Multivariate spectrofluorimetry of ultra trace zirconium(IV) and hafnium(IV) assisted by several chemometrics methods

In the presence of sodium lauryl sulfate (SLS), an anionic surfactant, highly sensitive molecular fluorescence reactions occur between Zr(IV), Hf(IV) ions and quercetin (3, 3', 4', 5, 7-pentahydroxyflavone) in acidic medium to form stable ternary micellar ion-association complexes. Their lambda(ex(max))/lambda(em(max)) values are 435.0/497.9 and 435.0/492.0 nm, respectively, indicating their heavily overlapping fluorescence excitation and emission spectra. The linear ranges of their regression calibration curves are 0 to 0.20 and 0 to 0.12 mg l(-1), respectively, with 0.5 ng ml(-1) for all of sensitivities. The multivariate spectrofluorimetry of ultra trace or trace Zr(IV) and Hf(IV) without separation was performed using a partial least squares (PLS) algorithm and other algorithms. The optimum PLS computation conditions are wavelength point number of 21 and corresponding wavelength range from 450 to 540 nm oriented from lambda(em) 495 nm to two sides at combined intervals of 2.5 and 5.0 nm at a fixed lambda(ex)of 435.0 nm with an optimum calibration sample number of 12, and respective optimum abstracted factor numbers of 4 and 5. It was concluded that accuracy and precision of the determination results obtained for simulation and real soil samples were poor for LP-M, LP-P, OLS-P, KF-P, and TFA-P algorithms, moderate for OLS-M, KF-M, and TFA-M algorithms, and good for PLS algorithm.     

Determination of Riboflavin in Human Plasma by Excitation‐Emission Matrix Fluorescence and Multi‐Way Analysis

Direct determination of riboflavin (Fig. 1), a vitamin, in human plasma was accomplished based on excitation‐emission matrix (EEM) fluorescence measurements and multi‐way chemometrics method based on parallel factor analysis (PARAFAC). The PARAFAC trilinear model, without restrictions and using one factor was used in the data analysis. The excitation wavelength range was from 380 to 460 nm and the emission was recorded from 480 to 600 nm. The calibration set was constructed with sixteen standard solutions in a concentration range of 0.02–0.38 μg mL^?1 for riboflavin. The capabilities of the method for the analysis were evaluated by determination of riboflavin in synthetic and real samples with satisfactory results. The accuracy of the methods, evaluated through the root mean square error of prediction (RMSEP), was 0.0059 for riboflavin by the PARAFAC model. Also, partial least squares (PLS) model was built at one excitation wavelength and used to determine a set of synthetic and real samples. The best model was obtained with PARAFAC. This result shows that molecular fluorescence spectroscopy can be used for the development of robust analytical methods for the direct determination of riboflavin in complex backgrounds such as human plasma.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Characterization of the autofluorescence of polymorphonuclear leukocytes, mononuclear leukocytes and cervical epithelial cancer cells for improved spectroscopic discrimination of inflammation from dysplasia

Fluorescence spectroscopy has the potential to improve the in vivo detection of intraepithelial neoplasias; however, the presence of inflammation can sometimes result in misclassifications. Inflammation is a common and important pathologic condition of epithelial tissues that can exist alone or in combination with neoplasia. It has not only been associated with the presence of cancer but also with the initiation of cancer by damage induced due to the oxidative activity of inflammatory cells. Microscopic examination of cervical biopsies has shown increased numbers of polymorphonuclear and mononuclear leukocytes in inflamed tissues mostly confined to the stroma. The purpose of this study was to characterize the fluorescence properties of human polymorpho- and mononuclear leukocytes and compare their fluorescence to that of cervical cancer cells. Human neutrophils were purified from peripheral blood and their fluorescence characterized over an excitation range of 250-550 nm. There are four notable excitation emission maxima: the tryptophan peak at 290 nm excitation, 330 nm emission; the NAD(P)H peak at 350 nm excitation, 450 nm emission, the FAD peak at 450 nm excitation, 530 nm emission and an unidentified peak at 500 nm excitation, 530 nm emission. Treatment of these peripheral blood neutrophils with 40 nM phorbol myristate acetate or with the chemotactic peptide formyl-Met-Leu Phe (1 M) demonstrated a significant increase in NAD(P)H fluorescence. Isolated mononuclear cells have similar emission peaks for tryptophan and NAD(P)H and a small broad peak at 450 nm excitation, 530 nm emission suggestive of FAD. Comparison of the fluorescence from leukocytes to epithelial cancer cell fluorescence has demonstrated the presence of these fluorophores in different quantities per cell. The most notable difference is the high level of tryptophan in cervical epithelial cancer cells, thus offering the potential for discrimination of inflammation.     

Studies on Simultaneous Fluorescence-Spectrophotometric Determination of Ultratrace Niobium(V), Tantalum(V), and Zirconium(IV) Using Partial Least-Squares Algorithm

In the presence of cetyltrimethylammonium bromide, a cationic surfactant, highly sensitive molecular fluorescence reactions occur between Nb(V), Ta(V), and Zr(IV) ions and morin (3, 5, 7, 2′, 4′-pentahydroxyflavone) in acidic medium to form stable ternary micellar complexes. Their λ_ex(max)/λ_em(max)values are 421.0/492.2, 416.2/489.6, and 424.2/507.8 nm, respectively, and their λ_em(max)values are 490.5, 488.6, and 507.2 nm, respectively, at the same fixed λ_exof 420.5 nm, indicating their seriously overlapping fluorescence excitation spectra and fluorescence emission spectra. The linear ranges of their regression calibration curves are 0 to 0.20, 0 to 0.50, and 0 to 0.20 mg/liter, respectively, with 0.5 ng/ml for all of sensitivities. The simultaneous molecular fluorescence-spectrophotometric determination of ultratrace or trace Nb(V), Ta(V), and Zr(IV) without separation was made using a partial least-squares (PLS) algorithm and other algorithms. The optimum PLS computation conditions are wavelength point number of 25 and corresponding wavelength range from 450 to 550 nm oriented from λ_em500 nm to two sides at combined intervals of 2.5 and 5.0 nm at a fixed λ_exof 420.5 nm with an optimum calibration sample number of 14 and respective optimum abstracted factor numbers of 6, 4, and 3. With respect to both accuracy and precision of the obtained results, the PLS algorithm is superior to the ordinary least-squares algorithm.     

Optimal excitation wavelengths for in vivo detection of oral neoplasia using fluorescence spectroscopy

There is no satisfactory mechanism to detect premalignant lesions in the upper aero-digestive tract. Fluorescence spectroscopy has potential to bridge the gap between clinical examination and invasive biopsy; however, optimal excitation wavelengths have not yet been determined. The goals of this study were to determine optimal excitation-emission wavelength combinations to discriminate normal and precancerous/cancerous tissue, and estimate the performance of algorithms based on fluorescence. Fluorescence excitation-emission matrices (EEM) were measured in vivo from 62 sites in nine normal volunteers and 11 patients with a known or suspected premalignant or malignant oral cavity lesion. Using these data as a training set, algorithms were developed based on combinations of emission spectra at various excitation wavelengths to determine which excitation wavelengths contained the most diagnostic information. A second validation set of fluorescence EEM was measured in vivo from 281 sites in 56 normal volunteers and three patients with a known or suspected premalignant or malignant oral cavity lesion. Algorithms developed in the training set were applied without change to data from the validation set to obtain an unbiased estimate of algorithm performance. Optimal excitation wavelengths for detection of oral neoplasia were 350, 380 and 400 nm. Using only a single emission wavelength of 472 nm, and 350 and 400 nm excitation, algorithm performance in the training set was 90% sensitivity and 88% specificity and in the validation set was 100% sensitivity, 98% specificity. These results suggest that fluorescence spectroscopy can provide a simple, objective tool to improve in vivo identification of oral cavity neoplasia.     

Multi-class classification algorithm for optical diagnosis of oral cancer

We report development of a direct multi-class spectroscopic diagnostic algorithm for discrimination of high-grade cancerous tissue sites from low-grade as well as precancerous and normal squamous tissue sites of human oral cavity. The algorithm was developed making use of the recently formulated theory of total principal component regression (TPCR). The in vivo autofluorescence spectral data acquired from patients screened for neoplasm of oral cavity at the Government Cancer Hospital, Indore, was used to train and validate the algorithm. The diagnostic algorithm based on TPCR was found to provide satisfactory performance in classifying the tissue sites in four different classes - high-grade squamous cell carcinoma, low-grade squamous cell carcinoma, leukoplakia, and normal squamous tissue. The classification accuracy for these four classes was observed to be approximately 94%, 100%, 100% and 91% for the training data set (based on leave-one-out cross-validation), and was approximately 90%, 90%, 85% and 88%, respectively for the corresponding classes for the independent validation data set.     

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

The identification of normal and cancer breast tissue of rats was investigated using high-frequency (HF) FT-Raman spectroscopy with a near-infrared excitation source on in vivo and ex vivo measurements. Significant differences in the Raman intensities of prominent Raman bands of lipids and proteins structures (2,800?C3,100?cm^?1) as well as in the broad band of water (3,100?C3,550?cm^?1) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high-frequency Raman spectra of normal and cancer tissues. LDA results with the leave-one-out cross-validation option yielded a discrimination accuracy of 77.2, 83.3, and 100% for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy HF Raman spectra. Despite the lower discrimination value for the in vivo transcutaneous measurements, which could be explained by the breathing movement and skin influences, our results showed good accuracy in discriminating between normal and cancer breast tissue samples. To support this, the calculated integration areas from the receiver-operating characteristic (ROC) curve yielded 0.86, 0.94, and 1.0 for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy measurements, respectively. The feasibility of using HF Raman spectroscopy as a clinical diagnostic tool for breast cancer detection and monitoring is due to no interfering contribution from the optical fiber in the HF Raman region, the shorter acquisition time due to a more intense signal in the HF Raman region, and the ability to distinguish between normal and cancerous tissues.     

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.     

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

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

Improvement of multivariate calibration techniques applied to 1-to-N component mixtures through an optimized experimental design

This work describes a novel experimental design aimed at building a calibration set constituted by samples containing a different number of components. The algorithm performs a reiteration process to maintain the number of samples at the lower value as possible and to ensure an homogeneous presence of all the concentration levels. The mixture design was applied to a drug system composed by one-to-four components in different combination. The resolution of the system was performed by three multivariate UV spectrophotometric methods utilizing principal component regression (PCR) and partial last squares (PLS1 and PLS2) algorithms. The calibration set was composed by 61 references on four concentration levels, including 15 samples for each quaternary, ternary and binary composition and 16 one-component samples. The calibration models were optimized through a careful selection of number of factors and wavelength zones, in such a way as to remove interferences from instrumental noise and excipients present in the pharmaceutical formulations. The prediction power of the regression models were verified and compared by analysis of an external prediction set. The models were finally used to assay pharmaceutical specialities containing the studied drugs in one-to-four formulations.     

Evaluation of nutritional parameters in infant formulas and powdered milk by Raman spectroscopy

It has been made a critical evaluation of the application of near infrared Fourier transform-Raman spectroscopy for the simultaneous determination of the most important nutritional parameters such as energetic value, carbohydrate, protein and fat contents of infant formula and powdered milk samples based on the use of partial least squares (PLS) regression analysis. A highly heterogeneous population of 23 samples, covering a wide range of infant food formula and powdered milk, were obtained from the Spanish market. Raman spectra, obtained by excitation with a Nd:YAG laser at 1064 nm, show no disturbing fluorescence effects; therefore sample spectra can be recorded without any previous preparation step. After correcting the spectra, hierarchical cluster analysis was performed in order to select a representative calibration set and the corresponding validation sample set. Different PLS models and several spectral windows were tested in order to evaluate their prediction capabilities for the validation set. Considering a calibration set comprised of three replicate spectra of 15 samples and a validation data set of eight samples, the procedure developed provided figures of merit which complied with the statutory values declared by the United States Food and Drug Administration (US FDA).     

基于灰狼算法的近红外光谱变量选择方法研究

基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。     

Application of FT-Raman Spectroscopy and Chemometric Analysis for Determination of Adulteration in Milk Powder

In this work, FT-Raman spectroscopy is explored as a rapid technique for the assessment of the milk powder quality. Based on information provided by Raman spectra of samples adulterated with starch and whey, a quantitative method is developed to identify the fraud, using Partial Least Squares regression (PLS). In regression models using PLS the results are satisfactory, and such models can be used to identify and quantify samples presenting whey and starch in milk powder at concentrations of 2.32% and 1.64% (w/w), respectively. In the whey determination, the obtained values in the PLS model of the new samples are compared with those obtained by the spectrophotometric method of acid ninhydrin. This result shows that there is no significant difference with the 95% level of confidence between the values provided by the PLS regression method and the acid ninhydrin. The present work shows Raman spectroscopy as an analytical tool which can be used in quality control of milk powder, even in fraud processes, and the calculated figures of merit such as sensitivity, accuracy, limit of detection and limit of quantification clearly demonstrate this potential use. Although the multivariate models developed are not strictly quantitative, especially for low concentrations, they can be used as screening methods for routine analysis, as showed by this work.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Simultaneous spectrophotometric determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric methods

In this study, the simultaneous determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric approaches using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. Spectra of paracetamol, ibuprofen and caffeine were recorded at several concentrations within their linear ranges and were used to compute the calibration mixture between wavelengths 200 and 400 nm at an interval of 1 nm in methanol:0.1 HCl (3:1). Partial least squares regression (PLS), genetic algorithm coupled with PLS (GA-PLS), and principal component-artificial neural network (PC-ANN) were used for chemometric analysis of data and the parameters of the chemometric procedures were optimized. The analytical performances of these chemometric methods were characterized by relative prediction errors and recoveries (%) and were compared with each other. The GA-PLS shows superiority over other applied multivariate methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity. Although the components show an important degree of spectral overlap, they have been determined simultaneously and rapidly requiring no separation step. These three methods were successfully applied to pharmaceutical formulation, capsule, with no interference from excipients as indicated by the recovery study results. The proposed methods are simple and rapid and can be easily used in the quality control of drugs as alternative analysis tools.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.     

Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm

An enzymatic flow-batch system with spectrophotometric detection was developed for simultaneous determination of levodopa [(S)-2 amino-3-(3,4-dihydroxyphenyl)propionic acid] and carbidopa [(S)-3-(3,4-dihydroxyphenyl)-2-hydrazino-2-methylpropionic acid] in pharmaceutical preparations. The data were analysed by univariate method, partial least squares (PLS) and a novel variable selection for multiple lineal regression (MLR), the successive projections algorithm (SPA). The enzyme polyphenol oxidase (PPO; EC 1.14.18.1) obtained from Ipomoea batatas (L.) Lam. was used to oxidize both analytes to their respective dopaquinones, which presented a strong absorption between 295 and 540 nm. The statistical parameters (RMSE and correlation coefficient) calculated after the PLS in the spectral region between 295 and 540 nm and MLR-SPA application were appropriate for levodopa and carbidopa. A comparative study of univariate, PLS, in different ranges, and MLR-SPA chemometrics models, was carried out by applying the elliptical joint confidence region (EJCR) test. The results were satisfactory for PLS in the spectral region between 295 and 540 nm and for MLR-SPA. Tablets of commercial samples were analysed and the results obtained are in close agreement with both, spectrophotometric and HPLC pharmacopeia methods. The sample throughput was 18 h(-1).     

A new method for simultaneous determination of 226Ra and uranium in aqueous samples by liquid scintillation using chemometrics

In this work, simultaneous determination of low levels of ²²⁶Ra and uranium in aqueous samples were performed by alpha-liquid scintillation counting (LSC) in conjunction with artificial neural network (ANN) and partial least squares (PLS). The counting rates at 73 channels, which were selected by genetic algorithm, were used for training. A PLS model with four latent variables and a principle component ANN model (4-4-2) with linear transfer function after hidden and output layers were created. Total relative error of prediction for PLS and ANN in synthetic mixtures was 18.05% and 24.78%, respectively. The matrix effect was studied by spiking the real samples with radium and uranium. Laser induced fluorescence was used for assessment of uranium prediction results in real samples.     

Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy

Native fluorescence characteristics of blood plasma were studied in the visible spectral region, at two different excitation wavelengths, 405 and 420 nm, to discriminate patients with different stages of oral malignancy from healthy subjects. The fluorescence spectra of blood plasma of oral malignant subjects exhibit characteristic spectral differences with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values at those emission wavelengths that give characteristic spectral features of each group of experimental subjects studied. These fluorescence intensity ratios were used as input variables for a multiple linear discriminant analysis across different groups. Leave-one out cross-validation was used to check the reliability of each discriminant analysis performed. The discriminant analysis performed across normal and oral cancerous subjects classified 94.7% of the original grouped cases and 93.7% of the cross-validated grouped cases. A classification algorithm was developed on the basis of the score of the discriminant functions (discriminant score) resulted in the analyses. The diagnostic potentiality of the present technique was also estimated in the discrimination of malignant subjects from normal and nonmalignant diseased subjects such as liver diseases. In the discriminant analysis performed across the three groups, normal, oral malignancy (including early and advanced stages) and liver diseases, 99% of the original grouped cases and 95.9% of the cross-validated grouped cases were correctly classified. Similar analysis performed across normal, early stage of oral malignancy, advanced oral malignancy and liver diseases correctly classified 94.9% of the original grouped cases and 91.8% of the cross-validated grouped cases.