Quantitative Analysis Of Complex Samples Research Articles

Firefly Interval Selection Combined With Extreme Learning Machine for Spectral Quantification of Complex Samples

ABSTRACTFirefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA‐ELM is compared with full‐spectrum PLS, ELM, genetic algorithm‐ELM (GA‐ELM), and particle swarm optimization‐ELM (PSO‐ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near‐infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA‐ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples.

Variational mode decomposition unfolded partial least squares regression for ultraviolet–visible spectral analysis of edible oil blend, fuel oil and aqueous samples

To exploit the abundant information embedded amongst spectra, a novel regression model, named variational mode decomposition unfolded partial least squares regression (VMD-UPLSR), was developed for ultraviolet–visible (UV–Vis) spectral analysis of complex samples. In the method, variational mode decomposition (VMD) is firstly used to decompose each spectrum into K mode components (uk) with different frequencies. Then the mode components are unfolded to an extended matrix in variable dimensions. Finally, partial least squares regression (PLSR) is used to build a quantitative model between the extended matrix and target values. The performance of VMD-UPLSR is verified by three UV–Vis spectral datasets of edible oil blend, fuel oil and aqueous samples for quantification of sunflower oil, polyaromatics and chromium ion, respectively. The contents of the three analytes are in the ranges of 0–98.99 % (m/m), 0–0.9 % (V/V) and 5.01–73.12 mg/L, respectively. Root mean square error of prediction (RMSEP) of VMD-UPLSR is 6.96, 0.057 and 4.19 for edible oil blend, fuel oil and aqueous solution datasets, respectively. Compared with single PLSR and uk-PLSR, results show that the proposed method displays the best prediction accuracy in the three datasets. Therefore, VMD-UPLSR can be a valuable alternative method for the quantitative analysis of complex samples.

Absolute Metabolite Quantification Using Pure Shift NMR: Toward Quantitative Metabolic Profiling of Aqueous Biological Samples.

Accurate quantification of metabolites by nuclear magnetic resonance (NMR) is of prime importance in the field of health sciences for understanding the metabolic pathways of the investigated system, to address the mechanisms of action of diseases, and improving their diagnosis, treatment, and prognosis. Unfortunately, the absolute quantitative analysis of complex samples is still limited by sensitivity and resolution issues that are intrinsic to this technique. Ultrahigh-resolution pure shift methods have especially shown to be suitable for interpreting mixtures of metabolites in biological samples. Here, we introduce a robust analytical protocol based on the use of a pure shift library of calibration reference spectra to fit the fingerprint of each metabolite of interest and determine its concentration. The approach based on the SAPPHIRE pulse sequence enhanced with a block for solvent suppression has been validated through the results of a series of model mixtures, exhibiting excellent trueness (slope values in the range of 0.93-1.02) and linearity (R2 > 0.996) in a total time (a few hours) that is fully compatible with metabolomics studies. Furthermore, we have successfully applied our method to determine the absolute metabolite concentrations in a lymphoma extracellular medium, which improves metabolomic protocols reported to date by providing a quantitative and highly resolved vision of metabolic processes at play.

A selective ensemble preprocessing strategy for near-infrared spectral quantitative analysis of complex samples

Preprocessing of raw near-infrared (NIR) spectra is typically required prior to multivariate calibration since the measured spectra of complex samples are often subject to overwhelming background, light scattering, varying noises and other unexpected factors. Various preprocessing methods have been developed aimed at removing or reducing the interference of these effects. However, it is usually difficult to determine the best preprocessing method for a given data. Instead of selecting the best one, a selective ensemble preprocessing strategy is proposed for NIR spectral quantitative analysis. Firstly, numerous preprocessing methods and their combinations are obtained by full factorial design in order of baseline correction, scattering correction, smoothing and scaling. Then partial least squares (PLS) model is built for each preprocessing method. The models which have better predictions than PLS are selected and their predictions are averaged as the final prediction. The performance of the proposed method was tested with corn, blood and edible blend oil samples. Results demonstrate that the selective ensemble preprocessing method can give comparative or even better results than the traditional selected best preprocessing method. Therefore, in the framework of selective ensemble preprocessing, more accurate calibration can be obtained without searching the best preprocessing method.

Multi-way chromatographic calibration—A review

This review discusses recent advances in both theory and experiment regarding multi-way chromatographic calibration. The focus is directed towards the chemometric processing of multi-way data arrays in which one or more instrumental modes describe the elution time behavior across liquid or gas chromatographic columns, and the remaining ones are of spectroscopic nature, e.g. UV–vis absorption, fluorescence emission (either as vectors or matrices) or mass spectrometry. Successful multi-way data decomposition into profiles for the contributing components allows one to perform quantitative analysis of complex samples. Quantitative applications usually make appropriate use of the second-order advantage which is inherent to multi-way data processing, and reduce the complexity of the data to a virtual univariate calibration which is typical of classical analytical chemistry. A number of recent experimental works will be discussed to illustrate the above concepts.

Multi-energy calibration and sample fusion as alternatives for quantitative analysis of high silicon content samples by laser-induced breakdown spectrometry

Calibration aiming at quantitative analysis of complex samples is one of the most difficult issues in laser-induced breakdown spectroscopy (LIBS) due to matrix interferences, heterogeneity, and sample particle size effects.

Miniaturized Sample Preparation for Transmission Electron Microscopy

Due to recent technological progress, cryo-electron microscopy (cryo-EM) is rapidly becoming a standard method for the structural analysis of protein complexes to atomic resolution. However, protein isolation techniques and sample preparation methods for EM remain a bottleneck. A relatively small number (100,000 to a few million) of individual protein particles need to be imaged for the high-resolution analysis of proteins by the single particle EM approach, making miniaturized sample handling techniques and microfluidic principles feasible. A miniaturized, paper-blotting-free EM grid preparation method for sample pre-conditioning, EM grid priming and post processing that only consumes nanoliter-volumes of sample is presented. The method uses a dispensing system with sub-nanoliter precision to control liquid uptake and EM grid priming, a platform to control the grid temperature thereby determining the relative humidity above the EM grid, and a pick-and-plunge-mechanism for sample vitrification. For cryo-EM, an EM grid is placed on the temperature-controlled stage and the sample is aspirated into a capillary. The capillary tip is positioned in proximity to the grid surface, the grid is loaded with the sample and excess is re-aspirated into the microcapillary. Subsequently, the sample film is stabilized and slightly thinned by controlled water evaporation regulated by the offset of the platform temperature relative to the dew-point. At a given point the pick-and-plunge mechanism is triggered, rapidly transferring the primed EM grid into liquid ethane for sample vitrification. Alternatively, sample-conditioning methods are available to prepare nanoliter-sized sample volumes for negative stain (NS) EM. The methodologies greatly reduce sample consumption and avoid approaches potentially harmful to proteins, such as the filter paper blotting used in conventional methods. Furthermore, the minuscule amount of sample required allows novel experimental strategies, such as fast sample conditioning, combination with single-cell lysis for "visual proteomics," or "lossless" total sample preparation for quantitative analysis of complex samples.

Miniaturized Sample Preparation for Transmission Electron Microscopy.

Due to recent technological progress, cryo-electron microscopy (cryo-EM) is rapidly becoming a standard method for the structural analysis of protein complexes to atomic resolution. However, protein isolation techniques and sample preparation methods for EM remain a bottleneck. A relatively small number (100,000 to a few million) of individual protein particles need to be imaged for the high-resolution analysis of proteins by the single particle EM approach, making miniaturized sample handling techniques and microfluidic principles feasible.A miniaturized, paper-blotting-free EM grid preparation method for sample pre-conditioning, EM grid priming and post processing that only consumes nanoliter-volumes of sample is presented. The method uses a dispensing system with sub-nanoliter precision to control liquid uptake and EM grid priming, a platform to control the grid temperature thereby determining the relative humidity above the EM grid, and a pick-and-plunge-mechanism for sample vitrification. For cryo-EM, an EM grid is placed on the temperature-controlled stage and the sample is aspirated into a capillary. The capillary tip is positioned in proximity to the grid surface, the grid is loaded with the sample and excess is re-aspirated into the microcapillary. Subsequently, the sample film is stabilized and slightly thinned by controlled water evaporation regulated by the offset of the platform temperature relative to the dew-point. At a given point the pick-and-plunge mechanism is triggered, rapidly transferring the primed EM grid into liquid ethane for sample vitrification. Alternatively, sample-conditioning methods are available to prepare nanoliter-sized sample volumes for negative stain (NS) EM.The methodologies greatly reduce sample consumption and avoid approaches potentially harmful to proteins, such as the filter paper blotting used in conventional methods. Furthermore, the minuscule amount of sample required allows novel experimental strategies, such as fast sample conditioning, combination with single-cell lysis for "visual proteomics," or "lossless" total sample preparation for quantitative analysis of complex samples.

Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate

Near infrared diffuse reflectance spectroscopy (NIRDRS) has gained wide attention due to its convenience for rapid quantitative analysis of complex samples. A method for rapid analysis of triglycerides in human serum using NIRDRS with silver mirror as the substrate is developed. Due to the even and high reflectance of the silver mirror, the spectral response is enhanced and the background interference is reduced. Furthermore, both linear and nonlinear modeling strategies were investigated adopting the partial least squares (PLS) and least squares support vector regression (LS-SVR), continuous wavelet transform (CWT) was used for spectral preprocessing, and variable selection was tried using Monte Carlo uninformative variable elimination (MC-UVE), randomization test (RT) and competitive adaptive reweighted sampling (CARS) for optimization the models. The results show that the determination coefficient (R) between the predicted and reference concentration is 0.9624 and the root mean squared error of prediction (RMSEP) is 0.21. The maximum deviation of the prediction results is as low as 0.473mmol/L. The proposed method may provide an alternative method for routine analysis of serum triglycerides in clinical applications.

A Spectral Shift-Based Electrochemiluminescence Sensor for Hydrogen Sulfide.

Classic electrochemiluminescence (ECL) assays relying on the change in luminescence intensity face a challenge in the quantitative analysis of complex samples. Here, we report the design and implementation of a new sensing strategy, using the maximum luminescence wavelength (λmax) shift as the readout to achieve quantitative detection. This approach includes an ECL luminophore (RuSiO2@GO) and a H2S-sensitive inner filter absorber (CouMC). The absorbance of CouMC illustrates a dependence on the H2S concentration, which induces a change in the maximum luminescence wavelength (Δλmax) of the ECL luminophore. Both experimental and simulated results suggest that the spectral shift of ECL effectively avoids the interference of the total luminescence intensity fluctuations, enabling a highly reliable quantitative analysis. This spectral shift-based ECL assay strategy offers a wide application potential by extending types of ECL luminophores and absorptive chemodosimeters, based on an inner filter effect.

Ensemble calibration for the spectral quantitative analysis of complex samples

AbstractEnsemble strategies have gained increasing attention in multivariate calibration for quantitative analysis of complex samples. The aim of ensemble calibration is to obtain a more accurate, stable, and robust prediction by combining the predictions of multiple submodels. The generation and calibration of the training subsets, as well as the integration of the submodels, are three keys to the success of ensemble calibration. Many training subset generating and submodel integrating strategies have been developed to form numerous ensemble calibration methods for improving the performance of the basic calibration method. This contribution focuses on the recent ensemble strategies in relation to calibration, especially the ensemble modeling for quantitative analysis of complex samples. The limitations and perspectives of ensemble strategies are also discussed.

Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples

Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias. To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples. Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM.

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.

Spectral quantitative analysis of complex samples based on the extreme learning machine

A novel algorithm called the extreme learning machine is introduced for the spectral quantitative analysis of complex samples, which enhances predictive performance.

Mini 12, Miniature Mass Spectrometer for Clinical and Other Applications—Introduction and Characterization

A benchtop miniature mass spectrometer system, Mini 12, with ambient ionization source and tandem mass spectrometry capabilities has been developed and characterized. This instrument was developed as a self-contained system to produce quantitative results for unprocessed samples of small volumes including nonvolatile analytes. The ion processing system, vacuum system, and control system are detailed. An integrated sample loading system facilitates automated operation. A user interface has been developed to acquire and to interpret analytical results for personnel who have limited mass spectrometry knowledge. Peak widths of Δm/z 0.6 Th (full width at half-maximum) and a mass range of up to m/z 900 are demonstrated with the rectilinear ion trap mass analyzer. Multistage experiments up to MS5 are accomplished. Consumable cartridges have been designed for use in ambient paper spray ionization, and the recently developed extraction spray ionization method has been employed to improve the quantitative performance. Monitoring of trace-levels of chemicals in therapeutic drugs, as well as in food safety and environmental protection operations is demonstrated. Dual MS/MS scans are implemented to obtain the intensities of the fragment ions from the analyte and its internal standard, and the ratio is used in quantitative analysis of complex samples. Limits of quantitation (LOQ) of 7.5 ng/mL, with relative standard deviations below 10%, have been obtained for selected therapeutic drugs in whole blood throughout their individual therapeutic ranges.

Quantitative structure–ion intensity relationship strategy to the prediction of absolute levels without authentic standards

The lack of authentic standards represents a major bottleneck in the quantitative analysis of complex samples. Here we propose a quantitative structure and ionization intensity relationship (QSIIR) approach to predict the absolute levels of compounds in complex matrixes. An absolute quantitative method for simultaneous quantification of 25 organic acids was firstly developed and validated. Napierian logarithm (LN) of the relative slope rate derived from the calibration curves was applied as an indicator of the relative ionization intensity factor (RIIF) and serves as the dependent variable for building a QSIIR model via a multiple linear regression (MLR) approach. Five independent variables representing for hydrogen bond acidity, HOMO energy, the number of hydrogen bond donating group, the ratio of organic phase, and the polar solvent accessible surface area were found as the dominant contributors to the RIIF of organic acids. This QSIIR model was validated to be accurate and robust, with the correlation coefficients (R2), R2 adjusted, and R2 prediction at 0.945, 0.925, and 0.89, respectively. The deviation of accuracy between the predicted and experimental value in analyzing a real complex sample was less than 20% in most cases (15/18). Furthermore, the high adaptability of this model was validated one year later in another LC/MS system. The QSIIR approach is expected to provide better understanding of quantitative structure and ionization efficiency relationship of analogous compounds, and also to be useful in predicting the absolute levels of analogous analytes in complex mixtures.

Liquid chromatography-matrix-assisted laser desorption/ionization mass spectrometric imaging with sprayed matrix for improved sensitivity, reproducibility and quantitation

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) has been employed as a detection method for both capillary electrophoresis (CE)-MALDI and liquid chromatography (LC)-MALDI analyses. Based on our previous studies, here we report a new interface to couple LC with MSI by employing an automated matrix sprayer. The LC trace is directly collected on a ground stainless steel MALDI plate and dried. The matrix is sprayed on the MALDI plate using a programmable matrix sprayer. With the highly uniform matrix layers produced from the sprayer, the MS image signal quality is significantly improved with enhanced signal-to-noise ratios for analyte peaks. With the programmable matrix application and imaging MS data acquisition, the new LC-MSI platform exhibits highly stable and reproducible performance. A total of 87 bovine serum albumin (BSA) tryptic peptides and 295 putative neuropeptides from blue crab pericardial organs have been observed with LC-MSI analysis, exhibiting better performance in terms of peptide coverage than regular LC-MALDI with discrete spot collection and our previously reported LC-MSI interface with the matrix being delivered by a capillary. In addition to relative quantitation with isotopic labeling as we have previously demonstrated, we performed the first absolute quantitation using the new LC-MSI platform and obtained accurate quantitation results for neuropeptides, indicating great potential for quantitative analysis of complex samples.

Factors that affect quantification of diode array data in comprehensive two-dimensional liquid chromatography using chemometric data analysis

There has been a tremendous increase in research on comprehensive two dimensional LC (LC × LC); however, to date, the central analytical issue, quantification, has received only minimal attention. It is vital to the further development of LC × LC that a greater understanding of the specific factors affecting peak quantification in LC × LC be attained. This work focuses on the following factors: data complexity, retention time shifting, dynamic range issues, chromatographic and spectral peak overlap and difficulties related to background signal removal. The above mentioned factors that affect peak quantification are investigated using fourteen replicate analyses of a urine sample, representing the effects of such factors when analyzing samples in complex matrices. We demonstrate that quantification of LC × LC data is improved following implementation of chemometric techniques that minimized the deleterious effects on quantification due to chromatographically overlapped peaks, retention time shifting and background signal interference. The chemometrically resolved data shows a 2.5-fold increase in precision of quantification over the quantification of the raw data. It is also demonstrated that the method quantifies sixteen peaks that were not visually evident prior to chemometric analysis. The purpose of this paper is to determine the impact of these issues on the effectiveness of LC × LC as a technique for the quantitative analysis of complex samples.

Comparative analysis of CE‐SSCP to standard RFLP‐CE‐FLA method in quantification of known viral variants within an RNA virus quasispecies

RNA viruses display the highest replication error rate in our biosphere, leading to highly diverse viral populations termed quasispecies. The gold standard method for detection and quantification of variants in a quasispecies is cloning and sequencing, but it is expensive, laborious and time consuming. Therefore, other mutation detection approaches, including SSCP, are often used. In this study, we demonstrate development and the usage of a CE-SSCP method for quantification of two nearly identical viral variants in heterogenic population of a mumps virus strain and its comparison to RFLP-CE-fragment length analysis (RFLP-CE-FLA). Analyzed PCR fragments were of the same size (245 bp) with one difference in their nucleotide sequence. The limit of detection of both methods was at 5% of the minor variant. When PCR amplicons of the two variants were pooled, methods' results were very similar. On the contrary, the quantification results of samples in which variants were mixed prior to PCR showed substantial difference between the two methods. Our results indicate that although both methods can be used for detection and monitoring of a specific mutation within a viral population, caution should be taken when quantitative analysis of complex samples is based solely on results of one method.

A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples.