First Derivative Spectra Research Articles

Application of GA-RBF networks to the nondestructive determination of active component in pharmaceutical powder by NIR spectroscopy

A new method was applied to nondestructive quantitative analysis of pharmaceutical samples with different concentrations on the basis of the near-infrared spectral data. By the proposed method powerful radial basis function (RBF) networks can be produced based on a genetic algorithm (GA), which is applied to auto-configuring the structure of the networks to optimize the near-infrared wavelength regions used and variables employed in building radial basis function networks. Estimation and calibration of the sample concentration via NIR spectroscopy were made with the aid of genetic algorithm-radial basis function (GA-RBF) network models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra, various optimum models of which were established and compared. Experiment results show that GA-RBF networks can give robust and satisfactory prediction and the GA-RBF model based on SNV preprocessing spectra was found to provide the best performance. Therefore, the proposed method may have significant potential for use in nondestructive quantitative analysis of pharmaceutical samples.

Rapid and accurate determination of VFAs and ethanol in the effluent of an anaerobic H 2-producing bioreactor using near-infrared spectroscopy

A rapid quantification method, based on Fourier transform near-infrared (FT-NIR) spectroscopy, was developed and validated to nondestructively quantify the concentrations of volatile fatty acids and ethanol in the effluent of an anaerobic H 2-producing bioreactor. The first-derivative spectra calculated by a simple numerical difference, combined with the orthogonal signal correction method, were used as spectral preprocessing options. A calibration model was established and validated using gas chromatography measurement results. The number of internal latent variables was optimized based on the lowest root-mean-square error of calibration. The calibration model established shows the satisfactory results for the lowest root-mean-square errors of prediction compared to other preprocess methods. The method developed in this work is demonstrated to be more flexible compared with other approaches to determine the concentrations of volatile fatty acids and ethanol in the anaerobic reactor effluents.

X-ray Absorption Near Edge Structure Study of Lead Sorption on Phosphate-Treated Kaolinite

Presorbed phosphate significantly increases Pb sorption on the phyllosilicate kaolinite in the pH range from 4to 8. The sorbed Pb-to-P molar ratios over this pH range stray little from the molar ratio found in the mineral pyromorphite, suggesting sorbed phosphate reacts with soluble Pb to form a surface precipitate similar to pyromorphite. X-ray absorption near edge structure (XANES) studies at the Pb L3-edge support this interpretation. In particular, the fine structure of first-derivative Pb L3-edge XANES spectra of Pb species sorbed to phosphate-treated kaolinite samples covering a pH range extending from 4 to 10 match the fine structure of the spectrum of pyromorphite. Although the productis structurally and compositionally similarto pyromorphite, the ion activity product in the pH range 4-6 was undersaturated with respect to the solubility product of pyromorphite.

Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China

Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.

Determination of acetolactate synthase activity and protein content of oilseed rape ( Brassica napus L.) leaves using visible/near-infrared spectroscopy

A new acetolactate synthase (ALS)-inhibiting herbicide, propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273), was applied to oilseed rape ( Brassica napus L.) leaves in different leaf positions. Visible/near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of ALS activity and protein content in rapeseed leaves. Partial least squares (PLS) analysis was the calibration method with comparison of different spectral preprocessing by Savitzky–Golay (SG) smoothing, standard normal variate (SNV), first and second derivative. The best PLS models were obtained by first-derivative spectra for ALS, whereas original spectra for soluble, non-soluble and total protein contents. Simultaneously, certain latent variables (LVs) were used as the inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models. All LS-SVM models outperformed PLS models and BPNN models. The correlation coefficient ( r), root mean square error of prediction (RMSEP) and bias in validation set by LS-SVM were 0.998, 0.715 and 0.079 for ALS, 0.999, 33.084 and 1.178 for soluble protein, 0.997, 42.773 and 6.244 for non-soluble protein, 0.999, 59.562 and 7.437 for total protein, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the determination of ALS activity and protein content of rapeseed leaves. The results would be helpful for further on field analysis of using Vis/NIR spectroscopy to monitor the growing status and physiological properties of oilseed rape.

Screening of humic and fulvic acids in estuarine sediments by near-infrared spectrometry

Diffuse reflectance near-infrared spectroscopy (NIR) combined with partial least squares (PLS) data treatment has been employed for the rapid and nondestructive determination of sedimentary humic substances. Forty one samples of surface estuarine sediments, taken during distinct seasonal periods from different locations across Ria de Arousa (northwest of Spain), were scanned at wavelengths from 833 to 2,976 nm (12,000 to 3,360 cm(-1)). Twenty four samples were randomly selected, from previous hierarchical cluster analysis of their NIR spectra, for the calibration set, and the 17 remaining samples were assigned to the validation set. NIR spectra of calibration samples were correlated to measured values of humic acids (HAs) and fulvic acids (FAs), which ranged from 1.53 to 28.17 mg/g and from 0.37 to 2.45 mg/g, respectively, using PLS regression and multiplicative scattering correction on the raw and first-derivative NIR spectra, respectively. Low root mean square error of prediction values of 4.3 mg HA/g sediment and 0.25 mg FA/g sediment were obtained. Good residual prediction deviation values of 1.16 and 1.2 were obtained for HA and FA, respectively, allowing the PLS models built to be considered as appropriate tools for screening purposes.

Simultaneous determination of Fe(III) and Al(III) by first-derivative spectrophotometry and partial least-squares (PLS-2) method – Application to post-haemodialysis fluids

Derivative spectrophotometry (graphical method) and partial least-squares regression (numerical method) methods were developed for the spectrophotometric multi-component analysis of post-haemodialysis fluids and synthetic mixtures containing Al(III) and Fe(III) without any chemical separation. The complexes of these metal ions with chrome azurol S were formed immediately at pH 5.5 and were stable for at least 3 h. The graphical method is based on the use of first-derivative spectra for evaluation because working wavelength determination was more precise and spectral overlap was less than in the ordinary spectra. Two wavelengths at which the complexes exhibited maximum absorption values for Fe(III) and Al(III) were selected as analytical wavelengths, i.e., 675 and 623.5 nm, respectively. Lambert–Beer's law is obeyed between 0.0896–8.064 μg/mL Fe(III) and 0.054–0.486 μg/mL Al(III). Limits of detection for Fe(III) and Al(III) were 0.056 and 0.044 μg/mL, respectively. The reproducibility, expressed as variation coefficients, for two sets of 10 standard mixtures containing 3.584 μg/mL Fe(III) and 0.27 μg/mL Al(III) were 1.9% and 2% for iron and aluminium, respectively. In the numerical method, a training set was randomly prepared by using 14 samples. The concentration of each component has been varied in the linear range of the analytical signal. The spectral regions between 510 and 720 nm were selected for the analysis of the binary mixture of Fe(III)/Al(III). The proposed methods were validated by using synthetic binary mixtures and applied to the simultaneous determination of Fe(III) and Al(III) in post-haemodialysis samples. The obtained results were compared with each other; in general, both multi-component methods gave rise to similar recovery results for laboratory-prepared mixtures and real samples.

Sensitive detection of weak absorption signals in photoacoustic spectroscopy by using derivative spectroscopy and wavelet transform

This report presents a practical analytical method of photoacoustic (PA) spectroscopy that is based on wavelet transform (WT) and the first-derivative PA spectrum. An experimental setup is specially designed to obtain the first-derivative spectrum, which aims to identify some unnoticeable absorption peaks in the normal PA spectrum. To enhance the detectability of overlapping spectral bands, the WT is used to decompose the PA spectrum signals into a series of localized contributions (details and approximation) on the basis of the frequency. For the decomposed contributions do not change the absorption peak position of PA spectrum, one can retrieve the weak absorption signals by the decomposed result of WT. Because of the use of derivative spectroscopy and WT, three unnoticeable absorption peaks that are hidden in the PA spectrum of carbon absorption are precisely retrieved, the wavelengths of which are 699.7, 752.7, and 775.5nm, respectively. This analytical method, which has the virtue of using a physical method and using a computer software method, can achieve great sensitivity and accuracy for PA spectral analysis.

Radial basis function networks combined with genetic algorithm applied to nondestructive determination of compound erythromycin ethylsuccinate powder

A new assay method for the nondestructive determination of pharmaceutical samples with different concentrations on the basis of the near-infrared (NIR) spectral data is presented in this paper. By the proposed method, powerful radial basis function (RBF) networks can be produced based on a genetic algorithm (GA), which is applied for auto-configuring the structure of the networks and obtaining the optimal network parameters. The Akaike's information criterion (AIC) is used to evaluate the fitness of individual networks. Therefore, the genetic algorithm-radial basis function (GA-RBF) networks have a better generalization performance and simpler network structure. Four different GA-RBF network models based on pretreated spectra (multiplicative scatter correction MSC, standard normal variate SNV, first-derivative and second-derivative spectra) have been established and compared. The obtained GA-RBF networks can give robust and satisfactory prediction and the optimal GA-RBF networks after the SNV treatment is found to provide the best results. It is demonstrated that the proposed GA-RBF method based on NIR spectral data is a valuable tool for quantitative analysis.

Nondestructive quantitative analysis of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy combined with radial basis function neural networks

A new assay method for the nondestructive determination of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy (NIR) combined with radial basis function (RBF) neural networks is investigated. The modern near-infrared spectroscopy analysis technique is efficient, simple and nondestructive, which has been used in chemical analysis in diverse fields. Short-wave NIR is a more rapid, flexible, and cost-effective method to control product concentration in pharmaceutical industry. The RBF neural networks are local approximation networks that have superiorities in function approximation and learning speed. In addition, the structure of RBF networks is simple. Estimate and calibration of the sample concentration via short-wave NIR are made with the aid of RBF models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra. Various optimum models of them are established and compared. Experiment results show that the models of SNV spectra can give better performance, and the optimized RBF neural network model after SNV treatment were given, by which the root-mean-square-errors (RMSE) for calibration set and test set were 0.3266% and 0.5244%, respectively and the correlation coefficients ( R) for calibration set and test set were 0.9942 and 0.9852, respectively. The proposed RBF method based on short-wave NIR is more valuable and economical for quantitative analysis than traditional methods such as partial least squares (PLS).

Application of Sample—Sample Two-Dimensional Correlation Spectroscopy to Determine the Glass Transition Temperature of Poly(Ethylene Terephthalate) Thin Films

The glass transition temperatures (Tg) of poly(ethylene terephthalate) (PET) thin films with different thicknesses are determined by analyzing their in situ reflection-absorption infrared (RAIR) spectra measured over a temperature range of 28 to 84 degrees C. The criterion of standard deviation of the covariance matrices is used as a graphical indicator for the determination of the Tg present in the sample-sample two-dimensional (2D) correlation spectra calculated from the temperature-dependent RAIR spectra. After two data pretreatments of the first derivative of the spectral absorbance versus temperature and the mean normalization over the wavenumbers are sequentially carried out on the RAIR spectra, an abrupt change of the first-derivative correlation spectra with respect to temperature is quickly obtained. It reflects the temperature at which the apparent intensity changes in pertinent absorption bands of PET thin films take place due to the dramatic segmental motion of PET chain conformation. The Tg of the thin PET films is accordingly determined. The results reveal that it decreases with a great dependence on the film thickness and that sample-sample 2D correlation spectroscopy enables one to determine the transition temperature of polymer thin films in an easy and valid way.

Direct determination of U(VI) in TBP-kerosene extracts using first derivative ultraviolet spectroscopy

The extractive first order derivative spectrophotometry is a selective method for the separation and determination of U(VI) using tri-n-butyl phosphate (TBP), which combines the roles of solvent and complexing agent. The complex is formed by extracting U(VI) from an aqueous 6M sodium nitrate solution at initial pH 3.0 into a 25% solution of TBP in kerosene. This extraction also separates U(VI) from many diverse ions that interfere. After extraction, the determination of uranium shows good accuracy and precision with relative standard deviation of 1.5% (n = 5) at 20 ppm using zero-order spectrum at lmax = 250 nm. Calibration curve was also found to obey Beer's law in the range of 10-100 ppm with 3.33 ppm detection limit. However, these accuracy and precision have been improved to give relative standard deviation of 0.7% (n = 5) at 20 ppm with a lower detection limit of 2.24 ppm using the first-derivative spectrum at l = 263 nm comparing to the normal one.

FTIR and Raman microspectroscopy of normal, benign, and malignant formalin-fixed ovarian tissues

Ovarian cancer is the sixth most common cancer among women worldwide, and mortality rates from this cancer are higher than for other gynecological cancers. This is attributed to a lack of reliable screening methods and the inadequacy of treatment modalities for the advanced stages of the disease. FTIR and Raman spectroscopic studies of formalin-fixed normal, benign, and malignant ovarian tissues have been undertaken in order to investigate and attempt to understand the underlying biochemical changes associated with the disease, and to explore the feasibility of discriminating between these different tissue types. Raman spectra of normal tissues indicate the dominance of proteins and lower contents of DNA and lipids compared to malignant tissues. Among the pathological tissues studied, spectra from benign tissues seem to contain more proteins and less DNA and lipids compared to malignant tissue spectra. FTIR studies corroborate these findings. FTIR and Raman spectra of both normal and benign tissues showed more similarities than those of malignant tissues. Cluster analysis of first-derivative Raman spectra in the 700-1700 cm(-1) range gave two clear groups, one corresponding to malignant and the other to normal+benign tissues. At a lower heterogeneity level, the normal+benign cluster gave three nonoverlapping subclusters, one corresponding to normal and two for benign tissues. Cluster analysis of second-derivative FTIR spectra in the combined spectral regions of 1540-1680 and 1720-1780 cm(-1) resulted into two clear clusters corresponding to malignant and normal+benign tissues. The cluster corresponding to normal+benign tissues produced nonoverlapping subclusters for normal and benign tissues at a lower heterogeneity level. The findings of this study demonstrate the feasibility of Raman and FTIR microspectroscopic discrimination of formalin-fixed normal, benign, and malignant ovarian tissues.

Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy

The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.

A General Computational Method for Converting Normal Spectra into Derivative Spectra

The mathematical problem of converting a normal spectrum into the corresponding first- and second-derivative spectra is formulated as an integral equation of the first kind. Tikhonov regularization is then applied to solve the spectral conversion problem. The end result is a set of linear algebraic equations that takes in as input the original spectrum and produces as output the second-derivative spectrum, which is then integrated to yield the first-derivative spectrum. Noise amplification is kept under control by adjusting the regularization parameter (guided by generalized cross-validation) in the algebraic equations. The performance of this procedure is demonstrated by applying it to different types of spectral data taken from the literature.

Simultaneous Determination of Food Dyes by First Derivative Spectrophotometry with Sorption onto Polyurethane Foam

A simple spectrophotometric method is described for resolving binary mixtures of some food dyes: Amaranth, Brilliant Blue, Sunset Yellow and Tartrazine, using the first-derivative spectra with measurements at zero-crossing wavelengths. Analytical curves are linear up to 20 mg L(-1). Standard deviations of 1.30, 2.22, 1.93 and 0.81% were obtained for synthetic binary mixtures of 2 mg L(-1) of Amaranth, Brilliant Blue, Sunset Yellow and Tartrazine, respectively. Before the spectrophotometric measurements, the dyes were sorbed onto polyurethane foam and recovered in sodium dodecyl benzene sulfonate solution. Therefore, matrix complexity was eliminated and simple spectra were obtained. The method was very satisfactorily used for determining the colorants in synthetic mixtures, with recoveries in the 96 - 101% range. Detection limit values were dependent on the colorant combination investigated. Commercial products containing binary combinations of these dyes in different ratios (from 1:1 to 1:8) were analyzed. The results were compared with those obtained by HPLC; very similar values were found by the two methods.

Quantum-Chemical Investigation of the Electroabsorption Spectra of Directly Meso−Meso-Linked Porphyrin Arrays: Essential Role of Charge-Transfer Excited States Accidentally Overlapping with Soret Bands

The electroabsorption (EA) spectra of directly meso-meso-linked porphyrin arrays (Zn, n = 1-3) have been investigated by means of the sum-over-states (SOS) approach at the INDO/S-SCI level theory. The experimental EA spectra of Zn (n > or = 2) exhibit an unusual second-derivative line shape at the exciton split low-energy B(x) band in contrast to the first-derivative spectrum of Z1, which is readily ascribed to a quadratic Stark shift of the B (Soret) band. Although the second-derivative line shape is usually attributed to a difference in the permanent dipole moment (Deltamu) between the ground and excited states, it should be vanishing for Zn due to their essentially D(2)(d) or D(2)(h) symmetry. As pointed out in our previous studies, the interporphyrinic charge-transfer (CT) excited states are accidentally overlapping with the excitonic B bands and the present calculations reveal that the B(x) state is strongly coupled via a transition dipole moment with two such CT states. These situations give rise to a quadratic Stark effect on the B(x) band that is intermediate between Stark shift (first derivative) and Stark broadening (second derivative), and play a central role in establishing the anomalous second derivative nature of the EA spectrum. Moreover, based on the comparison between the theoretical and experimental spectra, there must be an additional factor that further enhances the second derivative nature of the EA spectrum of porphyrin arrays. Discussions on this issue including the preliminary investigations on the role of solvent (PMMA)-induced asymmetry are also presented.

Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy

Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.

Line-shape study of water vapour by tunable diode laser spectrometer in the 822?832�nm wavelength region

A near-infrared tunable diode laser absorption spectrometer is set up to measure the air-induced broadening coefficients and the line-strength parameters of water-vapour overtone transitions within the (2,1,1)←(0,0,0) band in the 822–832 nm wavelength region. A Hitachi HL8311 E double hetero-junction structure diode laser is used as a probe. The diode laser controller is home-built and stable within ±10 μA and ±10 mK, respectively. The laser-head mount has a simple design and provides easy access whenever changing of the laser head is required. The diode laser emission wavelength is thermally tuned between 50 °C and 12 °C. Thermal tuning of the diode laser emission wavelength is used to reveal the mode structure of the diode laser and to probe the overtone-band transitions of water vapour within its operating wavelength range. Current tuning of the diode laser is used at a fixed laser temperature to study the transitions one at a time. A balanced detector is used to improve the S/N ratio of the spectrum. A phase sensitive detection technique is followed to obtain the first-derivative spectra of the overtone transitions. The first-derivative spectra have been recorded at different air pressures inside the sample cell while the water-vapour pressure is kept fixed. The first-derivative spectrum is numerically integrated to obtain the original line shape. The original line shape is fitted with a Voigt profile by using a nonlinear least-squares fit program to extract the air-broadening coefficient and the line-strength parameter. The data obtained in our work is compared with the results of the HITRAN database.

Characterization of hydrogenated diamond-like carbon films electrochemically deposited on a silicon substrate

Diamond-like carbon (DLC) films were deposited on a Si substrate by electrolysis in a methanol solution at ambient pressure and low temperature. The morphology and microstructure of the resulting DLC films were analysed using atomic force microscopy, Raman spectroscopy, Fourier transformation infrared spectrometry, x-ray photoelectron spectroscopy (XPS), and x-ray excited Auger electron spectroscopy (XAES). The surface energy and mechanical properties of the DLC films were examined, and the growth mechanism of the DLC films in liquid phase electro-deposition is discussed as well. The results of the study show that the hydrogenated diamond-like carbon films are smooth and compact. The percentage of sp3 carbon in the DLC films is determined as 55–60%, based on the corresponding XPS and first-derivative XAES spectra of graphite, diamond, and the tested films. The DLC films show low surface free energy, good mechanical properties, excellent friction–reduction and wear-resistance. It is suggested that methanol dissociates to generate the active species of and C2H4 at high voltage applied to the electrode, followed by the generation of the alkyl chain [–CH2–CH2–]n whose C–C and C–H bond lengths and C–C–C and H–C–H bond angles are close to that of diamond. Subsequently, a diamond-like structure was formed by the ordered dehydrogenation of a short-chain [–CH2–CH2–]n in the electrolysis process.