of dissolution profiles of the combined tablet formulations was also performed with the aid of the proposed methods. The methods are easy to apply, use relatively simple equipment, require minimum sample pre-treatment, enable high sample throughput, and generate less solvent waste than other procedures.

Application of a chemometric method for simultaneous determination of acetaminophen and diclofenac in content-uniformity and drug-dissolution studies

A new, repeatable, and rapid method has been developed for resolution of binary mixtures of acetaminophen and diclofenac with minimum sample pretreatment and without separation of the analytes. The method, based on the PLS1 processing of absorbance data in the UV region, was successfully used for quantification of the drug content of three tablet preparations. The results obtained were in good agreement with HPLC recovery data. The method also enabled determination of drug-dissolution profiles of these commercial tablets, by simultaneous determination of both analytes during the dissolution test.

Detector for particulate polycyclic aromatic hydrocarbons in water

It is estimated that most polycyclic aromatic hydrocarbons (PAHs) in environmental water are not dissolved but rather in particulate form. Nevertheless, the currently available optical detectors are not suited for proper sampling of solid PAHs. A new setup for direct sampling and quantification of suspended particulate PAHs in water is suggested. It is based on a polymeric film that has the capability of dissolving PAH particulates, coupled to a traditional laser-induced fluorescence probe. Kinetics and performance of two sampling modes have been studied: bulk sampling, by immersing the probe into the water, and surface sampling, by laying the film on the water surface. The latter method has proved to be more sensitive; however, it is diffusion-limited. Linear calibration plots have provided quantification over a wide concentration range with detection limits in the ppb range (these could be improved by using a modified probe). The effects due to other particulates in water have been studied and only little interferences have been observed. The possibility of analysis of PAH mixtures has been addressed and it has been concluded that multivariate analysis is needed.

Chemometric determination of amiloride hydrochloride, atenolol, hydrochlorothiazide and timolol maleate in synthetic mixtures and pharmaceutical formulations

Different chemometric methods such as classical least squares (CLS), principal components regression (PCR) and partial least squares with one dependent variable (PLS-1) applied on UV spectral data (0 D) and on their first derivatives (1 D) were evaluated for the simultaneous quantification of samples containing mixtures of amiloride hydrochloride, atenolol, hydrochlorothiazide and timolol maleate. Their performances were compared by means of ANOVA tests, which evidenced that 0 D-PCR, 0D-PLS-1, 1D-PCR, 1D-PLS-1, were reproducible and gave statistically similar results, while 0 D-CLS and 1D-CLS displayed higher variances than the former and failed to comply with the Levene's variance homogeneity test at different stages of the method comparison and validation process. The four statistically equivalent procedures were successfully applied to the analysis of synthetic samples with two to four analytes and to commercial tablet preparations containing amiloride hydrochloride and hydrochlorothiazide alone or in association with atenolol or timolol maleate.

Simultaneous Spectrophotometric Determination of p-Benzoquinone and Chloranil after Microcrystalline Naphthalene Extraction by Using Genetic Algorithm-Based Wavelength Selection-Partial Least Squares Regression

Microcrystalline naphthalene extraction has been used for the preconcentration of p-benzoquinone and tetrachloro-p-benzoquinone (chloranil), after their reaction by aniline, and later simultaneous spectrophotometric analysis by genetic algorithm-partial least squares (GA-PLS) calibration. The chemical variables affecting the analytical performance of the methodology were studied and optimized. Under the optimum conditions i.e., [aniline] = 0.05 M and [naphthalene] = 2.2% (w/v), preconcentration of 25 ml of sample solution permitted the detection of 0.32 and 0.23 microg ml(-1) for p-benzoquinone and chloranil, respectively. The predictive abilities of partial least squares regression (PLS) and genetic algorithm-partial least squares regression (GA-PLS) were examined for simultaneous determination of two quinones. The GA-PLS shows superiority over other PLS methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity, provides useful information about the chemical system.

Simultaneous determination of amiloride hydrochloride and hydrochlorothiazide in synthetic samples and pharmaceutical formulations by multivariate analysis of spectrophotometric data

The use of multivariate spectrophotometric calibration for the simultaneous analysis of synthetic samples and commercial tablet preparations containing hydrochlorothiazide (HCT) and amiloride hydrochloride (AMH) is reported. Partial least squares (PLS-1) analysis of electronic absorption spectral data allowed the rapid and accurate resolution of mixtures in which the analyte ratios were approximately 10:1, without the need of a previous separation step and without interference from other sample constituents. The method, validated by the analysis of synthetic mixtures of both drugs, where accuracy over the linear working range as well as inter- and intra-assay precision were determined, was used in the concentration ranges of 21.7-30.4 mg l(-1) for HCT and 1.8-3.0 mg l(-1) for AMH. The proposed method was successfully applied to the evaluation of the stability of the stock solutions of the analytes in MeOH-H(2)O and to the elaboration of drug dissolution profiles of commercial tablets, results being concordant with those furnished by the USP technique. The method was also employed for the determination of drug content in two different pharmaceutical formulations, providing results that were in excellent agreement with those obtained by HPLC.

Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data

A new wavelength interval selection procedure, moving window partial least-squares regression (MWPLSR), is proposed for multicomponent spectral analysis. This procedure builds a series of PLS models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of PLS models reaching a desired error level. Based on a proposed theory demonstrating the necessity of wavelength selection, it is shown that MWPLSR provides a viable approach to eliminate the extra variability generated by non-composition-related factors such as the perturbations in experimental conditions and physical properties of samples. A salient advantage of MWPLSR is that the calibration model is very stable against the interference from non-composition-related factors. Moreover, the selection of spectral intervals in terms of the least model complexity enables the reduction of the size of a calibration sample set in calibration modeling. Two strategies are suggested for coupling the MWPLSR procedure with PLS for multicomponent spectral analysis: One is the inclusion of all selected intervals to develop a PLS calibration model, and the other is the combination of the PLS models built separately in each interval. The combination of multiple PLS models offers a novel potential tool for improving the performance of individual models. The proposed procedures are evaluated using two open-path Fourier transform infrared data sets and one near-infrared data set, each having different noise characteristics. The results reveal that the proposed procedures are very promising for vibrational spectroscopy-based multicomponent analyses and give much better prediction than the full-spectrum PLS modeling.

A spectrophotometric-partial least squares (PLS-1) method for the simultaneous determination of furosemide and amiloride hydrochloride in pharmaceutical formulations

A numerical method, based on the use of spectrophotometric data coupled to PLS-1 multivariate calibration, is reported for the simultaneous determination of furosemide and amiloride hydrochloride in synthetic samples and commercial tablets. The method was applied in the concentration ranges of 8.0-13.0 mg l(-1) for furosemide and 1.0-1.6 mg l(-1) for amiloride hydrochloride. Its accuracy and precision were determined, and it was validated by the analysis of synthetic mixtures of both drugs. The method was successfully applied to the quantitation of furosemide and amiloride hydrochloride in three different pharmaceutical formulations, providing results in agreement with those obtained by HPLC. It allowed the rapid, accurate and precise simultaneous estimation of the concentration of both analytes of interest in spite of their important spectral overlap, high concentration relationship and the presence of small amounts of different, unmodelled, absorbing excipients.