Fractionation of freshwater colloids and particles by SPLITT: analysis by electron microscopy and 3D excitation-emission matrix fluorescence

This paper reports the first application of a combined approach utilizing split-flow thin-cell (SPLITT) separation to size fractionate natural aquatic colloids and particles collected from freshwater samples. No sample preconcentration was performed although some samples were investigated after alteration of the ambient pH. The unfractionated and fractionated samples were analyzed by scanning electron microscopy (SEM), environmental SEM, and 3D excitation emission matrix fluorescence. Qualitative and quantitative results by microscopy indicated that SPLITT produces well-resolved fractionations at appropriate sizes but with some perturbation of the sample. In addition, tryptophan-like fluorescence was shown to be caused by different organic moieties compared with humic-like and fulvic-like fluorescence. Tryptophan-like fluorescence intensity is found mainly in the particulate material but is not pH dependent, while humic- and fulvic-like fluorescence intensities are dependent on pH but not on size. Fulvic-like fluorescence intensity normalized to absorbance, related to fluorescence efficiency and molar mass, varies with size.     

Second-order calibration of excitation-emission matrix fluorescence spectra for the determination of N-phenylanthranilic acid derivatives

A spectrofluorimetric method has been developed for the quantitative determination of mefenamic, flufenamic, and meclofenamic acids in urine samples. The method is based on second-order data multivariate calibration (unfolded partial least squares (unfolded-PLS), multi-way PLS (N-PLS), parallel factor analysis (PARAFAC), self-weighted alternating trilinear decomposition (SWATLD), and bilinear least squares (BLLS)). The analytes were extracted from the urine samples in chloroform prior to the determination. The chloroform extraction was optimized for each analyte, studying the agitation time and the extraction pH, and the optimum values were 10 minutes and pH 3.5, respectively. The concentration ranges in chloroform solution of each of the analytes, used to construct the calibration matrix, were selected in the ranges from 0.15 to 0.8 microg mL-1 for flufenamic and meclofenamic acids and from 0.25 to 3.0 microg mL-1 for mefenamic acid. The combination of chloroform extraction and second-order calibration methods, using the excitation-emission matrices (EEMs) of the three analytes as analytical signals, allowed their simultaneous determination in human urine samples, in the range of approximately 80 mg L-1 to 250 mg L-1, with satisfactory results for all the assayed methods. Improved results over unfolded-PLS and N-PLS were found with PARAFAC, SWATLD, and BLLS, methods that exploit the second-order advantage.     

The chemical rank estimation for excitation-emission matrix fluorescence data by region-based moving window subspace projection technique and Monte Carlo simulation

A novel method, region-based on moving window subspace projection technique (RMWSPT) coupled with Monte Carlo simulation, was developed for the chemical rank estimation of excitation-emission matrix (EEM) fluorescence data arrays. RMWSPT determines the chemical rank by performing singular value decomposition (SVD) on the unfolded matrices of original data array and the subarrays yielded by a moving window, and through employing the subspace projection technique on the difference between the corresponding sub-bands of the significant eigenvectors and those of subarrays. Compared with the traditional methods, it utilizes the information from eigenvectors combined with the projection residual sum of squares values to estimate the rank of the EEM data arrays instead of using the eigenvalues. Two simulated and two real EEM data arrays were analyzed to demonstrate the excellent performance of the RMWSPT. Moreover, its performance was compared with that of other three factor-determining methods, i.e., factor indicator function (IND), the core consistency diagnostic (CORCONDIA) test and two-mode subspace comparison (TMSC) approaches. The results showed that the newly proposed method can accurately and quickly determine the chemical rank to fit the trilinear model, and it can deal with more complex situations in the presence of severe collinearity and trace concentration. The RMWSPT method thus lights a new avenue to determine the chemical rank of EEM data arrays and may hold great potential to be extended as a promising alternative for the chemical rank estimation.     

Mitigation of Rayleigh and Raman spectral interferences in multiway calibration of excitation-emission matrix fluorescence spectra

A weighted parallel factor analysis (W-PARAFAC) model is applied to excitation-emission matrix (EEM) fluorescence spectra of carbamate pesticides to aid with calibration in the presence of Raman scattering. Traditional PARAFAC inefficiently models the Raman scattering, resulting in prediction and calibration errors when a significant background is present. Four different weighting strategies were investigated and compared with subtraction of the appropriate sample background. Using a binary weighting strategy produced superior results, compared with a continuous distribution of weights. Further choice of weighting strategies, which are optimized to include either maximum analyte signal or to exclude a maximum amount of background scattering, is dependent on the degree of overlap and relative signal intensity.     

Chemometric analysis of frequency-domain photon migration data: quantitative measurements of optical properties and chromophore concentrations in multicomponent turbid media

Frequency-domain photon migration (FDPM) is a widely used technique for measuring the optical properties (i.e., absorption, micro(a), and reduced scattering, micro(s)', coefficients) of turbid samples. Typically, FDPM data analysis is performed with models based on a photon diffusion equation; however, analytical solutions are difficult to obtain for many realistic geometries. Here, we describe the use of models based instead on representative samples and multivariate calibration (chemometrics). FDPM data at seven wavelengths (ranging from 674 to 956 nm) and multiple modulation frequencies (ranging from 50 to 600 MHz) were gathered from turbid samples containing mixtures of three absorbing dyes. Values for micro(a) and micro(s)' were extracted from the FDPM data in different ways, first with the diffusion theory and then with the chemometric technique of partial least squares. Dye concentrations were determined from the FDPM data by three methods, first by least-squares fits to the diffusion results and then by two chemometric approaches. The accuracy of the chemometric predictions was comparable or superior for all three dyes. Our results indicate that chemometrics can recover optical properties and dye concentrations from the frequency-dependent behavior of photon density waves, without the need for diffusion-based models. Future applications to more complicated geometries, lower-scattering samples, and simpler FDPM instrumentation are discussed.     

Chemometric analysis of Raman spectroscopic data for process control applications

Two examples are given demonstrating the use of multivariate modeling in Raman process control applications. In one example, principal component analysis (PCA) and principal component regression (PCR) are used to model the curing of a high performance thermoset. The PCA results are found to give more accurate results when compared to univariate methods. In a second example, the octane number of gasoline is accurately modeled using partial least squares (PLS) regression analysis. For both examples, methods of normalization are considered in an effort to overcome the limitations of the single beam nature of Raman spectra.     

Chemometric labeling of cereal tissues in multichannel fluorescence microscopy images using discriminant analysis

This paper presents a novel, semiautomatic method for microscopic identification of multicomponent samples, which allows the identification, location, and percentage quantity of each component to be determined. The method involves applying discriminant analysis to a sequence of multichannel fluorescence microscopy images via a supervised learning approach; by selecting groups of pixels that are representative for each component type in a "known" sample, a computer is "taught" how to recognize the behavior (i.e., fluorescence emission) of the various components when illuminated under different spectral conditions. The identity, quantity, and location of these components in "unknown" samples (i.e., samples with the same component types but in different ratios or distributions) can then be investigated. The technique therefore enables semiautomatic quantitative fluorescence microscopy and has potential as a quality control tool. This work demonstrates the application of the technique to artificial and natural samples and critically discusses its quality, potential, and limitations.     

Second-order advantage achieved by unfolded-partial least-squares/residual bilinearization modeling of excitation-emission fluorescence data presenting inner filter effects

A second-order multivariate calibration approach, based on a combination of unfolded-partial least-squares with residual bilinearization (U-PLS/RBL), has been applied to fluorescence excitation-emission matrix data for multicomponent mixtures showing inner filter effects. The employed chemometric algorithm is the most successful one regarding the prediction of analyte concentrations when significant inner filter effects occur, even in the presence of unexpected sample components, which require strict adherence to the second-order advantage. Results for simulated fluorescence excitation-emission data are described, in comparison with the classical approach based on parallel factor analysis and other second-order algorithms, including generalized rank annihilation, bilinear least squares combined with residual bilinearization and multivariate curve resolution-alternating leastsquares. A set of experimental data was also studied, in which calibration was performed with fluorescence excitation-emission matrices for samples containing mixtures of chrysene (the analyte of interest) and benzopyrene (which produced strong inner filter effect across the useful wavelength range). Prediction was made on validation samples with a qualitative composition similar to the calibration set, and also on test samples containing an unexpected component (pyrene). In this latter case, U-PLS/RBL showed a unique success for the analysis of the calibrated component chrysene, achieving the useful second-order advantage.     

浮游植物活体三维荧光光谱特征提取

测量了6种东海常见的浮游植物在两个温度(20℃,15℃)、三个光照(7000Lux,4100Lux,1100Lux)下的不同生长期的三维激发／发射荧光光谱,研究了光谱特征提取方法。对去除散射干扰后的三维光谱进行了奇异值分解,得到的相应于激发光谱的第一主成分具有区分藻种的能力,可作为三维光谱的特征光谱。分析结果表明,实验条件下,等鞭金藻(Isochrysis galbana)、岛国大扁藻(Platymonas helgolanidica)和中肋骨条藻(Skeletonema costatuma)的特征光谱相似度高,塔玛亚历山大藻(Alexandrium tamarense)、东海原甲藻(Prorocentrum dentatum)和尖刺拟菱形藻(Pseudo—nitzschia pungens)光谱相似度稍差。     

Targeted profiling: quantitative analysis of 1H NMR metabolomics data

Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of "metabolomics", which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as "targeted profiling". Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional "spectral binning" analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 microM-8 mM). "Targeted profiling" is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.     

Chemometric resolution and quantification of four-way data arising from comprehensive 2D-LC-DAD analysis of human urine

Two-dimensional liquid chromatography (LC × LC) is quickly becoming an important technique for the analysis of complex samples, owing largely to the relatively high peak capacities attainable by this analytical technique. With the increase in the complexity of the sample comes a corresponding increase in the complexity of the collected data. Thus the need for chemometric methods capable of resolving and quantifying such data is ever more urgent in order to obtain the maximum information available from the data. To this end, we have developed a chemometric method that combines iterative key set factor analysis and multivariate curve resolution-alternating least squares analysis with a spectral selectivity constraint that is shown to be capable of resolving chromatographically rank deficient, non-multilinear data. (spectrally rank deficient compounds can only be quantified if the peaks having the same spectra are chromatographically resolved). Over 50 chromatographic peaks were found in a relatively small section of a LC × LC-diode array data set of replicate urine samples (a four-way data set) using the developed method. The relative concentrations for 34 of the 50 peaks were determined with % RSD values ranging from 0.09% to 16%.     

Fluorescence excitation-emission matrix spectroscopy with regional integration analysis for characterizing composition and transformation of dissolved organic matter in landfill leachates

Dissolved organic matter (DOM) obtained from landfill leachates was separated into hydrophobic base, hydrophilic matter (HIM), hydrophobic acid (HOA), and hydrophobic neutral fractions. The composition and transformation of the DOM and its fractions were investigated. The results show that the DOM isolated from young, intermediate, and old landfill leachates were mainly composed of tyrosine-, tryptophan-, and humic- and fulvic-like substances, respectively. The primary fractions of the DOM in leachates were HOA and HIM. The HOA and HIM fractions from young leachates predominantly contained tryptophan- and tyrosine-like materials, respectively. The HOA fractions in intermediate and old leachates were mainly composed of humic- and fulvic-like materials, whereas the HIM fractions were dominated by tryptophan-like materials and humic- and fulvic-like substances. The hydrophobic organic fractions and humic- and fulvic-like substances increased with time, whereas the HIM and the tyrosine-like materials decreased during the landfill process, rendering biological processing of leachates ineffective.     

Excitation-emission matrix fluorescence spectroscopy for natural organic matter characterization: a quantitative evaluation of calibration and spectral correction procedures

The influence of different data collection procedures and of wavelength-dependent instrumental biases on fluorescence excitation-emission matrix (EEM) spectral analysis of aqueous organic matter samples was investigated. Particular attention was given to fluorescence contours (spectral shape) and peak fluorescence intensities. Instrumental bias was evaluated by independently applying excitation and emission correction factors to the raw excitation and emission data, respectively. The peak fluorescence intensities of representative natural organic matter and tryptophan were significantly influenced by the application of excitation and emission spectral correction factors and by the manner in which the raw data was collected. Humification and fluorescence indices were also influenced by emission correction factors but were independent of reference (excitation) intensity normalization or correction. EEM surface contours were dependent on normalization of the fluorescence intensity to the reference intensity but were not influenced by either excitation or emission spectral correction factors. Authors should be explicit in how excitation and emission spectral correction procedures are implemented in their investigations, which will help to facilitate intra-laboratory comparisons and data sharing.     

海洋微藻产生的不同粒级溶解有机物质的三维荧光光谱

通过过滤(0.7μm)和切向超滤(100kDa、10kDa、1kDa)技术将实验室内培养的赤潮异弯藻和中肋骨条藻藻液分离成100kDa-0.7μm、10～100kDa、1～10kDa的胶体浓缩液和＜1kDa的超滤液,利用激发-发射矩阵荧光光谱技术测定了不同粒级溶液中有机物的荧光性质,分析了三维荧光光谱图中的荧光峰位置、数量及荧光强度的变化情况.结果表明,两种微藻产生的类腐殖质荧光物质主要是＜1kDa的小分子物质.赤潮异湾藻产生的类蛋白荧光物质中,15%是100kDa-0.7μm的大胶体,8%是10～100kDa的中胶体,而大部分是＜1kDa的小分子物质.赤潮异弯藻各级超滤截留液的类蛋白荧光峰的发射波长与过滤液相比,发生蓝移,而超滤液的则发生红移,表明赤潮异湾藻产生的类蛋白荧光物质随着粒径的增大极性减弱.     

Bayesian approach to the analysis of fluorescence correlation spectroscopy data I: theory

Fluorescence correlation spectroscopy (FCS) is a powerful tool to infer the physical process of macromolecules including local concentration, binding, and transport from fluorescence intensity measurements. Interpretation of FCS data relies critically on objective multiple hypothesis testing of competing models for complex physical processes that are typically unknown a priori. Here, we propose an objective Bayesian inference procedure for testing multiple competing models to describe FCS data based on temporal autocorrelation functions. We illustrate its performance on simulated temporal autocorrelation functions for which the physical process, noise, and sampling properties can be controlled completely. The procedure enables the systematic and objective evaluation of an arbitrary number of competing, non-nested physical models for FCS data, appropriately penalizing model complexity according to the Principle of Parsimony to prefer simpler models as the signal-to-noise ratio decreases. In addition to eliminating overfitting of FCS data, the procedure dictates when the interpretation of model parameters are not justified by the signal-to-noise ratio of the underlying sampled data. The proposed approach is completely general in its applicability to transport, binding, or other physical processes, as well as spatially resolved FCS from image correlation spectroscopy, providing an important theoretical foundation for the automated application of FCS to the analysis of biological and other complex samples.     

Acid-labile isotope-coded extractants: a class of reagents for quantitative mass spectrometric analysis of complex protein mixtures

Quantitative mass spectrometry using stable isotope-labeled tagging reagents such as isotope-coded affinity tags has emerged as a powerful tool for identification and relative quantitation of proteins in current proteomic studies. Here we describe an integrated approach using both automated two-dimensional liquid chromatography/ mass spectrometry (2D-LC/MS) and a novel class of chemically modified resins, termed acid-labile isotope-coded extractants (ALICE), for quantitative mass spectrometric analysis of protein mixtures. ALICE contains a thiol-reactive group that is used to capture all cysteine (Cys)-containing peptides from peptide mixtures, an acid-labile linker, and a nonbiological polymer. The acid-labile linker is synthesized in both heavy and light isotope-coded forms and therefore enables the direct relative quantitation of peptides/proteins through mass spectrometric analysis. To test the ALICE method for quantitative protein analysis, two model protein mixtures were fully reduced, alkylated, and digested in solution separately and then Cys-containing peptides covalently captured by either light or heavy ALICE. The reacted light and heavy ALICE were mixed and washed extensively under rigorous conditions and the Cys-containing peptides retrieved by mild acid-catalyzed elution. Finally, the eluted peptides were directly subjected to automated 2D-LC/MS for protein identification and LC/MS for accurate relative quantitation. Our initial study showed that quantitation of protein mixtures using ALICE was accurate. In addition, isolation of Cys-containing peptides by the ALICE method was robust and specific and thus yielded very low background in mass spectrometric studies. Overall, the use of ALICE provides improved dynamic range and sensitivity for quantitative mass spectrometric analysis of peptide or protein mixtures.     

Concentration dependence in the spectra of polycyclic aromatic hydrocarbon mixtures by front-surface fluorescence analysis

Polycyclic aromatic hydrocarbons (PAHs) are a challenge for fluorescence analysis. Inner-filter quenching effects and energy transfers of PAHs in petroleum differ with concentration, leading to characteristically different fluorescence. At high concentrations of single PAH molecules and petroleum mixtures the classic rectangular quartz cell presents a particular challenge due to inner filter effects from right-angle illumination. While quite a few instrumental and mathematical corrections for these effects are reported throughout the literature, for researchers with standard instrumental sample compartments a triangular fluorometer cell can be used to emulate front-surface fluorescence analysis. This work presents concentration study results from the single PAH molecules fluoranthene and azulene dissolved in spectral-grade cyclohexane and also seeks to correct an aspect of our past work on the fluorescence of petroleum mixtures.