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

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.

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images - EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

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.

Ag(I) Ion-Concentration-Dependent Dynamic Mechanism of Thiolactic-Acid-Capped Gold Nanoclusters Revealed by Fluorescence Spectra and Two-Dimensional Correlation Spectroscopy

Based on fluorescence spectroscopy, being combined with several spectral analysis techniques including principal component analysis (PCA), two-dimensional correlation spectroscopy (2D-COS), and moving window 2D-COS, the study disclosed the structural variations of gold nanoclusters capped by thiolactic acid (AuNCs@TLA) induced by Ag(I) ions. Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) were applied to monitor the morphology evolution of the surface and composition of the nanoclusters induced by Ag(I) ions. Several spectral components, centered at (790, 607) nm, (670, 590) nm, and (740, 670) nm were revealed by 2D-COS analysis, suggesting new luminescent species or groups were generated with the introduction of Ag(I) ions. A two-stage mechanism was revealed for the photoluminescence variations of AuNCs@TLA induced by Ag(I) ion. The first stage was characterized by the emission quench of 790 nm followed by the emerging emission of 607 nm, which was attributed to the anti-galvanic reaction; and the second stage featured by the noticeable growth of the emission's intensity around 670 nm as result of the AuNCs' size effect. The present study will attract more focuses on near-infrared (NIR)-emitted metal nanoclusters and promote their synthesis and utilities.

Enhanced Spectral Resolution and Two-Dimensional Correlation Spectroscopy (2D-COS)

The apparent enhancement of spectral resolution is one of the attractive features of two-dimensional correlation spectroscopy (2D-COS). Highly overlapped adjacent bands often encountered in one-dimensional spectra may be effectively differentiated and identified by spreading peaks along the second dimension. This differentiating feature or selectivity is especially prominent in asynchronous spectra, where even a slight difference in the variation patterns of overlapped bands in response to a given perturbation results in the generation of cross-peaks. While cross-peaks in asynchronous spectra can identify signals originating from different moieties or bands, they do not effectively specify which regions of spectra actually share the same molecular origin. Overreliance on asynchronous spectra alone risks the potential false negative assessment or lack of sufficient specificity, leading to the failure of classifying signals into a reasonable set of component groups. The combined use of synchronous and asynchronous spectra coupled with the scaling techniques, elimination of anti-correlated negative synchronous peaks, and a robust line shape narrowing method provides a means to achieve both selectivity and specificity for resolution-enhancement of 2D-COS.

Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis of Evolving Hyperspectral Images

The evolutionary behavior is examined for heterogeneously distributed hyperspectral images of a simulated biological tissue sample comprising lipid-like and protein-like components during the aging process. Taking a simple planar average of a spectral image loses useful information about the spatially resolved nature of the data. In contrast, multivariate curve resolution (MCR) analysis of a spectral image at a given stage of aging produces a set of loadings of major component groups. Each loading represents the combined spectral contributions of a mixture of similar but not identical constituents (i.e., lipid-like and protein-like components). Temporal analysis of individual component groups using two-dimensional correlation spectroscopy (2D-COS) and MCR provides much-streamlined results without interferences from the overlapped contributions. Grouping of data into separate components also allows for the effective comparison of the parallel processes of lipid oxidation and protein denaturation involving a number of constituents using the heterocomponent 2D-COS analysis. The complex interplays of lipid constituents and protein secondary structures during the tissue aging process are unambiguously highlighted. The possibility of extending this approach to a much more general form of applications using a moving window analysis is also discussed.

Tensor product based 2-D correlation data preprocessing methods for Raman spectroscopy of Chinese handmade paper

The paper introduces two new methods, namely the cross correlation method (CCM) and two-dimensional correlation method (TDCM), for preprocessing Raman spectroscopy data for analyzing Chinese handmade paper samples. CCM expands the spectral dimension from 1×N to 1×2N-1 by taking cross-correlation between two spectral data of the same category. TDCM includes two-dimensional synchronous correlation method (TDSCM) and two-dimensional asynchronous correlation method (TDACM), which expand the spectral dimension from 1×N to N×N by taking tensor products between two spectral data and between one spectral data and the Hilbert transformation of the other spectral data of the same category, respectively. The experimental data were preprocessed using baseline removal, CCM, TDSCM, and TDACM methods. Four machine learning models were employed to evaluate the effects of these methods: principal component analysis (PCA) combined with linear regression (LR), support vector machine (SVM) combined with LR, k-Nearest Neighbors (KNN), and random forest (RF). The results show that the R-squared values for the PCA model were nearly 1 for all types of data, indicating high accuracy. However, for SVM-LR, KNN, and RF models, the R-squared values were sorted in the order of raw data, baseline removal data, CCM, TDSCM, and TDACM preprocessed data. The R-squared values of KNN and RF machine learning models for TDACM preprocessed data were approaching 1, indicating that the accuracy of machine learning was significantly improved by nearly 100%. This has led to a remarkable improvement in the accuracy of supervised models such as KNN and RF, bringing them closer to the level of unsupervised models such as PCA.

Roles of hydrogen bonding interactions and hydrophobic effects on enhanced water structure in aqueous solutions of amphiphilic organic molecules

Insights into the solute-induced water structural transformations are essential to understand the role of water in biological and chemical reaction processes. Herein, the structural changes in water induced by amphiphilic organic molecules were investigated using concentration-dependent derivative Raman spectroscopy (DRS) combined with two-dimensional Raman correlation spectroscopy (2D Raman-COS). We shall restrict our attention in this work to binary mixtures of water with dimethyl sulfoxide (DMSO), acetone, and isopropanol (IPA), all of which have similar chemical structures. The spectral changes in O:H and OH stretching modes illustrate that the solute molecules induce an enhancement of the water structure in dilute solutions, where the enhanced degree of water structure is closely related to the size of the dipole moment of organic molecules. In addition, the transformations of solute-induced water-specific structures were evaluated by 2D Raman-COS, which shows that the strong hydrogen bond (H-bond) structure of water is more sensitive to organic molecules and induces a transition to the weak H-bond structure of water.

Discrimination of Milk Freshness Based on Synchronous Two-Dimensional Visible/Near-Infrared Correlation Spectroscopy Coupled with Chemometrics

Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality.

Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

Stimuli-responsive polymers can switch specific physical properties in response to a change of the environmental conditions. This behavior offers unique advantages in applications where adaptive materials are needed. To tune the properties of stimuli-responsive polymers, a detailed understanding of the relationship between the applied stimulus and changes in molecular structure as well as the relationship between the latter and macroscopic properties is required, which until now has required laborious methods. Here, we present a straightforward way to investigate the progressing trigger, the change of the chemical composition of the polymer and the macroscopic properties simultaneously. Thereby, the response behavior of the reversible polymer is studied in situ with molecular sensitivity and spatial as well as temporal resolution utilizing Raman micro-spectroscopy. Combined with two-dimensional correlation analysis (2DCOS), this method reveals the stimuli-response on a molecular level and determines the sequence of changes and the diffusion rate inside the polymer. Due to the label-free and non-invasive approach, it is furthermore possible to combine this method with the investigation of macroscopic properties revealing the response of the polymer to the external stimulus on both the molecular and the macroscopic level.

Spectroscopic investigations to reveal synergy between polystyrene waste and paraffin wax in super-hydrophobic sand

Sand based superhydrophobic materials, such as paraffin-coated sand, are the focus of global research to fight land desertification. The present work investigates the development of paraffin-coated sand with extending service life as well as improving and stabilizing hydrophobic property by adding plastic waste. While the addition of polyethylene (PE) did not improve the hydrophobic property of paraffin coated sand, incorporating 4.5% of polystyrene (PS) in the composition of coated sand increased the contact angle. Fourier Transform Infrared spectroscopy (FTIR), X-ray diffraction patterns (XRD) and two-dimensional correlation spectroscopy (2D-COS) indicated that PS increased the molecular orientation of sand and reduced the thickness of the paraffin coating. Paraffin on the other hand improved the distribution of PS and prevented aggregation with sand. Both FTIR bands at 1085 cm^-1 and 462 cm^-1 were more sensitive to change in PS contents, while other bands at 780 cm^-1 and 798 cm^-1 were more sensitive to change in paraffin contents. Moreover, the XRD patterns of sand were split into two components by the addition of PS indicating the transformation of morphology to less ordered or more distorted form. 2D-COS is a powerful tool to reveal harmony of components in mixtures, extract information related to the role of each of them, and help in decision-making process regarding choosing the appropriate recipes.