Frontiers of two-dimensional correlation spectroscopy. Part 2. Perturbation methods, fields of applications, and types of analytical probes

Baseline matching preprocessing of temperature perturbation infrared spectra

An infrared spectrum baseline matching procedure that compensates for measurement drift and eliminates sloping baselines from sequentially acquired spectra is described. The theory underlying this procedure is provided and examples are given for three implementations based on infrared spectrum data sets containing at least 100 successively measured spectra. Consecutive spectra were acquired when the infrared beam contained: no sample, poly(styrene) powder, and a poly(styrene) film. The first two data sets consisted of 120 spectra and were used to characterize instrument reproducibility and identify short- and long-term measurement drifts. The 200 infrared spectra obtained while heating and then cooling a poly(styrene) film were subjected to baseline matching to reveal subtle temperature-dependent changes that are not evident when overlayed spectra are displayed. Baseline matching preprocessing is easily implemented on large numbers of similar spectra by using macro programming.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

Two-Dimensional (2D) Hybrid Method: Expanding 2D Correlation Spectroscopy (2D-COS) for Time Series Analysis

We present a concise report on the two-dimensional (2D) hybrid method, an innovative extension of 2D correlation spectroscopy, tailored for quasar light curve analysis. Addressing the challenge of discerning periodic variations against the background of intrinsic "red" noise fluctuations, this method employs cross-correlation of wavelet transform matrices to reveal distinct correlation patterns between underlying oscillations, offering new insights into quasar dynamics.

Composition and structure analysis of different depths in the stratum corneum using confocal Raman microscopy combined with two-dimensional correlation spectroscopy

The chemical composition and structure of the stratum corneum (SC) play a crucial role in the skin barrier function. Therefore, accurately determining the SC thickness and studying the changes in lipid and keratin structure and distribution within it are key aspects of skin barrier research. Currently, there are limited analytical tools and data analysis methods available for real-time and online studies of SC composition and structural changes. In this study, we focus on depth as a perturbation and employ confocal Raman microscopy combined with moving-window two-dimensional correlation spectroscopy (MW2D) technique to investigate the SC thickness. Additionally, we employ confocal Raman microscopy combined with perturbation-correlation moving-window two-dimensional correlation spectroscopy (PCMW2D) to precisely characterize the stratification of the SC. Furthermore, the two-dimensional correlation spectroscopy (2DCOS) method is utilized to examine the content of various conformations in the keratin secondary structure within the SC, as well as the subtle interrelationships between lipid and keratin structures.

Chemometric Combination of Ultrahigh Resolving Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy for a Structural Characterization of Lignin Compounds

In recent years, the potential of lignins as a resource for material-based applications has been highlighted in many scientific and nonscientific publications. But still, to date, a lack of detailed structural knowledge about this ultracomplex biopolymer undermines its great potential. The chemical complexity of lignin demands a combination of different, powerful analytical methods, in order to obtain these necessary information. In this paper, we demonstrate a multispectroscopic approach using liquid-state and solid-state Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and nuclear magnetic resonance (NMR) spectroscopy to characterize a fractionated LignoBoost lignin. Individual FT-ICR-MS, tandem MS, and NMR results helped to determine relevant information about the different lignin fractions, such as molecular weight distributions, oligomer sizes, linkage types, and presence of specific functional groups. In addition, a hetero spectroscopic correlation approach was applied to chemometrically combine MS, MS/MS, and NMR data sets. From these correlation analyses, it became obvious that a combination of tandem MS and NMR data sets gives the opportunity to comprehensively study and describe the general structure of complex biopolymer samples. Compound-specific structural information are obtainable, if this correlation approach is extended to 1D-MS and NMR data, as specific functional groups or linkages are verifiable for a defined molecular formula. This enables structural characterization of individual lignin compounds without the necessity for tandem MS experiments. Hence, these correlation results significantly improve the depth of information of each individual analysis and will hopefully help to structurally elucidate entire lignin structures in the near future.

Interlayer Anions of Layered Double Hydroxides as Mobile Active Sites To Improve the Adsorptive Performance toward Cd2

Layered double hydroxides (LDHs) have been considered important sinks for ionic contaminants in nature and effectively engineered adsorbents for environmental remediation. The availability of interlayer active sites of LDHs is critical for their adsorptive ability. However, inorganic LDHs generally have a nano-confined interlayer space of ca. 0.3-0.5 nm, and it is unclear how LDHs can utilize their interlayer active sites during the adsorption process. Thus, LDHs intercalated with SO42-, PO43-, NO3-, Cl-, or CO32- were taken as examples to reveal this unsolved problem during Cd2+ adsorption. New adsorption behaviors and pronounced differences in adsorption performance were observed. Specifically, SO42-/PO43- intercalated LDHs showed a maximum Cd2+ adsorption capacity of 19.2/9.8 times higher than other LDHs. The ligand exchange of H+ (on the surface -OH) by Cd2+ and formation of Cd-SO42-/PO43- complexes led to the efficient removal of Cd2+. Interestingly, interlayer SO42- was demonstrated to be able to move to the edges/outer surfaces of LDHs, providing abundant movable adsorption sites for Cd2+. This novel phenomenon made the SO42- intercalated LDH a superior adsorbent for Cd2+ among the tested LDHs, which also suggests that LDHs with a nano-confined interlayer space can also highly utilize their interlayer active sites based on the mobility of interlayer anions, offering a new method for constructing superior LDH adsorbents.

Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.

Tuning the Fermi resonance of pyridine using ethanol molecules

The Fermi resonance (FR) phenomenon is prevalent in infrared and Raman spectroscopy, and it can be observed in a variety of molecules. In particular, pyridine is a compound that has two Fermi doublets: ν₁ ∼ ν₁₂ and ν₁ + ν₆ ∼ ν₈. To analyze the effect of environmental changes on the FR, this study first investigated the Raman spectra of pyridine mixed with ethanol at different concentrations. Results indicated that the FR parameters exhibited a nonlinear dependence on the pyridine concentration fractions, and changing the concentration fraction of pyridine led to different hydrogen bond strengths. Second, the interaction mechanism of pyridine-ethanol binary solutions was analyzed by two-dimensional correlation Raman spectroscopy (2DCRS). In addition, high-pressure Raman spectra of a 50% pyridine-ethanol binary solution were measured up to a pressure of 19.65 GPa by a diamond anvil cell technique, and the phase transition of the binary solution occurred at 6.35 GPa. Finally, the impact of ethanol on the FR of pyridine was determined by deducing the FR parameters at different pressures. The turning point at 6.35 GPa was consistent with the Raman frequency-pressure relationships. The results demonstrated that changes in the intensity of ν₁ did not affect the FR of ν₁ + ν₆ ∼ ν₈, whereas the undisturbed frequency ν₁ still played a role in the FR. When the pressure was compressed to 13.36 GPa, the disappearance of the Raman peaks (ν₁ and ν₁') was attributed to the tuning of the molecular symmetry by pressure during the phase transition.

Temperature-perturbed two-dimensional generalized correlation characteristic slice spectra combined with multivariate method to identify adulterated milk

In order to improve the discrimination accuracy of adulterated milk, a detection method was proposed based on temperature-perturbed generalized two-dimensional (2D) correlation characteristic slice spectra. A total of 240 samples were prepared including three brands of 40 pure milk and 40 urea-tainted milk, respectively. The infrared attenuated total reflection spectra of each sample were collected at different temperatures. Synchronous 2D infrared correlation spectrum of each sample was calculated under the external perturbation of temperature. The characteristic slice spectra of each sample were extracted from synchronous 2D correlation spectrum at characteristic peaks of milk and adulterants. N-way partial least squares discriminant analysis (NPLS-DA) models of single brand and the fusion of three brands of adulterated milk were established based on 2D correlation characteristics slice spectra. For comparison, the discrimination models were established using synchronous 2D correlation spectra and one-dimensional (1D) infrared spectra at room temperature, respectively. For the three brand fusion models, the discrimination accuracies of unknown samples were 100%, 98.8% and 82.7% using 2D correlation characteristic slice spectra, 2D correlation spectra, and 1D spectra, respectively. The results showed that the proposed method not only compressed the data, but also effectively extracted the characteristic information, and improved the accuracy of discrimination.

Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling

Near infrared (NIR) spectroscopy has the characteristics of rapid processing, nondestructive analysis and on-line detection. This technique has been widely used in the fields of quantitative determination and substance content analysis. However, for complex NIR spectral data, most traditional machine learning models cannot carry out effective quantitative analyses (manifested as underfitting; that is, the training effect of the model is not good). Small amounts of available data limit the performance of deep learning-based infrared spectroscopy methods, while the traditional threshold-based feature selection methods require more prior knowledge. To address the above problems, this paper proposes a competitive adaptive reweighted sampling method based on dual band transformation (DWT-CARS). DWT-CARS includes four types in total: CARS based on integrated two-dimensional correlation spectrum (i2DCOS-CARS), CARS based on difference coefficient (DI-CARS), CARS based on ratio coefficient (RI-CARS) and CARS based on normalized difference coefficient (NDI-CARS). We conducted comparative experiments on three datasets; compared to traditional machine learning methods, our method achieved good results, demonstrating that this method has considerable prospects for the quantitative analysis of near-infrared spectroscopic data. To further improve the performance and stability of this method, we combined the idea of integrated modeling and constructed a partial least squares model based on Monte Carlo sampling for the samples obtained by CARS (DWT-CARS-MC-PLS). Through comparative experiments, we verified that the integrated model could further enhance the accuracy and stability of the results.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS): Part III. Versatile applications

In this review, the comprehensive summary of two-dimensional correlation spectroscopy (2D-COS) for the last two years is covered. The remarkable applications of 2D-COS in diverse fields using many types of probes and perturbations for the last two years are highlighted. IR spectroscopy is still the most popular probe in 2D-COS during the last two years. Applications in fluorescence and Raman spectroscopy are also very popularly used. In the external perturbations applied in 2D-COS, variations in concentration, pH, and relative compositions are dramatically increased during the last two years. Temperature is still the most used effect, but it is slightly decreased compared to two years ago. 2D-COS has been applied to diverse systems, such as environments, natural products, polymers, food, proteins and peptides, solutions, mixtures, nano materials, pharmaceuticals, and others. Especially, biological and environmental applications have significantly emerged. This survey review paper shows that 2D-COS is an actively evolving and expanding field.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part I. Yesterday and today

This comprehensive survey review, as the first of three parts, compiles past developments and early concepts of two-dimensional correlation spectroscopy (2D-COS) and subsequent evolution, as well as its early applications in various fields for the last 35 years. It covers past review articles, books, proceedings, and numerous research papers published on 2D-COS. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields confirms that it is well accepted as a powerful analytical technique to provide the in-depth understanding of systems of interest.

A fast multi-source information fusion strategy based on deep learning for species identification of boletes

Wild mushroom market is an important economic source of Yunnan province in China, and its wild mushroom resources are also valuable wealth in the world. This work will put forward a method of species identification and optimize the method in order to maintain the market order and protect the economic benefits of wild mushrooms. Here we establish deep learning (DL) models based on the two-dimensional correlation spectroscopy (2DCOS) images of near-infrared spectroscopy from boletes, and optimize the identification effect of the model. The results show that synchronous 2DCOS is the best method to establish DL model, and when the learning rate was 0.01, the epochs were 40, using stipes and caps data, the identification effect would be further improved. This method retains the complete information of the samples and can provide a fast and noninvasive method for identifying boletes species for market regulators.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm^–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm^–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm^–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Insights into enzymatic mimicking activity of silver nanoprisms: spectral monitoring and analysis

Peroxidase-like reaction process involving o-phenylenediamine (OPD) and silver nanoprisms in the presence of hydrogen peroxide (H₂O₂) was monitored using time-resolved ultraviolet-visible (UV-Vis) absorption spectroscopy. The oxidation of OPD and etching of silver nanoprisms were investigated by analyzing the dynamic spectral data. Two-dimensional correlation spectroscopy (2D-COS) and principal component analysis (PCA) were employed to gain insights into the correlation between catalytic oxidation of OPD and etching of silver nanoprisms. It was found that OPD offered significant protection effect for silver nanoprisms so that morphologies of silver nanoprisms maintained at the beginning period after addition of H₂O₂. Moreover, silver nanoprisms accelerated the oxidation of OPD by H₂O₂, demonstrating enzymatic mimicking activity of silver nanoprisms. The combination of time-resolved UV-Vis absorption spectroscopy and spectral calculation methods could be used for exploration of complex reaction systems with spectral variations.

Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs)

Geographical discrimination and adulteration analysis play significant roles in edible oil analysis. A novel method for discrimination and adulteration analysis of edible oils were proposed in this study. The two-dimensional correlation spectra of edible oils were obtained by solvents perturbation and the convolutional neural networks (CNNs) were constructed to analyze the synchronous and asynchronous correlation spectra of the edible oils. The differences for geographical origins of oils or oil types could be amplificated through the networks. For different networks, the layer sequences and the filter number of convolutional layers may affect the analysis results. A group of sesame oils from different geographical origins and a group of olive oils adulterated by other vegetable oils were adopted to evaluate the proposed method. The results show that the proposed method may provide an alternative method for edible oil discrimination and adulteration analysis in practical applications. For the two datasets, the prediction accuracy could be 97.3% and 88.5%, respectively.

Characterizing Properties and Environmental Behaviors of Dissolved Organic Matter Using Two-Dimensional Correlation Spectroscopic Analysis

Dissolved organic matter (DOM) exists ubiquitously in environments and plays critical roles in pollutant mitigation, transformation, and organic geochemical cycling. Understanding its properties and environmental behaviors is critically important to develop water treatment processes and environmental remediation strategies. Generalized two-dimensional correlation spectroscopy (2DCOS), which has numerous advantages, including enhancing spectral resolution and discerning specific order of structural change under an external perturbation, could be used as a powerful tool to interpret a wide range of spectroscopic signatures relating to DOM. A suite of spectroscopic signatures, such as UV-vis, fluorescence, infrared, and Raman spectra that can be analyzed by 2DCOS, is able to provide additional structural information hiding behind the conventional one-dimensional spectra. In this article, the most recent advances in 2DCOS applications for analyzing DOM-related environmental processes are reviewed, and the state-of-the-art novel spectroscopic techniques in 2DCOS are highlighted. Furthermore, the main limitations and requirements of current approaches for exploring DOM-related environmental processes and how these limitations and drawbacks can be addressed are explored. Finally, suggestions and new approaches are proposed to significantly advance the development of 2DCOS in analyzing the properties and behaviors of DOM in natural and engineered environments.

Drying of electrically conductive hybrid polymer-gold nanorods studied with in situ microbeam GISAXS

Gold nanorods (AuNRs) with conductive polymer shells are interesting colloidal building blocks for electronics. Hybrid particles with AuNR cores and poly(3,4-ethylenedioxythiophene) or polystyrene sulfonate (PEDOT:PSS) shells were prepared as stable aqueous dispersions. Film formation during the drying of such dispersions is known to affect the electric conductivity of the material. We observed the mechanisms of drying in thin, spray-coated films with grazing incidence small-angle X-ray scattering (GISAXS). A sparse, uniform monolayer formed because the anisotropic shape of the AuNR inhibited "coffee-ring" effects. We used generalized two-dimensional correlation (2DC) spectroscopy to analyze the GISAXS data and to decipher the microscopic structure formation of the film during drying. Four major scattering peaks were attributed to porous PEDOT, PSS, Au, and the substrate layer. Their time-dependent intensity indicated the sequence of film formation: AuNRs with mobile shells arranged on the substrate first, and PEDOT and then PSS dried sequentially around the gold core. We discuss the final phase-separation of PEDOT:PSS on the hybrid rods.

Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra

The last two decades have seen tremendous progress in the application of two-dimensional correlation spectroscopy (2D-COS) as a versatile analysis method for data series obtained using a large variety of different spectroscopic modalities, including infrared (IR) and Raman spectroscopy. The analysis technique is applicable to a series of spectra recorded under the influence of an external sample perturbation. Two-dimensional COS analysis is not only helpful to decipher correlations, which may exist between distinct spectral features, but can also be utilized to obtain the sequence of individual spectral changes. The focus of this review article is on the application of 2D-COS for analyzing spatially resolved data with special emphasis on hyperspectral imaging (HSI) study. In this review, we briefly introduce the fundamentals of the generalized 2D-COS analysis approach, discuss specific points of 2D-COS application to spatially resolved spectra and demonstrate essential aspects of data pre-processing for 2D-COS analysis of spatially resolved spectra. Based on illustrative examples, we show that 2D-COS is useful for spectral band assignment in HSI applications and demonstrate its utility for detecting subtle correlations between spectra features, or between features from different imaging modalities in the case of heterospectral (multimodal) HSI. Furthermore, a short overview on existing 2D-COS software tools is provided. It is hoped that this article represents not only a useful guideline for 2D-COS analyses of spatially resolved hyperspectral data but supports also further dissemination of the 2D-COS analysis method as a whole.

Two-dimensional correlation and codistribution spectroscopy (2DCOS and 2DCDS) analyses of time-dependent ATR IR spectra of d-glucose anomers undergoing mutarotation process in water

Two cyclic diastereoisomeric structures, known as α- and β-anomers of d-glucose with different configurations around C1 with OH groups in axial or equitroial positions, undergo the mutarotation conversion to each other in water. Two-dimensional correlation and codistribution spectroscopy (2DCOS and 2DCDS) analyses were applied to the time-dependent ATR IR spectra of aqueous solutions of α- and β-d-glucose undergoing such mutarotation conversion. 2DCOS analysis reveals that the increase and decrease in the IR intensities after the dissolution of α- or β-d-glucose are not fully synchronized, suggesting the mutarotation of d-glucose in water is not a simple binary conversion process but a multi-step reaction involving an intermediate species with a finite and observable concentration level and lifetime. 2DCDS analysis of the time-dependent ATR IR spectra clearly demonstrated the presence of intermediate species contributing to the band positions overlapped close to bands for α- and β-d-glucose. The fact that band positions identified for the intermediate species for α- to β-d-glucose conversion are the same for the reverse reaction suggests that they arise from the same species, most likely the open-ring structure produced by the hydrolysis.

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50μgL^-1 and root mean square error of prediction (RMSEP) of 4.42μgL^-1 for anthracene and of 3.61μgL^-1 and 4.29μgL^-1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97μgL^-1 and 4.63μgL^-1 for anthracene, 4.46μgL^-1 and 4.52μgL^-1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Vibrational two-dimensional correlation spectroscopy (2DCOS) study of proteins

A tutorial is provided for the generalized two-dimensional correlation spectroscopy (2DCOS), which is applicable to the vibrational spectroscopic study of proteins and related systems. In 2DCOS, similarity or dissimilarity among variations of spectroscopic intensities, which are induced by applying an external perturbation to the sample, is examined by constructing correlation spectra defined by two independent spectral variable axes. By spreading congested or overlapped peaks along the second dimension, apparent spectral resolution is enhanced and interpretation of complex spectra becomes simplified. A set of simple rules for the intensities and signs of correlation peaks is used to extract insightful information. Simulated IR spectra for a model protein are used to demonstrate the specific utility of 2DCOS. Additional tools useful in the 2DCOS analysis of proteins, such as data segmentation assisted with moving-window analysis, 2D codistribution analysis, Pareto scaling, and null-space projection are also discussed.

2D IR Correlation Spectroscopy in the Determination of Aggregation and Stability of KH Domain GXXG Loop Peptide in the Presence and Absence of Trifluoroacetate

Trifluoroacetate (TFA) is a strong anion byproduct of solid-phase peptide synthesis. Fourier transform infrared (FT-IR) spectroscopy can be used to ascertain the presence of this excipient in peptide samples for quality assessment. TFA absorbs as a strong sharp peak (1675 cm^-1) within the amide I' band of the spectral region. A peptide sample and the TFA excipient can be studied simultaneously by FT-IR and 2D IR correlation spectroscopies. In addition, these techniques are able to determine the effect of TFA on the stability of the peptide. Herein, we describe the spectroscopic characterization of the GXXG loop peptide (GXXGlp), which is present in KH domain containing proteins. The sequence of the Homo sapiens Krr1 GXXGlp is evolutionarily conserved (₁₆₅KRRQRLIGPKGSTLKALELLTNCY₁₈₉) and has been associated with ssDNA interaction and ribosome biogenesis. Our goal was to determine the structural elements present in this peptide and evaluate whether TFA affects the stability of GXXGlp during thermal stress. We observed differences in the molecular behavior of the synthetic peptide in the presence and absence of TFA at various peptide concentrations. Finally, 2D IR correlation spectroscopy was used for the determination of the unfolding process, mechanism and extent of peptide aggregation, and the effect of TFA on the stability of the peptide. This spectroscopic method can be applied to the characterization of any synthetic peptide.

Chemical composition and surfactant characteristics of marine foams investigated by means of UV-vis, FTIR and FTNIR spectroscopy

In this study, we collected the ultraviolet-visible (UV-vis), Fourier transform infrared (FTIR) and Fourier transform near-infrared (FTNIR) spectra of marine foams from different sites and foams produced by marine living organisms (i.e. algae and molluscs) to retrieve information about their molecular and structural composition. UV-vis spectra gave information concerning the lipid and pigment contents of foams. FTIR spectroscopy gave a more detailed qualitative information regarding carbohydrates, lipids and proteins in addition with information about the mineral contents of foams. FTNIR spectra confirmed the presence of carbohydrates, lipids and proteins in foams. Then, due to the higher content of structural information of FTIR spectroscopy with respect to FTNIR and UV-vis, we join the FTIR spectra of marine foams to those of humic substance from marine sediments and to the spectra of foams obtained by living organisms. We submitted this resulting FTIR spectral dataset to statistical multivariate methods to investigate specific aspects of foams such as structural similarity among foams and in addition, contributions from the organic matter of living organisms. Cluster analysis (CA) evidenced several cases (i.e. clusters) of marine foams having high structural similarity with foams from vegetal and animal samples and with humic substance extracted from sediments. These results suggested that all the living organisms of the marine environment can give contributions to the chemical composition of foams. Moreover, as CA also evidenced cases of structural differences within foam samples, we applied two-dimensional correlation analysis (2DCORR) to the FTIR spectra of marine foams to investigate the molecular characteristics which caused these structural differences. Asynchronous spectra of two-dimensional correlation analysis showed that the structural heterogeneity among foam samples depended reasonably on the presence and on the qualitative difference of electrostatic (hydrogen bonds) and nonpolar (van der Waals and π-π) interactions involving carbohydrate proteins and lipids present. The presence and relevance of these interactions agree with the supramolecular and surfactant characteristics of marine organic matter described in the scientific literature.