Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum, which might be beneficial for certain applications. We conclude that the subsequent parallel factor analysis models fitted to excitation–emission data after scatter identification based on either ROBPCA or S‐PCA are comparable; however, the modified method based on S‐PCA clearly outperforms the original approach in relation to computational time. Copyright © 2013 John Wiley & Sons, Ltd.     

Water-source characterization and classification with fluorescence EEM spectroscopy: PARAFAC analysis

Water-quality protection and environmental forensics require rapid water monitoring and source identification. In this paper, parallel factor analysis (PARAFAC) of fluorescence excitation-emission matrix spectra (EEMS) was used to characterize and classify water samples from landfills, wastewater treatment plants, lakes, and rivers. The study showed that the optimal number of components was four to represent the data set. The fluorescence fingerprints for water samples from different sources were sufficiently different, so qualitative water classification could be achieved. Specifically, Component 1 was the major fluorescing centre in river waters, with characteristics consistent with humic-like fluorophores; Component 2 was the dominant fluorophore in the treated wastewaters; Component 3 was the characteristic fluorophore in landfill leachates; and Components 1, 3, and 4 existed in lake waters at comparable weight, among which Component 4 may be considered as a protein- or amino acid-like fluorophore.     

PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data

Novel post‐genomics experiments such as metabolomics provide datasets that are highly multivariate and often reflect an underlying experimental design, developed with a specific experimental question in mind. ANOVA‐simultaneous component analysis (ASCA) can be used for the analysis of multivariate data obtained from an experimental design instead of the widely used principal component analysis (PCA). This increases the interpretability of the model in terms of the experimental question. Aside from the levels of individual factors, variation that can be described by the experimental design may also depend on levels of multiple (crossed) factors simultaneously, e.g. the interactions. ASCA describes each contribution with a PCA model, but a contribution depending on crossed factors may be described more parsimoniously by multiway models like parallel factor analysis (PARAFAC). The combination of PARAFAC and ASCA, named PARAFASCA, provides a view on the data that is both parsimonious and focused on the experimental question. The novel method is used to analyze a dataset in which the effect of two doses of hydrazine on the urinary chemical composition of rats is investigated by time‐resolved metabolic fingerprinting with nuclear magnetic resonance (NMR) spectroscopy. This experiment has been conducted to monitor the dose‐specific urine composition changes in time upon hydrazine administration. Comparison of the PCA, the ASCA and the PARAFASCA models shows that ASCA and PARAFASCA describe the data more dedicated to the experimental question than PCA, but that PARAFASCA is more parsimonious than ASCA, and separates the variation underlying different effects better. Copyright © 2008 John Wiley & Sons, Ltd.     

A weak degeneracy revealing decomposition for the CANDECOMP/PARAFAC model

The CANDECOMP/PARAFAC (CP) model is a well known and frequently used tool for extracting substantial information from a three‐way data array. It has several useful characteristics and usually gives meaningful insights about the underlying structure of the data. However, in some cases it has a ‘strange’ behaviour suffering from the so‐called ‘degenerate solutions’, i.e. solutions where the components show a diverging pattern and are meaningless. Several authors have investigated the causes of degeneracy concluding that the phenomenon is due to a lack of minimum of the loss function. In this paper, we study the degeneracy of CP limiting our attention to the two‐component case. The study is done by introducing a canonical form, called 2DR, which is ‘weakly degeneracy revealing’. On the ground of this framework, degeneracy is studied along with some of the remedies proposed in the literature by using a Tucker3 model having a core in the 2DR form. The analysis gives new insights about the behaviour of the CP model and suggests new ideas on how to deal with degeneracy. Copyright © 2010 John Wiley & Sons, Ltd.     

Application of parallel factor analysis to total synchronous fluorescence spectrum of dilute multifluorophoric solutions: Addressing the issue of lack of trilinearity in total synchronous fluorescence data set

In recent years, total synchronous fluorescence (TSF) spectroscopy has become popular for the analysis of multifluorophoric systems. Application of PARAFAC, a popular deconvolution tool, requires trilinear structure in the three-way data array. The present work shows that TSF based three-way array data set of dimension sample × wavelength × Δλ does not have trilinear structure and hence it should not be subjected to PARAFAC analysis. This work also proposes that a TSF data set can be converted to an excitation–emission matrix fluorescence (EEMF) like data set which has trilinear structure, so that PARAFAC analysis can be performed on it. This also enables the retrieval of PARAFAC-separated component TSF spectra.     

Independent component analysis as a pretreatment method for parallel factor analysis to eliminate artefacts from multiway data

Parallel factor analysis (PARAFAC) has successfully been used in many applications for the analysis of excitation-emission fluorescence data. However, some measurement “artefacts”, such as Rayleigh or Raman scattering, can pose a problem for the extraction of the PARAFAC components and their interpretation. Replacing the spectral zones corresponding to these signals by missing values in the data is not necessarily a method of choice in the cases where informative signals lie in the same wavelength regions. In this article, independent component analysis (ICA) is used on the unfolded cubic array, and the independent components related to the Rayleigh and Raman scattering are identified and removed prior to the reconstruction of the excitation-emission fluorescence data cube. PARAFAC is then applied on these data reconstructed after selective artefact removal, and satisfactory models can be obtained. This procedure, although particularly useful for 3D fluorescence data, may be applied to other types of data as well.     

A rapid fluorescence based method for the quantitative analysis of cell culture media photo-degradation

Cell culture media are very complex chemical mixtures that are one of the most important aspects in biopharmaceutical manufacturing. The complex composition of many media leads to materials that are inherently unstable and of particular concern, is media photo-damage which can adversely affect cell culture performance. This can be significant particularly with small scale transparent bioreactors and media containers are used for process development or research. Chromatographic and/or mass spectrometry based analyses are often time-consuming and expensive for routine high-throughput media analysis particularly during scale up or development processes.     

Strategies for solving matrix effects in the analysis of sulfathiazole in honey samples using three-way photochemically induced fluorescence data

A widely employed compound for honey treatment, sulfathiazole (ST), was determined in commercial honey samples, employing a combination of photochemically induced fluorescence excitation-emission matrices (EEMs) and chemometric processing of the recorded second-order data. Parallel Factor Analysis (PARAFAC) and Self-Weighted Alternating Trilinear Decomposition (SWATLD) methods were used for calibration. An appropriately designed calibration with a set of standards composed of 18 samples, coupled to the use of the second-order advantage offered by the applied chemometric techniques, allowed quantitation of sulfathiazole in spiked commercial honey samples. No previous separation or sample pretreatment steps were required. The results were compared with other calibration methods such as N-PLS and PLS-1 that produced good results on synthetic samples but not on the investigated commercial honey samples.     

Comparison of PARAFAC and PARALIND in modeling three‐way fluorescence data array with special linear dependences in three modes: a case study in 2‐naphthol

Owing to excited‐state proton transfer in 2‐naphthol solutions, the fluorescence excitation–emission matrices (EEMs) have factors that are highly dependent in three modes. For the first time such EEMs are used to compare the capability of PARALIND (PARAFAC with linear dependence) and conventional PARAFAC in modeling three‐way EEMs having linearly dependent factors in three modes. Two primary conclusions have been drawn. First, the results indicate that a 3‐factor PARAFAC model fit the data better than two PARALIND models (type 1 and 2) in this case while equally well with a specially PARALIND model (type 3); second, a negative core consistency (CC) in the 3‐factor PARAFAC model is reported but the type 3 PARALIND model reports a nearly 100 CC. This work has demonstrated that a properly constrained PARALIND can fit the very special EEMs of 2‐naphthol. The presence of negative CC associated with a perfect PARAFAC model would imply the presence of very special linear dependences in EEMs, which would be used as an “alarm” for the investigators to interpret the data more carefully when dealing with complicated environmental EEMs in the absence of a priori knowledge. Copyright © 2010 John Wiley & Sons, Ltd.     

茶叶中的茶多酚荧光检测方法

以石墨烯量子点为荧光探针,基于二氧化锰纳米片与石墨烯量子点之间发生有效的荧光共振能量转移,构建了一种茶叶中茶多酚含量的荧光检测新方法。MnO2纳米片使石墨烯量子点的荧光淬灭,而茶多酚能与MnO2发生氧化还原反应,将其还原成Mn^2+,使体系荧光恢复。茶多酚含量与荧光强度增量在20~750μg/mL范围内成线性相关,线性方程为F=1.0574c-114.9,线性相关系数为R=0.9936。将该方法用于茶叶中茶多酚含量的检测,结果与国标法接近。方法有望应用于食品中其它抗氧化物质的实时检测。     

A robust partial least squares regression method with applications

Partial least squares (PLS) regression is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper, we show that if the sample covariance matrix is properly robustified further robustification of the linear regression steps of the PLS algorithm becomes unnecessary. The robust estimate of the covariance matrix is computed by searching for outliers in univariate projections of the data on a combination of random directions (Stahel—Donoho) and specific directions obtained by maximizing and minimizing the kurtosis coefficient of the projected data, as proposed by Peña and Prieto [1]. It is shown that this procedure is fast to apply and provides better results than other methods proposed in the literature. Its performance is illustrated by Monte Carlo and by an example, where the algorithm is able to show features of the data which were undetected by previous methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Combining standard addition method and second-order advantage for direct determination of salicylate in undiluted human plasma by spectrofluorimetry

Second-order advantage turns possible a determination in the presence of unknown interferences. This work presented an application of the second-order advantage provided by parallel factor analysis (PARAFAC). The aim was the direct determination of salicylic acid (SA), the main product of aspirin degradation, in undiluted human plasma by spectrofluorimetry. The strategy of this analysis combined the use of PARAFAC, for extraction of the pure analyte signal, with the standard addition method, for a determination in the presence of a strong matrix effect caused by the quenching effect of the proteins present in the plasma. For each sample, four standard additions were performed, in triplicates. A specific PARAFAC model was built for each triplicate of each sample, from three-way arrays formed by 436 emission wavelengths, 7 excitation wavelengths and 5 measurements (sample plus 4 additions). In all the cases, the models were built with three factors and explained more than 99.90% of the total variance. The obtained loadings were related to SA and two background interferences. The scores related to SA were used for a linear regression in the standard addition method. Good results were obtained for determinations in the SA concentration range from 3.0 to 24.0 μg ml⁻¹, providing errors of prediction between 0.7 and 6.3%.     

Evaluation of a tungsten coil atomization-laser-induced fluorescence detection approach for trace elemental analysis

The analytical utility of a tungsten (W)-coil atomization-laser-induced fluorescence (LIF) approach has been evaluated for trace level measurements of elemental chromium (Cr), arsenic (As), selenium (Se), antimony (Sb), lead (Pb), tin (Sn), copper (Cu), thallium (Tl), indium (In), cadmium (Cd), zinc (Zn) and mercury (Hg). Measurements of As, Cr, In, Se, Sb, Pb, Tl, and Sn were performed by laser-induced fluorescence using a single dye laser operating near 460 nm whose output was converted by frequency doubling and stimulated Raman scattering to wavelengths ranging from 196 to 286 nm for atomic excitation. Absolute limits of detection (LODs) of 1, 0.3, 0.3, 0.2, 1, 6, 1, 0.2 and 0.8 pg and concentration LODs of 100, 30, 30, 20, 100, 600, 100, 20, and 80 pg/mL were achieved for As, Se, Sb, Sn, In, Cu, Cr, Pb and Tl, respectively. Determinations of Hg, Pb, Zn and Cd were performed using two-color excitation approaches and resulted in absolute LODs of 2, 30, 5 and 0.6 pg, respectively, and concentration LODs of 200, 3000, 500 and 60 pg/mL, respectively. The sensitivities achieved by the W-coil LIF approaches compare well with those reported by W-coil atomic absorption spectrometry, graphite furnace atomic absorption spectrometry, and graphite furnace electrothermal atomization-LIF approaches. The accuracy of the approach was verified through the analysis of a multielement reference solution containing Sb, Pb and Tl which each had certified performance acceptance limits of 19.6-20.4 μg/mL. The determined concentrations were 20.05 ± 2.60, 20.70 ± 2.27 and 20.60 ± 2.46 μg/mL, for Sb, Pb and Tl, respectively. The results demonstrate that W-coil LIF provides good analytical performance for trace analyses due to its high sensitivity, linearity, and capability to measure multiple elements using a single tunable laser and suggest that the development of portable W-coil LIF instrumentation using compact, solid-state lasers is feasible.     

基于碳点荧光恢复法测定生物巯基化合物

碳点(CDots)是一种新型荧光纳米材料,Cu2+可以有效猝灭其荧光;而当有生物巯基化合物存在时,碳点-Cu2+体系的荧光可以恢复.基于此原理,我们成功地构建了检测生物体内总巯基化合物的新方法.该方法具有很好的选择性,常见氨基酸和金属离子对谷胱甘肽(GSH)、半胱氨酸(Cys)和高半胱氨酸(Hcy)的检测无影响.最佳实验条件下,谷胱甘肽、半胱氨酸、高半胱氨酸的浓度在6.0×10-6mol/L～1.0×10-4mol/L与相对荧光强度呈线性,R>0.996,检出限为2.0×10-6mol/L.该体系成功用于血清样品中总巯基化合物的检测.     

Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.     

Bisquare robust polynomial fitting method for dimple distortion minimization in surface quality analysis

In this paper, the bisquare robust polynomial fitting (BSRPF) method for surface texture features assessment was proposed. Plateau-honed cylinder liner bronze surfaces with additionally burnished microcavities (defined as dimple, holes, and/or cavities) were taken into consideration. Analyzed details were measured with stylus (Talyscan 150) or white light interferometer (Talysurf CCI Lite) methods. It was assumed that application of commonly used (in surface contact and noncontact measurement technology) polynomial fitting procedures did not allow to extract required features; distortion of microreservoir dimples for oil consumption assessment was falsely estimated when valleys were near edge or on the edge located. Application of bisquare robust polynomial fitting method caused a minimization of microflattens of dimples irrespective of valley distribution; the biggest degree of polynomial fitting method applied, the minimal distortions of oil reservoirs were noticed. The valley size (depth and width) and valley-to-valley distance were also taken into account.     

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.     

A data treatment method for detecting fluorescence anisotropy peaks in capillary electropherograms

A data treatment method is presented to detect fluorescence anisotropy (FA) peaks in capillary electrophoresis electropherograms. The data treatment method converts plots of fluorescence anisotropy vs. time that contain no peaks that are distinguishable from the noise of the anisotropy background into plots that show distinct fluorescence anisotropy peaks. The method was demonstrated using laser-induced fluorescence anisotropy data from individual Aβ (1–42) aggregates separated using capillary electrophoresis. Applying this data treatment method enabled the detection of anisotropy peaks for individual Aβ aggregate fluorescence peaks that were not observed prior to the data treatment method. The data treatment method is not specifically designed for Aβ aggregate analysis or capillary electrophoresis, and it should be applicable to other applications and other separation methods with FA detection.     

A fluorescence anisotropy method for measuring protein concentration in complex cell culture media

The rapid, quantitative analysis of the complex cell culture media used in biopharmaceutical manufacturing is of critical importance. Requirements for cell culture media composition profiling, or changes in specific analyte concentrations (e.g. amino acids in the media or product protein in the bioprocess broth) often necessitate the use of complicated analytical methods and extensive sample handling. Rapid spectroscopic methods like multi-dimensional fluorescence (MDF) spectroscopy have been successfully applied for the routine determination of compositional changes in cell culture media and bioprocess broths. Quantifying macromolecules in cell culture media is a specific challenge as there is a need to implement measurements rapidly on the prepared media. However, the use of standard fluorescence spectroscopy is complicated by the emission overlap from many media components. Here, we demonstrate how combining anisotropy measurements with standard total synchronous fluorescence spectroscopy (TSFS) provides a rapid, accurate quantitation method for cell culture media. Anisotropy provides emission resolution between large and small fluorophores while TSFS provides a robust measurement space. Model cell culture media was prepared using yeastolate (2.5 mg mL^–1) spiked with bovine serum albumin (0 to 5 mg mL^–1). Using this method, protein emission is clearly discriminated from background yeastolate emission, allowing for accurate bovine serum albumin (BSA) quantification over a 0.1 to 4.0 mg mL^–1range with a limit of detection (LOD) of 13.8 μg mL^–1.     

A dual-responsive fluorescence method for the detection of clenbuterol based on BSA-protected gold nanoclusters

The illegal feeding of clenbuterol (CLB) to domestic animals and the potential harm of it to human health lead an urgent requirement for the efficient detection of CLB, especially in the edible meat. In this paper we reported a new fluorescence method for the detection of trace amount of CLB by using the BSA-protected gold nanoclusters (AuNCs@BSA). Under the excitation of either 280 or 500 nm the emission of AuNCs@BSA was quenched obviously by diazotized CLB, supplying a dual-responsive fluorescence method to detect CLB in aqueous solution. In addition, the linear response of the fluorescence intensity of AuNCs@BSA to diazotized CLB allowed the quantitative detection of CLB in a range of 4.0 nM–300 μM upon excitation at two wavelength, and the limit of detection for CLB was 3.0 nM upon 280 nm excitation and 1.6 nM upon 500 nm excitation, respectively. In addition, the dual-responsive mechanism of AuNCs@BSA to CLB was investigated in detail by using several CLB analogues and reference compounds. Particularly, the proposed method was successfully applied to detect CLB in pork mince and the results were validated well by HPLC, illustrating it could be used as a reliable, rapid, and cost-effective technique for the determination of CLB residues in real samples.