Frontiers of Two-Dimensional Correlation Spectroscopy. Part 1. New concepts and noteworthy developments

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.

Appling fluorescence spectroscopy with absolute principal component coefficient to explore dynamic migration of DOM fractions from an urbanized river during torrential rainfall

This research delves into the dynamic interplay between urbanization and the characteristics of Dissolved Organic Matter (DOM) in the Anyang River, particularly under the stress of torrential rain. The motivation stems from a critical need to decipher how urban landscapes influence water quality, focusing on the intricate transformations and movements of DOM. Employing advanced fluorescence spectroscopy techniques like Excitation-Emission Matrices (EEM) and Parallel Factor Analysis (PARAFAC), the study meticulously differentiates DOM compositions in urban and agricultural settings. It unveils a pronounced distinction, with urban streams showing elevated proteinaceous DOM from wastewater, contrasting with the humic substances prevalent in agricultural runoff. The analysis also captures how intense rainfall events catalyze significant shifts in DOM profiles, thereby emphasizing the need for tailored water quality management strategies in urbanized catchments. This comprehensive approach not only bridges gaps in understanding the urban impact on riverine ecosystems but also sets a foundation for future research and policy development in the face of escalating environmental changes.

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.

Deciphering DOM-metal binding using EEM-PARAFAC: Mechanisms, challenges, and perspectives

Dissolved organic matter (DOM) is a pivotal component of the biogeochemical cycles and can combine with metal ions through chelation or complexation. Understanding this process is crucial for tracing metal solubility, mobility, and bioavailability. Fluorescence excitation emission matrix (EEM) and parallel factor analysis (PARAFAC) has emerged as a popular tool in deciphering DOM-metal interactions. In this review, we primarily discuss the advantages of EEM-PARAFAC compared with other algorithms and its main limitations in studying DOM-metal binding, including restrictions in spectral considerations, mathematical assumptions, and experimental procedures, as well as how to overcome these constraints and shortcomings. We summarize the principles of EEM to uncover DOM-metal association, including why fluorescence gets quenched and some potential mechanisms that affect the accuracy of fluorescence quenching. Lastly, we review some significant and innovative research, including the application of 2D-COS in DOM-metal binding analysis, hoping to provide a fresh perspective for possible future hotspots of study. We argue the expansion of EEM applications to a broader range of areas related to natural organic matter. This extension would facilitate our exploration of the mobility and fate of metals in the environment.

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.

Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata

To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.

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.

Insight into suppression of dibutyl phthalate on DOM removal during municipal sewage treatment using fluorescence spectroscopy with PARAFAC and moving-window 2D-COS

Dibutyl phthalate (DBP) has been widely detected in municipal and industrial wastewater, which could indirectly inhibit pollutant removals, especially degradation of dissolved organic matter (DOM). Here, the inhibition of DBP on DOM removal from wastewater in pilot-scale A2O-MBR system was investigated by fluorescence spectroscopy with two-dimensional correlation (2D-COS) and structural equation modeling (SEM). Seven components were extracted from DOM using parallel factor analysis, i.e., tryptophan-like (C1 and C2), fulvic-like (C4), tyrosine-like (C5), microbial humic-like (C6) and heme-like (C7). The tryptophan-like had a blue-shift at DBP occurrence, defined as blue-shift tryptophan-like (C3). DBP with 8 mg L^-1 exhibited a stronger inhibition on removals of DOM fractions, extraordinarily tyrosine-like and tryptophan-like in anoxic unit than DBP of 6 mg L^-1 by moving-window 2D-COS. The indirect removals of C1 and C2 through the C3 removal were more strongly inhibited by 8 mg L^-1 DBP than those by 6 mg L^-1 DBP, while the former exhibited a weaker inhibition on the direct degradation of C1 and C2 than the latter via SEM. Based on metabolic pathways, abundances of key enzymes secreted by microorganism in anoxic unit, degrading tyrosine-like and tryptophan-like, were higher in wastewater with 6 mg L^-1 DBP than those with 8 mg L^-1 DBP. These could provide a potential approach for online monitoring of DBP concentrations in wastewater treatment plants, which could rectify operating parameters, and then enhance the treatment efficiencies.

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.

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.

Applying EEM-PARAFAC combined with moving-window 2DCOS and structural equation modeling to characterize binding properties of Cu (II) with DOM from different sources in an urbanized river

Dissolved organic matter (DOM) in aquatic environment distinctly affects the behavior and fate of heavy metals via complexation, while the interfacial mechanisms and processes are still lacking in detail. Here, Cu (II) binding characteristics of DOM originated from hilly (NDOM), rural (RDOM) and urban (UDOM) regions in an urbanized river was explored by fluorescence excitation-emission matrix spectroscopy (EEM) combined with principal component coefficients, parallel factor analyses (PARAFAC), moving-window two-dimensional correlation spectroscopy (MW2DCOS) and structural equation modeling (SEM). Eight components were extracted from the titrants through EEM-PARAFAC, i.e., phenol-like substance (C1), tyrosine-like substance (C2), visible tryptophan-like substance (C3), ultraviolet tryptophan-like substance (C4), recent biological production (C5), wastewater-derived organic matter (C6), microbial humic-like substance (C7) and fulvic-like substance (C8). Interestingly, NDOM only contained C1, C3, C5 and C8, while nearly all components were found in RDOM (except for C2) and UDOM (except for C4). The f value of C1 (1.239) in NDOM was much higher than those in RDOM (0.134) and UDOM (0.115), so was of C8. It indicated that phenol-like and fulvic-like derived from autochthonous sources exhibited great binding ratios in the complexation with Cu (II). Moreover, C3 and C5 from UDOM exhibited higher f values (0.591 and 1.983) than those from NDOM and RDOM, suggesting that Cu (II) has a great binding capacity on protein-like from domestic and industrial wastewater. The MW2DCOS revealed that phenol-like and protein-like in NDOM and RDOM were essential for the binding of 160 μmol L^-1 Cu (II), whereas fulvic-like in NDOM and UDOM could react significantly with 10 μmol L^-1 Cu (II). Based on SEM, Cu (II) concentration had a negative direct effect on the fluorescence intensity of C7 or C8, whereas it showed an indirect positive effect on C7 or C8 through influencing C5, so was C6. It suggested that Cu (II) showed an indirect positive effect on the C8. This study might present a further comprehend of the environmental behaviors of Cu (II) in rivers.

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.

Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR

INTRODUCTION

Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology.

OBJECTIVE

This research aimed to identify the geographical traceability for different parts of W. cocos.

METHODS

The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm^-1 , 6300-5150 cm^-1 and 4450-4050 cm^-1 of original spectra and the second-order derivative (SD) spectra preprocessed.

RESULTS

From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified.

CONCLUSION

Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

Mediating the slow dynamics of polyacrylates by small molecule-bridged hydrogen bonds

This report studied the changes in the slow dynamics of polyacrylate by adding a hindered phenol (CA) capable of forming three intermolecular hydrogen bonds (inter-HBs) per molecule with the polymer chain. The CA molecule apparently diminishes the slow modes (with lower peak temperatures and peak heights) of the polyacrylate, although it has a higher glass transition temperature (T_g) than the acrylic matrix, and the rigid CA-bridged HB network significantly amplifies the α-relaxation near T_g (with higher peak temperatures and peak heights). Consequently, the mixtures exhibit a diminishing slow mode that gradually merges with the prominent α-peak with increasing CA loadings. The anomalous dynamics concerning the opposite behaviors of the slow mode and α-relaxation was further rationalized in terms of dissociation of inter-HBs when the temperature is higher than T_g, together with the small molecule-alleviated macromolecular connectivity. This work provides essential insights into the slow dynamics of such HB-driven hybrids, and paves the way for tailoring the viscous flow properties of the hybrid material from a molecular level perspective.

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.

Hybrid 2D Correlation-Based Loss Function for the Correction of Systematic Errors

We present the derivation of a new kind of loss function from the symmetry rules of synchronous and asynchronous two-dimensional correlation maps. This loss function, which takes into account correlations that are based on causal relations among the members of a series of spectra, can be employed to solve non-linear inverse problems that are plagued by systematic multiplicative errors. This possibility results from the correlation-based loss function being practically insensitive to such systematic errors, which often arise in spectroscopy because sample spectra are usually ratioed against reference spectra. Using dispersion analysis, a sophisticated method of band fitting, of the spectra of poly(methyl methacrylate) films deposited on gold, we demonstrate the applicability and validity of the new loss function. If gold is used as a substrate, experimental spectra are often unphysical, that is, they display reflectance values larger than unity. In such cases, our correlation-based loss function not only helps to achieve accurate fits but also provides corrections to obtain physically meaningful spectra, which leads to results that are superior to conventional correction methods. The validity of the results is checked and proved with help of the results of dispersion analysis of spectra of films of poly(methyl methacrylate) on calcium fluoride (CaF₂) and silicon (Si), which do not suffer from the systematic errors.

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.

Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example

Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

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.

Mechano-responsive hydrogen-bonding array of thermoplastic polyurethane elastomer captures both strength and self-healing

Self-repairable materials strive to emulate curable and resilient biological tissue; however, their performance is currently insufficient for commercialization purposes because mending and toughening are mutually exclusive. Herein, we report a carbonate-type thermoplastic polyurethane elastomer that self-heals at 35 °C and exhibits a tensile strength of 43 MPa; this elastomer is as strong as the soles used in footwear. Distinctively, it has abundant carbonyl groups in soft-segments and is fully amorphous with negligible phase separation due to poor hard-segment stacking. It operates in dual mechano-responsive mode through a reversible disorder-to-order transition of its hydrogen-bonding array; it heals when static and toughens when dynamic. In static mode, non-crystalline hard segments promote the dynamic exchange of disordered carbonyl hydrogen-bonds for self-healing. The amorphous phase forms stiff crystals when stretched through a transition that orders inter-chain hydrogen bonding. The phase and strain fully return to the pre-stressed state after release to repeat the healing process.

Self-healing materials strive to emulate curable and resilient biological tissue but their performance is often insufficient for commercial applications because self-healing and toughening are mutually exclusive properties. Here, the authors report a tough and strong carbonate-type thermoplastic polyurethane elastomer that self-heals at ambient temperature.

The Competitive Adsorption of Chromate and Sulfate on Ni-Substituted Magnetite Surfaces: An ATR-FTIR Study

With similar chemical properties and geometrical configurations, sulfate and chromate display interesting competitive adsorption on mineral surfaces. Although such issues have been investigated on several Fe (hydr)oxide surfaces, e.g., ferrihydrite, goethite and hematite, the competitive adsorption on magnetite surfaces and the constraint mechanism have seldom been studied. This impedes the understanding of the transfer and fate of chromate and sulfate on magnetite surfaces, as magnetite is not only a useful adsorbent but also an efficient reductant to decrease the mobility and toxicity of chromium. In the present study, the geometries of the competitive adsorption of chromate and sulfate on Ni-substituted magnetite surfaces over a pH range of 4–9 were investigated using in situ attenuated total reflectance Fourier transform infrared spectroscopy and two-dimensional correlation analysis. In individual adsorption, nonprotonated monodentate mononuclear (NMM) complexes dominated chromate adsorption, accompanied by a few bidentate binuclear (BB) complexes. For sulfate, NMM complexes and outer-sphere (OS) species predominated under acidic and neutral–alkaline conditions, respectively. The above variation in adsorption configuration resulted in the different adsorption competitiveness between chromate and sulfate at different pH values. Specifically, the NMM complexes of chromate were substituted by NMM sulfate complexes under acidic conditions and vice versa. However, under neutral and alkaline conditions, the OS species of sulfate scarcely affected the adsorption of chromate. The adsorption affinity of chromate and sulfate on Ni-substituted magnetite increased in the following order: OS complex (sulfate) < NMM complexes (chromate) < NMM complexes (sulfate).

Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (R_p²) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved R_p² = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

PC 2D-COS: A Principal Component Base Approach to Two-Dimensional Correlation Spectroscopy

This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.

Effects of copper ions on removal of nutrients from swine wastewater and on release of dissolved organic matter in duckweed systems

High concentration of Cu²⁺ in swine wastewater raises concerns about its potential adverse effects on nutrient removal by aquatic plants like duckweed. In this work, the effects of copper ions on nutrient removal and release of dissolved organic matter (DOM) were investigated in duckweed systems. Results showed that the removal performance of ammonia nitrogen (NH₃N) and total phosphorus (TP) increased at 0.1-1.0 mg/L of Cu²⁺, while dropped at 2.0-5.0 mg/L of Cu²⁺. A novel kinetic model in which Cu²⁺ was taken into account was then developed which was used to optimize Cu²⁺ concentration at 0.96 mg/L for nutrient removal in duckweed systems. NADH, detected in DOM by the parallel factor (PARAFAC) analysis, exhibited high capacities of binding copper ions, so it played an important role on the decrease of Cu²⁺ concentrations in duckweed systems. The principle component analysis (PCA) showed that the dominant DOM were lower molecular weight compounds at 1.0 mg/L of Cu²⁺ and higher molecular weight compounds at 2.0-5.0 mg/L of Cu²⁺. The bonds of CH (humic-like), NO (NO₃^-) and ArH (tyrosine) in DOM were responsible for not only the fastest binding with Cu²⁺ from the result of the two-dimensional Fourier transform infrared correlation spectroscopy (2D-FTIR-CoS) but also the variations of DOM conformations at a critical concentration of 0.5 mg/L Cu²⁺ from the perturbation correlation moving window two-dimensional (PCMW2D) analysis. These findings lead to a better understanding on the environmental behaviors and mechanisms of Cu²⁺ in duckweed systems.

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.

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.

Interactions between algal (AOM) and natural organic matter (NOM): Impacts on their photodegradation in surface waters

The occurrence of algae bloom would lead to the release of algae-derived organic matter (AOM) and then alter the abundance and behavior of dissolved organic matter (DOM) in aquatic ecosystems. In this study, the characteristics and photodegradation of AOM, naturally occurring organic matter (NOM) derived from soil and plants and their mixtures were explored to reveal the potential interactions between AOM and NOM in water. Results indicated that the protein-like components from AOM and the humic-like components from SRNOM took place inter-component interactions in the AOM-NOM mixtures. Meanwhile, application of two-dimensional Fourier transform infrared correlation spectroscopic (2D-FTIR-COS) analysis revealed that carboxylic C=O had a high priority to bind with other functional groups (e.g., phenolic-OH, polysaccharides C-O, amideⅡC-N/N-H and celluloses C-H). More crucially, it was found that the AOM-NOM mixtures subjected to a very different photodegradation behavior to their end-members (i.e., AOM and NOM), likely because of the occurrence of AOM-NOM interactions as well as their roles in mediating the yield of reactive oxygen species. For instance, the presence of AOM led to increased photodegradation degrees of the chromophoric fraction in NOM. In contrast, the NOM did not exhibit any photosensitization role in the photodegradation of the proteins from AOM. This study has potential implications for our understanding of the carbon cycling in anthropogenically impacted aquatic systems such as inland rivers and lakes.

Sensor array for qualitative and quantitative analysis of metal ions and metal oxyanion based on colorimetric and chemometric methods

Gold nanoparticle (AuNP)-based colorimetric sensor is sensitive for the detection of metal ions and metal oxyanion in aqueous solution. However, this method is usually not suitable for multi-objective analysis in complex mixture systems because it is suffering from interference of co-existents. In the present paper, we proposed a sensitive, flexible, low-cost, and multi-units sensor method for the qualitative and quantitative analysis of metal ions and metal oxyanion based on the global ultraviolet and visible (UV-Vis) spectra of amino acid-gold nanoparticles (amino acid-AuNPs) sensors in the range of 230-800 nm. Different amino acids (L-Histidine, L-Lysine, L-Methionine, D-Penicillium) which can prevent the aggregation of the AuNPs in NaCl solution, were investigated to build sensor arrays responding to different ions induced AuNPs aggregation. The UV-Vis spectra that Cd²⁺, Ba²⁺, Mn²⁺, Ni²⁺, Cu²⁺, Fe³⁺, Cr³⁺, Cr₂O₇^2-, Sn⁴⁺, Pb²⁺ induce amino acid-AuNPs displayed different characteristics and the ions were classified correctly by using partial least squares-discriminant analysis (PLS-DA). Taking the advantage of the multivariate analysis and sensor arrays, we simultaneously quantified the ions in binary and ternary mixture systems (Cr³⁺/Cr₂O₇^2-, Fe³⁺/Cd²⁺, Fe³⁺/Cr³⁺/Cr₂O₇^2-). Data fusion methods further improved the prediction accuracy of the chemometric models built on multi-amino acids-AuNPs sensors. The proposed method has a potential for analyzing metal ions and metal oxyanion in much more complex mixture systems.

Insights into the thermal phase transition behavior of a gemini dicationic polyelectrolyte in aqueous solution

The thermal-induced phase transition behavior of a LCST-type poly(ionic liquid) (PIL) aqueous solution with gemini-cationic structure, poly[(1,8-octanediyl-bis(tri-n-butylphosphonium)4-styrene sulfonate)] (P[SS-P2]), was investigated in this paper. Based on the calorimetric measurements, a unique dependence of transition points on concentration was found in P[SS-P2] aqueous solution compared to its mono-cationic PIL and [SS-P2] aqueous solution. Optical microscopy showed that globular microscopic droplets were formed during the phase transition, suggesting that gemini dications and the possible dynamic ionic bonds may facilitate the liquid-liquid phase separation (LLPS) in P[SS-P2] aqueous solution. Temperature-variable 1H NMR and FT-IR investigations manifested that the dehydration of anionic chains instead of the dehydration of dications served as the driving force of the phase separation in the P[SS-P2] aqueous solution, implying that the polymerized anions tended to aggregate together first and lay in the core with dications distributed around the globules at the end of the transition process. Notably, considering that the SO3 groups in the gemini-cationic system tended to be distributed around the surface of collapsed anionic main chains rather than be wrapped into the aggregates, it is supposed that dynamic ionic bonding between dication and anionic backbones was distributed in the periphery of the globules and acted as the "cross-linkers", which enhanced the stability of regular droplets after the phase transition in P[SS-P2] aqueous solution.

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.