Use of a Plackett-Burman design with multivariate calibration for the analysis of polycyclic aromatic hydrocarbons in micellar media by synchronous fluorescence

This work describes the use of a multilevel Plackett-Burman design (PB) for the creation of a calibration set for partial least square regression (PLS). The PB calibration set was compared to a collinear analogue by testing these two PLS models for the analysis of six polycyclic aromatic hydrocarbons (PAHs). These compounds were analyzed in micellar media by synchronous fluorescence after determination of the experimental conditions (choice of surfactant, analytical conditions such as deltalambda, step, and scan range). The external validation shows that the collinear set is inappropriate to quantify PAH in real samples, but the PB calibration set affords optimal results.     

Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.     

Principal alarms in multivariate statistical process control using independent component analysis

This article proposes a methodology that helps to predict the main mean shifts, denoted as principal alarms, in a non-normal multivariate process using the available in-control data. The analysis is based on the transformation of the observed correlated variables into independent factors using independent component analysis. These independent components allow us to simulate shifts preserving the covariance structure. The graphical representations of those simulated shifts are helpful in improving the design and control of the process. Two real manufacturing processes are presented showing the advantage of the proposed methodology.     

Detection of uranium in solids by using laser-induced breakdown spectroscopy combined with laser-induced fluorescence

Detection of uranium in solids by using laser-induced breakdown spectroscopy has been investigated in combination with laser-induced fluorescence. An optical parametric oscillator wavelength-tunable laser was used to resonantly excite the uranium atoms and ions within the plasma plumes generated by a Q-switched Nd:YAG laser. Both atomic and ionic lines can be selected to detect their fluorescence lines. A uranium concentration of 462 ppm in a glass sample can be detected by using this technique at an excitation wavelength of 385.96 nm for resonant excitation of U II and a fluorescence line wavelength of 409.0 nm from U II.     

Three-dimensional imaging of objects embedded in turbid media with fluorescence and Raman spectroscopy

We present a single-ended technique for three-dimensional imaging of objects embedded in a turbid medium by the use of time-resolved fluorescence emission or Raman scattering. The technique uses the earliest arriving photons, which we show are not sensitive to the relatively long fluorescence lifetime, and thus can be used to extract the desired spatial information accurately, even at a distance equivalent to 100 mean free paths. The results also demonstrate the feasibility and the potential of one's combining time-resolved optical tomography with fluorescence or Raman spectroscopy to localize and identify the embedded objects. This technique may be valuable for the diagnosis of disease in highly scattering human tissue because it can provide spatial and biochemical information about the composition of embedded lesions.     

Quantitative Raman spectroscopy of highly fluorescent samples using pseudosecond derivatives and multivariate analysis

Intense luminescence backgrounds cause significant problems in quantitative Raman spectroscopy, particularly in multivariate analysis where background suppression is essential. Taking second derivatives reduces the background, but differentiation increases the apparent noise that arises on spectra recorded with CCD detectors due to random, but fixed, variations in the pixel-to-pixel response. We have recently reported a very general method for correcting CCD fixed-pattern response in which spectra are taken at two or more slightly shifted spectrometer positions and are then subtracted to give a derivative-like shifted, subtracted Raman (SSR) spectrum. Here we show that differentiating SSR data (which has inherently higher S/N than the undifferenced data) yields spectra that are similar to those that are obtained from the normal two-step differentiation process and can be characterized as pseudo-second-derivative, PSD, spectra. The backgrounds are suppressed in the PSD spectra, which means they can be used directly in multivariate data analysis, but they have significantly higher S/N ratios than do simple second derivatives. To demonstrate the improvement brought about by using PSD spectra, we have analyzed known samples, consisting of simple binary mixtures of methanol and ethanol doped with laser dye. When the background levels of all samples included in the models were < or =10x greater than the intensity of the strongest Raman bands, partial least-squares calibration of the PSD data gave a standard error of prediction of 3.2%. Calibration using second derivatives gave a prediction error which was approximately twice as large, at 6.5%; however, when data with background levels . approximately 100x larger than the strongest Raman bands were included, the noise on the second-derivative spectra was so large as to prevent a meaningful calibration. Conversely, the PSD treatment of these samples gave a very satisfactory calibration with a standard error of prediction (3.3%) almost identical to that obtained when the most fluorescent samples were excluded. This method clearly has great potential for general purpose Raman analytical chemistry, because it does not depend on specialized equipment, is computationally undemanding, and gives stable and robust calibrations, even for samples in which the luminescence background level fluctuates between the extremes of being practically zero and completely dominating the Raman signal.     

Application of supervised self-organizing maps in breast cancer diagnosis by total synchronous fluorescence spectroscopy

Data from total synchronous fluorescence spectroscopy (TSFS) measurements of normal and malignant breast tissue samples are introduced in supervised self-organizing maps, a type of artificial neural network (ANN), to obtain diagnosis. Three spectral regions in both TSFS patterns and first-derivative TSFS patterns exhibited clear differences between normal and malignant tissue groups, and intensities measured from these regions served as inputs to neural networks. Histology findings are used as the gold standard to train self-organizing maps in a supervised way. Diagnostic accuracy of this procedure is evaluated with sample test groups for two cases, when the neural network uses TSFS data and when the neural network uses data from first-derivative TSFS. In the first case diagnostic sensitivity of 87.1% and specificity of 91.7% are found, while in the second case sensitivity of 100% and specificity of 94.4% are achieved.     

Identification of bacterial spores using statistical analysis of Fourier transform infrared photoacoustic spectroscopy data

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) has been applied for the first time to the identification and speciation of bacterial spores. A total of forty specimens representing five strains of Bacillus spores (Bacillus subtilis ATCC 49760, Bacillus atrophaeus ATCC 49337, Bacillus subtilis 6051, Bacillus thuringiensis subsp. kurstaki, and Bacillus globigii Dugway) were analyzed. Spores were deposited, with minimal preparation, into the photoacoustic sample cup and their spectra recorded. Principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations were used on this spectral library to develop algorithms for step-wise classification at three levels: (1) bacterial/nonbacterial, (2) membership within the spore library, and (3) bacterial strain. Internal cross-validation studies on library spectra yielded classification success rates of 87% or better at each of these three levels. Analysis of fifteen blind samples, which included five samples of spores already in the spectral library, two samples of closely related Bacillus globigii 01 spores not in the library, and eight samples of nonbacterial materials, yielded 100% accuracy in distinguishing among bacterial/nonbacterial samples, membership in the library, and bacterial strains within the library.     

Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis

A novel approach for matching oil samples by fluorescence spectroscopy combined with three-way decomposition of spectra is presented. It offers an objective fingerprinting based on the relative composition of polycyclic aromatic compounds (PACs) in oils. The method is complementary to GC-FID for initial screening of oil samples but can also be used for prescreening in the field, onboard ships, using a portable fluorescence spectrometer. Parallel factor analysis (PARAFAC) was applied to fluorescence excitation-emission matrixes (EEMs) of heavy fuel oils (HFOs), light fuel oils, lubricating oils, crude oils, unknown oils, and a sample collected in the spill area two weeks after the Baltic Carrier oil spill (Denmark, 2001). A total of 112 EEMs were decomposed into a five-factor PARAFAC model using excitation wavelengths from 245 to 400 nm and emission wavelengths from 280 to 550 nm. The PARAFAC factors were compared to EEMs of PAC standards with two to five rings, and the comparisons indicate that each of the factors can be related to a mixture of PACs with similar fluorescence characteristics: a mixture of naphthalenes and dibenzothiophenes, fluorenes, phenanthrenes, chrysenes, and five-ring PACs, respectively. Oils were grouped in score plots according to oil type. Except for HFOs and crude oils, the method easily discriminated between the four oil types. Minor overlaps of HFOs and crude oils were observed along all five PARAFAC factors, and the variability of crude oils was large along factor 2 due to a varying content of five-ring PACs. The spill sample was correctly assigned as a HFO with similar PAC pattern as oil from the cargo tank of the Baltic Carrier by comparing the correlation coefficient of scores for the oil spill sample and possible source oils (i.e., oils in the database).     

Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption

The fluorescence from a turbid medium such as biologic tissue contains information about scattering and absorption, as well as the intrinsic fluorescence, i.e., the fluorescence from an optically thin sample of pure fluorophores. The interplay of scattering and absorption can result in severe distortion of the intrinsic spectral features. These distortions can be removed by use of a photon-migration-based picture and information from simultaneously acquired fluorescence and reflectance spectra. We present experimental evidence demonstrating the validity of such an approach for extracting the intrinsic fluorescence for a wide range of scatterer and absorber concentrations in tissue models, ex vivo and in vivo tissues. We show that variations in line shape and intensity in intrinsic tissue fluorescence are significantly reduced compared with the corresponding measured fluorescence.     

Technique for enhancing signal in conventional backscattering fluorescence and Raman spectroscopy of turbid media

We demonstrate experimentally, for the first time, the feasibility of passively enhancing fluorescence and Raman signals from diffusely scattering media in a conventional backscattering collection geometry. The method employs transmission of the collimated excitation laser beam through a "unidirectional" dielectric mirror placed directly in front of the sample. This permits laser light that escapes from the sample surface to be reflected back into the sample where it can be more usefully employed in generating Raman and fluorescence signals. This leads to improved Raman signal, higher signal-to-noise ratio, and shorter acquisition times. Feasibility studies performed on standard pharmaceutical tablets and on sheets of Teflon, using a single enhancing element, demonstrate signal enhancement factors of 6 (fluorescence) and 3 (Raman). Potential applications of this simple device include improving quality control of pharmaceutical products, disease diagnosis of biological tissue, forensics, and security screening.     

Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis

This paper reports the potential of synchronous front-face fluorescence spectroscopy in the characterization at the molecular level of milk changes during mild heating from 4 to 50 degrees C and acidification in the pH range of 6.8 to 5.1. Synchronous fluorescence spectra were collected in the 250-550 nm excitation wavelength range using offsets of 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, and 240 nm between excitation and emission monochromators. The potential of parallel factor (PARAFAC) analysis in the decomposition of the whole synchronous fluorescence data set into the contribution of each of the fluorescent compounds present in milk has been investigated for heating and acidification data sets. Models were fitted from 1 to 7 components. Considering the core consistency values, PARAFAC models with three components have been considered. The first three components explained 94.43% and 94.13% of the total variance for heating and acidification data sets, respectively. The loading profiles of the first and second components derived from PARAFAC analysis performed on heating and acidification data sets corresponded quite well with the characteristics of tryptophan and vitamin A fluorescence spectra, respectively. The third component corresponded to the riboflavin fluorescence spectrum. Considering the heating experiment, the profile of the concentration mode for the second component showed large variations according to the temperature, which were assigned to the melting of triglycerides between 4 and 50 degrees C. For the acidification experiment, drastic changes in the concentration modes of the three components were observed for pH below 5.6, in agreement with structural changes in casein micelles.     

Effects of ovariectomy on rat mandibular cortical bone: a study using Raman spectroscopy and multivariate analysis

To investigate the effects of ovariectomy (OVX) on rat mandibular bone, the physicochemical compositions of mandibular cortical bone of ovariectomy and sham operated rats 2, 4, and 8 months after surgery were compared using Raman spectroscopy. With principal component analysis and linear discriminant analysis based on the Raman spectra, the mandibular cortical bone of the OVX group was clearly distinguished from that of the sham-operated group 8 months after surgery with no overlap. Specifically, significant reductions in the mineral-to-matrix ratio and full width at half-maximum as well as a significant increase in the carbonate-to-phosphate ratio were observed in the mandibular cortical bone of the OVX group. Results support the hypothesis that Raman spectroscopy is sensitive enough to distinguish between OVX and sham-operated mandibles with multivariate analysis by detecting the chemical composition of the mandibular cortical bone. The parameters mineral-to-matrix ratio, carbonate-to-phosphate ratio, and full width at half-maximum can appropriately characterize changes in the chemical composition of the mandibular cortical bone after OVX.     

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.     

Identification of organic materials in historic oil paintings using correlated extractionless surface-enhanced Raman scattering and fluorescence microscopy

A novel spectroscopic approach, correlated surface-enhanced Raman scattering (SERS) and fluorescence microscopy, is used to identify organic materials in two 18th century oil paintings. The vibrational fingerprint of analyte molecules is revealed using SERS, and corresponding fluorescence measurements provide a probe of local environment as well as an inherent capability to verify material identification. Correlated SERS and fluorescence measurements are performed directly on single pigment particles obtained from historic oil paintings with Ag colloids as the enhancing substrate. We demonstrate the first extractionless nonhydrolysis SERS study of oil paint as well as the potential of correlated SERS and fluorescence microscopy studies for the simultaneous identification of organic colorants and binding media in historic oil paintings.     

Detection of flowing fluorescent particles in a microcapillary using fluorescence correlation spectroscopy

Capillary flow experiments are described with fluorescent molecules, bacteria, and microspheres using fluorescence correlation spectroscopy as an analytical tool. The flow velocity in the microcapillary is determined by fitting autocorrelation traces with a model containing parameters related to diffusion and flow. The flow profile of pressure-driven flow inside a microcapillary is determined by using the fluorescence fluctuations of a small dye molecule. It was found that bacteria and microspheres are retarded in their flow by optical forces produced by the laser beam.     

Detection of lead in water using laser-induced breakdown spectroscopy and laser-induced fluorescence

Laser-induced breakdown spectroscopy (LIBS) is a well-known technique for fast, stand-off, and nondestructive analysis of the elemental composition of a sample. We have been investigating micro-LIBS for the past few years and demonstrating its application to microanalysis of surfaces. Recently, we have integrated micro-LIBS with laser-induced fluorescence (LIF), and this combination, laser ablation laser-induced fluorescence (LA-LIF), allows one to achieve much higher sensitivity than traditional LIBS. In this study, we use a 170 microJ laser pulse to ablate a liquid sample in order to measure the lead content. The plasma created was re-excited by a 10 microJ laser pulse tuned to one of the lead resonant lines. Upon optimization, the 3sigma limit of detection was found to be 35 +/- 7 ppb, which is close to the EPA standard for the level of lead allowed in drinking water.     

Differentiation of neotropical fish species with statistical analysis of fourier transform infrared photoacoustic spectroscopy data

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) was applied to nineteen fish species in Brazil's Upper Paraná River basin to identify differences in the structural composition of their scales. To differentiate the species, a canonical discriminant analysis was used to indicate the most important absorption peaks in the mid-infrared region. Significant differences were found in the chemical composition of scales among the studied fish species, with Wilk's lambda = 5.2 × 10(-6), F((13,18,394)) = 37.57, and P < 0.001, indicating that O-CH(2) wag at 1396 cm(-1) can be used as a biomarker of this species group. The species could be categorized into four groups according to phylogenetic similarity, suggesting that the O-CH(2) 1396 cm(-1) absorbance is related to the biological traits of each species. This procedure can also be used to complement evolutionary studies.