Total Synchronous Fluorescence Spectroscopy Research Articles

Rapid, simultaneous and non-destructive determination of multiple adulterants in Panax notoginseng powder by front-face total synchronous fluorescence spectroscopy

The authentication of traditional herbal medicines in powder form is of great significance, as they are always of high values but vulnerable to adulteration. Based on the distinct fluorescence of protein tryptophan, phenolic acids and flavonoids, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of Panax notoginseng powder (PP) adulterated with the powder of rhizoma curcumae (CP), maize flour (MF) and whole wheat flour (WF). For either single or multiple adulterants in the range of 5–40% w/w, prediction models were built based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The constructed PLS2 models simultaneously predicted the contents of multiple adulterants in PP and gave suitable results, with most of the determination coefficients of prediction (Rp2) >0.9, the root mean square error of prediction (RMSEP) no >4% and residual predictive deviation (RPD) >2. The limits of detections (LODs) were 12.0, 9.1 and 7.6% for CP, MF and WF, respectively. All the relative prediction errors for simulated blind samples were between −22% and + 23%. FFSFS offers a novel alternative to the authentication of powdered herbal plants.

Investigations of potential ionic liquid phases for chromatographic processes using spectroscopic and thermal techniques

The thermal degradation of ionic liquids 1-octyl-3-methyl imidazolium trifluoromethanesulfonate ([OMIM][TfO]) and 1-octyl-3-methyl imidazolium bis(trifluoromethylsulfonyl)imide ([OMIM][Tf2N]) were investigated using spectroscopic and thermal techniques to predict gas chromatographic phase stability. For differential scanning calorimetry, lowering the heating rate from 10 to 2 °C/min decreased the decomposition temperature of the ionic liquids ∼ 23 °C for [OMIM][TfO] and ∼ 13 °C for [OMIM][Tf2N]. In addition, lowering the heating rate gave better resolution and showed anion thermal decomposition occurred several degrees before cation decomposition for the ionic liquids. Experimental measurements showed that decomposition of the ionic liquids was indicated at lower temperatures when using spectroscopic methods compared to thermal scanning methods. Total synchronous fluorescence spectroscopy was used to monitor the formation of ionic liquid degradation products during thermal stress experiments. Decomposition temperatures determined by differential scanning calorimetry were approximately 140 °C higher compared to measurements by fluorescence spectroscopy.

Vision transformer for quality identification of sesame oil with stereoscopic fluorescence spectrum image

Sesame oil (SO), as a high-priced edible oil, is often counterfeited and adulterated. A new method for SO quality identification using Vision Transformer (ViT) network based on stereoscopic images of Excitation-emission matrix fluorescence (EEMF) and Total synchronous fluorescence (TSyF) spectroscopy was proposed. The basic samples including pure, counterfeit and adulterated SOs were characterized by fluorescence spectroscopy. A data augmentation strategy including linear interpolation, shift and noise injection was selected for few sample learning. All fluorescence spectral data were visualized as stereoscopic images with rich spectral characteristics. The ViT network architecture based on attention mechanism was designed and trained to establish four SO quality identification models. The macro averages of precision, recall and F1-score on the validation set were greater than 0.99. The values of these indicators on the test samples were equal to one. In conclusion, deep learning based on ViT using stereoscopic fluorescence spectrum image provided a new method for sesame oil identification.

Development of a rapid polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a model bioreactor broth.

Protein quantification during bioprocess monitoring is essential for biopharmaceutical manufacturing and is complicated by the complex chemical composition of the bioreactor broth. Here we present the early-stage development and optimization of a polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a hydrolysate-protein model (mimics clarified bioreactor broth samples) using a standard benchtop laboratory fluorometer. We used UV transmitting polarizers to provide wider range pTSFS spectra for screening of the four different TSFS spectra generated by the measurement: parallel (||), perpendicular (⊥), unpolarized (T) intensity spectra and anisotropy maps. TSFS|| (parallel polarized) measurements were the best for protein quantification compared to standard unpolarized measurements and the Bradford assay. This was because TSFS|| spectra had a better analyte signal to noise ratio (SNR), due to the anisotropy of protein emission. This meant that protein signals were better resolved from the background emission of small molecule fluorophores in the cell culture media. SNR of >5000 was achieved for concentrations of bovine serum albumin/yeastolate 1.2/10 g L-1 with TSFS|| . Optimization using genetic algorithm and interval partial least squares based variable selection enabled reduction of spectral resolution and number of excitation wavelengths required without degrading performance. This enables fast (<3.5 min) online/at-line measurements, and the method had an LOD of 0.18 g L-1 and high accuracy with a predictive error of <9%.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique.

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

Ozonation of natural organic matter and aquatic humic substances: the effects of ozone on the structural characteristics and subsequent trihalomethane formation potential

Abstract The effect of ozonation on the structural and chemical characteristics of natural organic matter (NOM) and its isolated humic fractions, humic acid (HA) and fulvic acid, were studied using Fourier transform infrared coupled to attenuated total reflectance (FTIR-ATR), ultraviolet/visible (UV/Vis) spectroscopy, and synchronous scanning fluorescence (SSF) spectroscopy. The results were linked to the effect of ozonation on trihalomethane formation potential (THMfp) reduction for water standards with high THM precursors. Results showed that ozonation at a dose of 1 mg ozone/mg dissolved organic carbon (DOC) was capable of reducing DOC, UV absorbance at 254 nm (UV254), and THMfp by up to 42%, 95%, and 89% for the HA water standard, respectively. The study of UV/Vis, FTIR-ATR, and SSF revealed trends showing that ozone can alter the composition of DOC in the water standards, causing a significant reduction in aromaticity. The reduction of UV254 for each ozonated sample also affirms that ozone mainly targets aromatic moieties contained in NOM. FTIR-ATR results showed that the reduction of unsaturated functional groups, including aromatic rings and C = C bonds in the water standards tested, were the main components impacted by ozone application. SSF results also revealed that ozonation decreases the fluorescence intensity of the maximum peak – as well as the whole spectra.

Using polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) with PARAFAC analysis for characterizing intrinsic protein emission

Using polarized Excitation Emission Matrix (pEEM) spectroscopy to measure the intrinsic emission of proteins offers a potentially useful methodology for a wide variety of potential applications. However, the presence of Rayleigh light scatter causes significant problems when attempting to use Parallel Factor (PARAFAC) and for anisotropy calculations. The use of polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) can minimize Rayleigh scatter and avoid the use of complex data correction methods. Here, we investigated for the first time the use of pTSFS and PARAFAC to analyze the intrinsic emission of an Immunoglobulin (IgG) type protein in its native state. To enable PARAFAC analysis however, TSFS data (which is not trilinear) must first be transformed into an EEM like layout (t-EEM) and this generated a region with no experimentally acquired information (<8%). Here we critically evaluated several data handling methods and determined that interpolation was the best solution for dealing with the spectral regions with no experimentally acquired data at the blue edge of the emission.There were only subtle structural changes measured over the temperature range (15–35 °C) analyzed and PARAFAC only resolved two emitting components. A Trp emission component (average signal from all Trp present) which represented >92% of the explained variance, and a much weaker, mostly Tyr related emission with ~3% of the explained variance. The recovery of this Tyr component was only possible because pTSFS measurements were less contaminated by Rayleigh scattering. Changes in Tyr-to-Trp energy transfer rates caused by thermal motion were detected as an increase in Tyr contribution, which could not be resolved with the equivalent pEEM measurements due to light scatter contamination. The increased selectivity, sensitivity, and reproducibility of pTSFS measurements shows that this is a better option than pEEM for fluorescence emission based monitoring of protein structural change or lot-to-lot variance of IgG type proteins.

Application of Akaike information criterion assisted probabilistic latent semantic analysis on non-trilinear total synchronous fluorescence spectroscopic data sets: Automatizing fluorescence based multicomponent mixture analysis

The lack of trilinear structure is known to limit the application of chemometric techniques on the total synchronous fluorescence spectroscopy (TSFS) data sets. To overcome this limitation, the present work successfully proposes application of Akaike information criterion (AIC) assisted probabilistic latent semantic analysis (pLSA) algorithm on TSFS data sets. The present work also discusses various practical and theoretical aspects that need to be considered while applying AIC assisted pLSA algorithm on TSFS data sets.

Processing Excitation-Emission Matrix Fluorescence and Total Synchronous Fluorescence Spectroscopy Data Sets with Constraint Randomised Non-negative Factor Analysis: a Novel Fluorescence Based Analytical Procedure to Analyse the Multifluorophoric Mixtures.

The present work successfully shows the application of novel chemometric approach constraint randomised non-negative factor analysis (CRNNFA) for the analyses of the composite multidimensional fluorescence data sets. The CRNNFA involves the initialisation of the spectral variables in a constraint fashion thus ensures that algorithm does not wander with chemically and spectro-chemically irrelevant variables. The CRNNFA approach does not require that there must be pure variables for each fluorophores of the multifluorophoric mixture. One of the biggest advantages of CRNNFA is that it does not involve any convergence criteria thus circumventing the premature convergence of the algorithm. The CRNNFA achieves the termination only when the iteration limit is reached. The CRNNFA analysis s carried out under the non-negativity constraints therefore the mathematically retrieved profiles can easily be compared with those obtained experimentally. In the present work, both trilinear as well as non-trilinear multidimensional data sets are subjected to CRNNFA to validate its applicability. Excitation emission matrix fluorescence (EEMF) spectral profiles of Catechol, Hydroquinone, Indole and Tryptophan mixtures is used as the source of trilinear data sets. Total synchronous fluorescence spectroscopy (TSFS) spectral profiles of Benzo[a] Pyrene, Chrysene and Pyrene mixtures are used as the source of non-trilinear data sets. The CRNNFA approach is found to work equally well with trilinear as well with non-trilinear data sets. Thus, CRNFFA clearly does not have any prerequisite in the data structure. The obtained results clearly shows that CRNNFA algorithm in combination with EEMF and TSFS data sets are potential analytical tool for the analysis of complex-multifluorophoric mixtures.

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures.

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.

Study on the Miscibility Behavior of Diesel–n-Butanol–Ethanol Blends and Fluorimetric Estimation of Diesel Fraction

This study focuses on developing a convenient method for quantification of ethanol in diesel–ethanol blend and extends the miscibility of diesel–ethanol blends using n-butanol as a third fuel component. Mutual solubility of n-butanol with diesel and ethanol in all proportions makes it a potential fuel additive/cosolvent to overcome the solubility problem. The amount of additive required at different temperatures was evaluated with temperature dependent phase diagrams at temperatures ranging from 10 to 40 °C. Four calibration models were made for both diesel–ethanol and diesel–n-butanol–ethanol blends using a combination of total synchronous fluorescence spectroscopy (TSFS) with two chemometric techniques, unfolded partial least square (unfolded PLS) and multivariate curve resolution–alternating least squares (MCR–ALS). The root mean square error (RMSE) of calibration, prediction, and square of the correlation coefficient (R2) were used to measure model performance in validation data set and reference data...

Parallel factor (PARAFAC) analysis on total synchronous fluorescence spectroscopy (TSFS) data sets in excitation–emission matrix fluorescence (EEMF) layout: Certain practical aspects

In a recently developed procedure for parallel factor (PARAFAC) analysis of total synchronous fluorescence spectroscopy (TSFS), the issue of no-trilinearity of TSFS data set was addressed by representing the TSFS data in excitation–emission matrix fluorescence (EEMF) layout which ensures the trilinearity to data sets “a must” for PARAFAC analysis. This representation leads to generation of significantly large number of variables, which do not contain any experimentally acquired fluorescence information. It is essential that such variables be handled properly before subjecting TSFS data in EEMF layout to PARAFAC analysis. Based on our understanding of mechanism by which PARAFAC analysis on TSFS data in EEMF layout works, we used three possible ways: (i) assigning a value of zero, (ii) assigning missing (NaN) values, and (iii) combination of zero and NaN values to handle such variables. We evaluated each of these three possibilities and compared the outcomes of PARAFAC analysis with respect to two important parameters: (i) proximity between the actual and retrieved TSFS profile of all the three fluorophores and (ii) time taken for the convergence of PARAFAC algorithm. The obtained results of PARAFAC analyses on TSFS data in EEMF layout showed that better analytical results are obtained if we set all the variables with no experimentally acquired information to missing (NaN) values, though the computational time is significantly high. PARAFAC analysis tends to converge prematurely when a value of zero was assigned to all the variables in EEMF layout that do not contain any experimentally acquired information. The present work also showed that by using the combination of zero and missing values it is possible to optimize the computational time and retrieve PARAFAC separated TSFS profile from EEMF layout with reasonable purity.

Multivariate curve resolution alternating least square (MCR-ALS) analysis on total synchronous fluorescence spectroscopy (TSFS) data sets: Comparing certain ways of arranging TSFS-based three-way array

Total synchronous fluorescence spectroscopy (TSFS)-based three-way array sample×excitation wavelength×Δλ has inherently complex data structure. Multivariate curve resolution alternating least square (MCR-ALS) analysis is a soft mathematical modelling technique that can be used to retrieve pure spectral and contribution profile of the fluorophores of a multifluorophoric mixture provided it is applied to bilinear data sets. In the present work, possibilities of generating bilinear two-way arrays by (i) unfolding and (ii) augmenting the TSFS-based three-way array were studied. Bilinear characteristics of the obtained two-way arrays were tested by subjecting them to MCR-ALS analysis. It was found that lack of analytical–mathematical relationship between spectral profile along the excitation wavelength mode and the offset mode do not allow the application of MCR-ALS analysis on augmented TSFS data set. Moreover, the augmentation procedure and chemical-mathematical rank of the subsequently obtained data sets were found to be ‘not independent’ of each other. Two-way array sample×(excitation wavelength×Δλ) obtained by unfolding TSFS-based three-way array along the sample mode was found to have symmetrical structure, i.e., both sample mode and spectral mode have equal mathematical and chemical rank making them highly attuned to MCR-ALS analysis.

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.

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 (2.5mgmL–1) spiked with bovine serum albumin (0 to 5mgmL–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 4.0mgmL–1range with a limit of detection (LOD) of 13.8μgmL–1.

Achieving second order advantage with multi-way partial least squares and residual bi-linearization with total synchronous fluorescence data of monohydroxy–polycyclic aromatic hydrocarbons in urine samples

An attractive approach to handle matrix interference in samples of unknown composition is to generate second- or higher-order data formats and process them with appropriate chemometric algorithms. Several strategies exist to generate high-order data in fluorescence spectroscopy, including wavelength time matrices, excitation–emission matrices and time-resolved excitation–emission matrices. This article tackles a different aspect of generating high-order fluorescence data as it focuses on total synchronous fluorescence spectroscopy. This approach refers to recording synchronous fluorescence spectra at various wavelength offsets. Analogous to the concept of an excitation–emission data format, total synchronous data arrays fit into the category of second-order data. The main difference between them is the non-bilinear behavior of synchronous fluorescence data. Synchronous spectral profiles change with the wavelength offset used for sample excitation. The work presented here reports the first application of total synchronous fluorescence spectroscopy to the analysis of monohydroxy-polycyclic aromatic hydrocarbons in urine samples of unknown composition. Matrix interference is appropriately handled by processing the data either with unfolded-partial least squares and multi-way partial least squares, both followed by residual bi-linearization.