Two-trace two-dimensional (2T2D) correlation spectroscopy – A method for extracting useful information from a pair of spectra

Investigation of the processing of calcined and vinegar-quenched ochre samples using 2T2D correlation Raman spectroscopy

The application of mineral medicine has a long history of thousands of years, and the processing of traditional Chinese medicine (TCM) is a unique pharmaceutical technology. However, the processing of most mineral medicines is still unclear and requires in-depth and systematic research. To achieve accurate identification of mineral medicines and elucidate their variation from a chemical perspective, two-trace two-dimensional (2T2D) correlation Raman spectroscopy was used. This is the first time that 2T2D correlation analysis has been used to elucidate the processing for TCM. Since the properties of mineral medicines change with processing (e.g., calcined and vinegar-quenched ochre), X-ray photoelectron spectroscopy, X-ray diffraction, and transmission electron microscopy were used as auxiliary tools to investigate and validate the Raman spectroscopy findings. Comparative analysis of the Raman spectra of ochre and its processed products revealed that the crystal structure and composition of the calcined ochre and vinegar-quenched ochre changed and that their purity improved. Based on these results, 2T2D correlation Raman spectroscopy method has excellent analytical performance, enables the rapid identification of mineral drugs and a deeper understanding of their processing. Our study also provides new concepts and methods for the quality detection of mineral drugs.

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational information from blood, effectively capturing potential structural changes in haemoglobin (Hb) in lung cancer patients. In this study, we address the challenge of detecting subtle Hb features within the broader blood matrix and introduce an innovative two-stage spectral analysis framework. First, continuous wavelet transform (CWT) is employed to enhance spectral resolution and reinforce the key absorption bands of Hb. Subsequently, two-trace two-dimensional correlation spectroscopy (2T2D-COS) is applied to examine the fine vibrational differences-in both synchronous and asynchronous spectra-between lung cancer patients and healthy controls, revealing alterations in Hb secondary structures (e.g., α-helices and β-sheets). Results show that critical Hb-related peaks at 4862 cm-1, 4615 cm-1, and 4432 cm-1 undergo significant changes in lung cancer samples. Furthermore, combining these refined spectral features with machine learning classifiers (e.g., support vector machines) achieves an overall accuracy of 97.50 % and a sensitivity of 100.00 %. This work not only confirms the value of NIRS in detecting protein-level molecular information in blood but also presents a promising, efficient spectroscopic strategy for early lung cancer diagnosis, offering broad biomedical applicability.

2T2D-correlation analysis of infrared specular reflectance and ratio-reflectance spectra for investigating siloxane nanofilms on silicate glass substrates

Ultrathin nanofilms of siloxane derived from methyltrichlorosilane (MTCS) and 3-aminopropyltrimethoxysilane (APTMS) were formed on the surface of silicate glass and investigated using infrared specular reflectance (IR-SR) spectroscopy. To differentiate the spectral bands of the film from those of the glass substrate, two-trace two-dimensional correlation spectroscopy (2T2D-COS) was employed. The spectra analyzed were either IR-SR spectra of unmodified and surface-modified glass or reflectance-absorbance (RA) spectra, calculated from ratio-reflectance (RR) spectra. The spectral region studied focused on the Si-O-Si stretching and bending vibrations, corresponding to the absorption-reflection IR region of the glass. The results demonstrated that 2T2D-COS enables the distinction of bands attributed to the film, not only for nanofilms with a thickness of approximately 80 nm but also for ultrathin nanofilms ranging from 3 to 10 nm. Additionally, specific characteristics of the 2T2D-COS spectra derived from the IR-SR spectra were discussed, along with precautions considered which are necessary to avoid mistakenly attributing glass bands to the film.

Noncovalent Interactions of Surface Adsorbed Species Control the Self-Assembly of Calcinated Nickel Oxide Nanoparticles

Utilizing state of the art diffraction, imaging and spectroscopic techniques in conjunction with two-dimensional correlation analysis, we provide novel in-depth insights into the physics and chemistry behind the different tendencies towards self-assembling in NiO nanoparticles as function of their surface facets. We demonstrate substantially different temperature dependence of the spectroscopic behavior of the two types of NiO NPs, polar versus non-polar faceted. Temperature-dependent spectroscopy data for NiO NPs obtained by the ammonia route are consistent with the process in which high amount of water molecules that take part in hydrogen-bonding interaction with the surface-adsorbed non-dissociated water molecules on the neutral (100) planes are lost during the thermal treatment and attached back upon cooling. Interactions between water molecules adsorbed on two vicinal NiO NPs are responsible for keeping the self-assembly of the Ni(OH)2 NPs upon heat treatment. In carbamide-based NiO NPs, the self-assembly of initially formed Ni3(OH)4(NO3)2 NPs is not preserved. These NPs are terminated with polar (111) atomic planes, on which water molecules dissociatively adsorb, giving surface hydroxyl groups. As the hydrogen bonding proton - donating and accepting abilities are negligible at OH-polar terminated NiO NPs, only unfavorable inter-NP interactions are possible which leads to disruption of the NP assembly.

COS-DeformDeep: Adaptive 2T2D spectral feature extraction method for improving the component identification performance in mixtures based on handheld Raman technology

BACKGROUND

Raman spectroscopy is extensively utilized for the analysis of mixture components. Handheld Raman spectrometers, characterized by their compactness and portability, can rapidly acquire on-site spectral data without the need for intricate pretreatment or bulky instrumentation. In comparison to traditional laboratory-grade spectrometers, handheld devices offer distinct advantages. Nevertheless, although the unique spectral fingerprints of different substances facilitate identification, accurately quantifying and analyzing each component in complex mixtures remains a significant challenge.

RESULTS

Therefore, a novel method called COS-DeformDeep is proposed to enhance and extract spectral features in handheld Raman mixture component identification. Firstly, synchronous two-trace two-dimensional correlation spectroscopy (2T2D-COS) is performed on pure components and mixture samples to highlight weak signals in overlapped peaks. Subsequently, deformable convolutions (DCNs) enhance the adaptability of deep learning models towards geometric deformation in the correlation peak region, thereby improving the capability of spectral feature extraction in 2T2D-COS. The proposed method was verified on three mixture datasets. Meanwhile, three substances, Ethanol, Diacetone alcohol, and Histidine, were chosen as the identified components with a volume-weight ratio ranging from 2 % to 20 %. The COS-DeformDeep model achieves the best performance with an average accuracy, precision, recall, and F1 score of 94.97 %, 98.45 %, 92.44 %, and 95.06 % respectively.

SIGNIFICANCE

The proposed COS-DeformDeep is a highly efficient method for extracting features from weak spectral signals. By effectively capturing and analyzing the subtle variations in signals, it significantly enhances the recognition accuracy of specific components at low concentrations in mixtures. Moreover, its simplicity and suitability for handheld devices make it accessible to a wide range of users.

Combining two-trace two-dimensional correlation analysis and convolutional autoencoder-based feature extraction from an entire correlation map to enhance vibrational spectroscopic discrimination of geographical origins of agricultural products

This study explored convolutional autoencoder (CAE)-based feature extraction from entire two-trace two-dimensional (2T2D) correlation maps as a promising tool to enhance the accuracy of vibrational spectroscopy-based discriminant analysis. Although 2T2D correlation maps constructed using only a pair of spectra were effective to highlight minute spectral differences, there was an excessive number of features (variables). Thus, only slice spectra at a wavenumber chosen from the map were typically used for discriminant analysis. In this case, exclusion of a huge number of remaining 2T2D features that would be complementary and descriptive for a given analysis was a major drawback limiting accuracy. Therefore, CAE was adopted to extract features from entire 2T2D correlation maps while minimizing information loss. For evaluation, near-infrared (NIR) and Raman spectra of chili pepper samples and NIR spectra of perilla seed samples were employed for hetero- and homo-spectral 2T2D correlation analysis, respectively. Then, CAE-extracted features from the 2T2D correlation maps were used to discriminate the geographical origins of samples using support vector machine (SVM). Accuracy improved by employing CAE-extracted variables in both cases compared with those using slice spectra chosen from the 2T2D maps. Moreover, to provide clearer insight into the models, gradient-weighted class activation mapping (Grad-CAM) identifying the variables significantly contributed to the discrimination was employed in parallel.

Correlation Filters to Streamline Analysis of Congested Spectral Datasets

The correlation filter (CF) technique is introduced as a versatile tool for data pretreatment to selectively attenuate interfering or overlapping signals of congested spectra. This technique leverages two-dimensional correlation spectroscopy (2D-COS) to create a filter multiplier that effectively addresses limitations inherent in traditional null-space projection (NSP) methods based on least-squares subtraction. We apply CF to the analysis of a model solution mixture system undergoing spontaneous evaporation, where volatile solvent concentrations change concurrently but at only slightly different rates. Despite the similarity of these parallel processes, CF successfully separates the overlapped dynamics of individual components by attenuating dominant signal contributions. CF also enables streamlined 2D codistribution spectroscopy (2D-CDS) analysis to determine the sequential order of component appearance. Multiple layers of CF can be applied to isolate individual component dynamics. Heterocomponent 2D correlation can then recover lost information by recombining CF-treated spectra. CF is applicable to two-trace two-dimensional (2T2D) correlation for comparative spectral analysis of a pair of spectra. CF treatment is expected to be a useful tool beyond 2D-COS applicable to many areas of spectral analyses, including the environmental and interfacial studies.

FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example of Panax notoginseng

Panax notoginseng (P. notoginseng) is a traditional medicinal plant with high medicinal and economic values. The authenticity of P. notoginseng often determines its quality, and the quality of geographical indication (GI)-producing areas is usually superior to that of other producing areas, which are exploited by unscrupulous traders and affect the market order. The aim of this study was to characterize and identify the geographic origin of P. notoginseng using Fourier transform near-infrared (FT-NIR) spectroscopy, with rapid detection combined with multivariate analysis. The use of principal component analysis and correlation spectral analysis enabled the initial differential characterization and identification of P. notoginseng from different production areas. Then, random forest (RF) and support vector machine (SVM) models were established, and the results show that the results showed that the second-order derivative preprocessing and successive projection algorithm feature extraction achieved 100% classification correctness and the model training time is the shortest. Further constructing the image recognition model, synchronous two-dimensional correlation spectroscopy (2DCOS) image combined with residual convolutional neural network achieved accurate classification (accuracy of 100%) and did not require complex preprocessing and artificial feature extraction process, to maximize the avoidance of errors caused by human factors. The recognition results of the externally validated set showed that the image recognition method has a strong generalization ability and has a high potential for application in the identification of P. notoginseng production areas.

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model. This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm

The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R 2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer's active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model's prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.

Spectroscopic studies under properties of chlorpromazine conjugated to gold nanoparticles

Scientific studies have demonstrated that conjugates of anticancer drugs with metal nanoparticles (MeNPs) lead to a more effective deactivation of tumor cells compared to free drugs. Similarly, it has been established that conjugates of antibiotics with MeNPs exhibit higher biocidal activity against bacteria than their unbound counterparts. However, limited information is available regarding conjugates formed from drugs other than anticancer and antibiotics. Therefore, our research aims to develop synthesis methods for conjugates of chlorpromazine (CPZ), a neuroleptic, with gold nanoparticles (AuNPs). CPZ-AuNP conjugates were prepared through a ligand exchange reaction conducted on the surface of quasi-spherical, negatively charged citrate-stabilized TC-AuNPs with an average size of 55 ± 5 nm. UV-vis spectroscopy was employed to determine the stability range of the conjugates under controlled conditions of pH and ionic strength. Based on electrokinetic measurements, it was observed that the zeta potential of CPZ-AuNP conjugates strongly depends on the amount of CPZ adsorbed on the TC-AuNP surface. Additionally, the conjugates exhibited an isoelectric point at pH 8.8. Surface-enhanced Raman spectroscopy (SERS) and surface-enhanced infrared absorption spectroscopy (SEIRA) were employed to elucidate the adsorption structure of CPZ on TC-AuNPs. The interpretation of the spectra was conducted based on the Raman and FTIR spectra of CPZ, along with calculations performed using Density Functional Theory (DFT). The results indicated that CPZ primarily interacts with the TC-AuNP surface through the angularly oriented phenothiazine ring and the propylene bridge. Furthermore, it was demonstrated that the C-N-C fragment is perpendicular to the surface of the TC-AuNP with which it interacts. The findings from this analysis suggest the potential for further research on the use of these conjugates in biomedical applications.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates

Identifying the aging time of Liupao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.

Simultaneous Measurement of Two-Trace Two-Dimensional (2T2D) Near-Infrared (NIR) Asynchronous Correlation Spectra and Small-Angle X-ray Scattering (SAXS) to Characterize Thermally Aged Polypropylene (PP)

In this study, a new system was developed to carry out simultaneous near-infrared (NIR) and small-angle X-ray scattering (SAXS) measurements. Aged polypropylene (PP) was examined with the NIR-SAXS system to demonstrate how it can be utilized to derive pertinent information about the polymer structure. Pairs of SAXS profiles and NIR spectra of PP in its initial state and after aging were measured to derive an in-depth understanding of the aging phenomenon. The SAXS profiles of the PP samples showed a clear shift of the SAXS peak to the lower q direction induced by the thermal aging, indicating an increase in the length of the long-period structure. Two-trace two-dimensional (2T2D) asynchronous correlation spectra derived from NIR spectra clearly revealed that the aging treatment leads to a substantial increase in the spectral intensity of the regularity bands representing the longer helix present in a folded lamellar structure. In other words, it suggests that the long helix structure is more abundantly present than the short helix structure in the aged PP than in the initial PP. By combining the information derived from the SAXS profiles and NIR spectra, the details of the aging-induced variation were clearly determined. Namely, aging causes additional crystallization of the PP by developing more helical structures, which involves an increase in the lamellar thickness as well as a decrease in the amorphous region. The growth of the rigid crystalline phase restricts the elastic deformation in the amorphous structure, which eventually induces the deterioration of PP by making the polymer hard but brittle. Such observation, in turn, implies that retarding or accelerating the crystallized structure of PP substantially works to control the progress of aging.

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

Rheo-Optical Near-Infrared (NIR) Characterization of Styrene-Butadiene Rubber (SBR) Using Two-Trace Two-Dimensional (2T2D) Correlation Analysis

A rheo-optical characterization technique based on the combination of near-infrared (NIR) spectroscopy and tensile testing was applied for the first time to an actual rubber sample based on styrene-butadiene rubber (SBR) including silica filler. When SBR samples were subjected to mechanical deformation, changes in the NIR spectral features were readily captured. Two-trace two-dimensional (2T2D) correlation analysis was then applied to the sets of NIR spectra to clearly reveal the subtle but pertinent difference between the NIR spectral features of the initial and deformed SBR. The initial deformation of the sample induces greater deformation of the soft butadiene groups than of the hard styrene groups. The inclusion of the silica filler and a coupling agent (CA) essentially develops firm links between the silica and butadiene via the CA to restrict the displacement of the butadiene during the tensile deformation of the system. The development of such linkage requires even more mechanical force to deform the SBR, which, in turn, improves Young's modulus of the rubber system. Asynchronous correlation spectra of SBR with no silica filler revealed that, during the deformation of the SBR, the butadiene groups were initially deformed, and this feature was then replaced by the predominant deformation of the hard styrene groups. On the other hand, this correlation feature became somewhat unclear when a similar analysis was applied to the SBR sample with silica filler, revealing subtle differences in interaction between individual comonomer functional groups distributed randomly along the copolymer chain and CA.

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.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Streamlined Multi-Attribute Assessment of an Array of Clinical-Stage Antibodies: Relationship Between Degradation and Stability

Clinical antibodies are an important class of drugs for the treatment of both chronic and acute diseases. Their manufacturability is subject to evaluation to ensure product quality and efficacy. One critical quality attribute is deamidation, a non-enzymatic process that is observed to occur during thermal stress, at low or high pH, or a combination thereof. Deamidation may induce antibody instability and lead to aggregation, which may pose immunogenicity concerns. The introduction of a negative charge via deamidation may impact the desired therapeutic function (i) within the complementarity-determining region, potentially causing loss of efficacy; or (ii) within the fragment crystallizable region, limiting the effector function involving antibody-dependent cellular cytotoxicity. Here we describe a transformative solution that allows for a comparative assessment of deamidation and its impact on stability and aggregation. The innovative streamlined method evaluates the intact protein in its formulation conditions. This breakthrough platform technology is comprised of a quantum cascade laser microscope, a slide cell array that allows for flexibility in the design of experiments, and dedicated software. The enhanced spectral resolution is achieved using two-dimensional correlation, co-distribution, and two-trace two-dimensional correlation spectroscopies that reveal the molecular impact of deamidation. Eight re-engineered immunoglobulin G4 scaffold clinical antibodies under control and forced degradation conditions were evaluated for deamidation and aggregation. We determined the site of deamidation, the overall extent of deamidation, and where applicable, whether the deamidation event led to self-association or aggregation of the clinical antibody and the molecular events that led to the instability. The results were confirmed using orthogonal techniques for four of the samples.

Investigation of selective SERS enhancement mechanism of Au nanospheres and Au nanorods based on 2T2D-SERS correlation spectroscopy

The selective enhancement mechanism in surface-enhanced Raman scattering (SERS) is demonstrated. Two different types of single nanoparticles (Au nanosphere and Au nanorod) were used to investigate the role of the localized surface plasmon resonance (LSPR) in SERS spectra by using the two-trace two-dimensional (2T2D) correlation spectroscopy. The SERS intensities of three probe molecules, 4-mercaptobenzoic acid (4-MBA), 4-aminothiophenol (4-ATP), and 4-bromobenzenethiol (4-BBT), respectively, were enhanced but slightly different when adsorbed on Au nanospheres and Au nanorods. 2T2D correlation SERS spectra clearly showed that even with the same shape of Au nanoparticles, the main factors influencing the SERS enhancement can vary depending on the specific type of SERS tags used. Such subtle difference could not be clearly identified by the conventional spectral analysis. This result sheds light on potential applications of 2T2D correlation spectroscopy. For 4-MBA molecules, the a1 and b2 modes are mainly affected by the Au nanospheres and Au nanorods. For 4-ATP molecules, the a1 and b2 modes related to C-S stretching combined with C-C stretching band are mainly affected by Au nanorods and Au nanospheres. For 4-BBT molecules, the a1 and b2 modes of C-C (aromatic ring) stretching band are mainly affected by Au nanorods and Au nanospheres. This study offers valuable insights into the relationship between nanoparticle shape and SERS enhancement.

The footprint of linear dichroism in infrared 2D-Correlation spectra

On the level of the Bouguer-Beer-Lambert approximation, the effects introduced by linear dichroism into absorbance spectra can be simulated by classic linear dichroism theory. If wave optics and dispersion theory are employed, linear dichroism can be modelled with a 4x4 matrix formalism. For linear dichroism theory, the angle between polarization direction and transition moment can be seen as a perturbation which allows to calculate corresponding infrared 2D correlation spectra. Similarly, with help of an orientation representation based on Euler's angles, varying the latter allows the same if electromagnetic theory is employed. Correspondingly, we compare the substantially different footprints of linear dichroism according to both theories in infrared 2D correlation spectra and show that only those based on wave optics and dispersion theory are in accordance with experimental results. Accordingly, in particular asynchronous 2D correlation spectra allow to detect orientation with a sensitivity that is unparalleled in case of conventional spectra, even if they are recorded with help of a polarizer and an analyzer.

Two-trace two-dimensional correlation spectra (2T2D-COS) analysis using FTIR spectra to monitor the immune response by COVID-19

There is a growing trend in using saliva for SARS-CoV-2 detection with reasonable accuracy. We have studied the responses of IgA, IgG, and IgM in human saliva by directly comparing disease with control analyzing two-trace two-dimensional correlation spectra (2T2D-COS) employing Fourier transform infrared (FTIR) spectra. It explores the molecular-level variation between control and COVID-19 saliva samples. The advantage of 2T2D spectra is that it helps in discriminating remarkably subtle features between two simple pairs of spectra. It gives spectral information from highly overlapped bands associated with different systems. The clinical findings from 2T2D show the decrease of IgG and IgM salivary antibodies in the 50, 60, 65, and 75-years COVID-19 samples. Among the various COVID-19 populations studied the female 30-years group reveals defense mechanisms exhibited by IgM and IgA. Lipids and fatty acids decrease, resulting in lipid oxidation due to the SARS-CoV-2 in the samples studied. Study shows salivary thiocyanate plays defense against SARS-CoV-2 in the male population in 25 and 35 age groups. The receiver operation characteristics statistical method shows a sensitivity of 98% and a specificity of 94% for the samples studied. The measure of accuracy computed as F score and G score has a high value, supporting our study's validation. Thus, 2T2D-COS analysis can potentially monitor the progression of immunoglobulin's response function to COVID-19 with reasonable accuracy, which could help diagnose clinical trials. KEY MESSAGES: The molecular profile of salivary antibodies is well resolved and identified from 2T2D-COS FTIR spectra. The IgG antibody plays a significant role in the defense mechanism against SARS-CoV-2 in 25-40 years. 2T2D-COS reveals the absence of salivary thiocyanate in the 40-75 years COVID-19 population. The receiver operation characteristic (ROC) analysis validates our study with high sensitivity and specificity.

Authentication of ST25 rice using temperature-perturbed Raman measurement with variable selection by Incremental Association Markov Blanket

A temperature-perturbed transmission Raman measurement was demonstrated for the discrimination of ST25 and non-ST25 rice samples. ST25 rice is a premium long-grain Vietnamese rice with the aroma of pandan leaves and the scent of early sticky rice. Raman spectra of rice samples were acquired with temperature perturbation ranging from 20 to 50 °C, and the variables (intensities of peaks) with greater discrimination were selected from the spectra using Incremental Association Markov Blanket (IAMB) for authentication. The combination of four, seven, and four variables selected from the spectra at 20, 30, and 50 °C, respectively, yielded the highest accuracy of 97.9%. The accuracies in the single-temperature measurements were lower, suggesting that the combination of mutually complementary spectral features acquired at these temperatures is synergetic to recognize the compositional differences between two sample groups, such as in the amylose/amylopectin ratio and the protein constituent.

An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum

BACKGROUND

The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed.

RESULTS

Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples.

SIGNIFICANCE

This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Course of Plasmodium infection studied using 2D-COS on human erythrocytes

BACKGROUND

The threat of malaria is still present in the world. Recognizing the type of parasite is important in determining a treatment plan. The golden routine involves microscopic diagnostics of Giemsa-stained thin blood smears, however, alternative methods are also constantly being sought, in order to gain an additional insight into the course of the disease. Spectroscopic methods, e.g., Raman spectroscopy, are becoming increasingly popular, due to the non-destructive nature of these techniques.

METHODS

The study included patients hospitalized for malaria caused by Plasmodium falciparum or Plasmodium vivax, in the Department of Infectious Diseases at the University Hospital in Krakow, Poland, as well as healthy volunteers. The aim of this study was to assess the possibility of using Raman spectroscopy and 2D correlation (2D-COS) spectroscopy in understanding the structural changes in erythrocytes depending on the type of attacking parasite. EPR spectroscopy and two-trace two-dimensional (2T2D) correlation was also used to examine the specificity of paramagnetic centres found in the infected human blood.

RESULTS

Two-dimensional (2D) correlation spectroscopy facilitates the identification of the hidden relationship, allowing for the discrimination of Raman spectra obtained during the course of disease in human red blood cells, infected by P. falciparum or P. vivax. Synchronous cross-peaks indicate the processes taking place inside the erythrocyte during the export of the parasite protein towards the cell membrane. In contrast, moieties that generate asynchronous 2D cross-peaks are characteristic of the respective ligand-receptor domains. These changes observed during the course of the infection, have different dynamics for P. falciparum and P. vivax, as indicated by the asynchronous correlation cross-peaks. Two-trace two-dimensional (2T2D) spectroscopy, applied to EPR spectra of blood at the beginning of the infection, showed differences between P. falciparum and P. vivax.

CONCLUSIONS

A unique feature of 2D-COS is the ability to discriminate the collected Raman and EPR spectra. The changes observed during the course of a malaria infection have different dynamics for P. falciparum and P. vivax, indicated by the reverse sequence of events. For each type of parasite, a specific recycling process for iron was observed in the infected blood.

Influence of the type of substrate on the properties of carbon nanotubes layer studied by Raman spectroscopy

The development of nanomaterials technology allows to design a novel medical strategies, and could also be useful in the field of regenerative medicine. The paper presents a study on the functionalized multi-walled carbon nanotubes (MWCNTs-f) layers deposited by electrophoretic method (EPD) on the surfaces of two types of substrates: titanium (Ti) and stainless steel. SEM and EDS analyses confirm that incubation in a simulated body fluid (SBF) caused a formation of hydroxyapatite on the surface of the Ti/MWCNTs-f. Raman micro-spectroscopy was a method of choice to study presented materials. The MWCNTs-f layer on the surface of the titanium plate shows better layer order than the corresponding layer deposited on the stainless steel. The structure and ordering of the nanocarbon layer play a key role in the biological activity of the materials. This was confirmed by the incubation of the plates with deposited layer of carbon nanotubes in SBF. A titanium substrate with a MWCNTs-f layer supports the deposition of some components from the environment, while a stainless steel substrate promotes the formation of a carbon film that inhibits the deposition of certain components from the environment. A two-trace two-dimensional (2T2D) analysis confirmed a different effect of SBF on the MWCNTs-f layer depending on the type of substrate. The MWCNTs-f layer on titanium substrate seems to represent an interesting proposition for novel bioactive strategies.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

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.

Identification of adulterated milk based on auto-correlation spectra

A qualitative analysis of melamine-adulterated milk was proposed based on two-trace two-dimensional (2T2D) auto-correlation spectra. The concentration of melamine was used as external perturbation, and 40 adulterated samples of each brand with different concentrations of melamine (0.01 g/L to 1 g/L) were configured. Four brands of milk were used to configure experimental samples, including Guangming brand, Mengniu brand, Sanyuan brand and Wandashan brand. Spectroscopic data of pure milk and melamine-adulterated milk were measured by infrared (IR) (80-4000 cm^-1) spectrophotometer. 2T2D auto-correlation spectral technology combined with least squares support vector machine (LS-SVM) method was used for qualitative analysis. The two strongest auto-correlation peaks in the auto-correlation spectra were selected for modeling. For Guangming brand, the intensities of auto-correlation at two wave numbers 2898 cm^-1 and 2972 cm^-1 were selected as independent variables. For Mengniu brand, the intensities of auto-correlation at two wave numbers 2852 cm^-1 and 2920 cm^-1 were selected. For Sanyuan brand, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For Wandashan brand, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For four brands fused together, the intensities of auto-correlation at two wave numbers 2900 cm^-1 and 2974 cm^-1 were selected. For each brand, the accuracy of qualitative analysis was 100 %. For four brands fused together, the accuracy of qualitative analysis was 99.05 %. In this way, it greatly reduced the amount of data to be processed. This study showed that 2T2D auto-correlation spectral technology combined with LS-SVM method was perfect for the discrimination of melamine-adulterated milk.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments

This comprehensive survey review compiles noteworthy developments and new concepts of two-dimensional correlation spectroscopy (2D-COS) for the last two years. It covers review articles, books, proceedings, and numerous research papers published on 2D-COS, as well as patent and publication trends. 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 strongly confirms that it is well accepted as a powerful analytical technique to provide an in-depth understanding of systems of interest.

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.

Exploring the use of infrared absorption spectroscopy and two-trace two-dimensional correlation analysis for the resolution of multi-component drug mixtures

Community drug checking provides an essential service that responds to the unpredictable and variable supply of illicit drugs. Point of care detection of trace components using portable infrared spectrometers is a harm reduction measure to prevent overdose. This study investigates the ability of weighted subtraction and two-trace two-dimensional (2T2D) correlation analysis to reveal the presence of heroin in an opioid mixture that contains heroin and fentanyl mixed with caffeine as a cutting agent. In both methods, a spectral trace was identified that provided reasonably high correlation scores to heroin when compared to entries in drug libraries. The two-trace correlation analysis produced a higher match score, suggesting that future improvements in spectral unmixing methods may enhance the reliability of detecting trace components in drugs.

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.

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Nanodiamond (ND)-based polyamide (PA) 66 nanocomposite studied with infrared (IR) microscopy and time-domain nuclear magnetic resonance (TD-NMR) combined with two-trace two-dimensional (2T2D) correlation analysis

Nanodiamond/polyamide (ND/PA) nanocomposite was examined with infrared (IR) microscopy and time-domain nuclear magnetic resonance (TD-NMR) to elucidate in detail the interphase between amino functionalized ND (ND-NH₂) and PA 66. An IR image of the ND/PA nanocomposite suggested the uniform nanoscale distribution of the ND-NH₂ particles thanks to the spherical shape and accessible external surface of ND terminated with reactive amino groups. On the other hand, a substantial level of change was observed in T₂ decay curves when the ND-NH₂ particles were incorporated in the PA 66. The fine features of the thermally induced changes in the decay curves were readily analyzed with the two-trace two-dimensional (2T2D) correlation method. The variation in the asynchronous correlation intensity indicated that the changes observed in the mechanical properties of the ND/NH₂ may be attributed to the development of crosslinking between tie chains in the amorphous region via the interaction between the ND-NH₂ and PA 66. Accordingly, such firm links have a substantial effect in preventing the displacement of the amorphous domain, which eventually increases the Young's modulus but reduces the ductility of the PA.

ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints

As a fungus with both medicinal and edible value, Wolfiporia cocos (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components' content fluctuates in wild and cultivated W. cocos, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of W. cocos, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated W. cocos in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of W. cocos. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated W. cocos in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of W. cocos. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for W. cocos under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of W. cocos, but also provide a basis for quality assessment of medicinal fungi.

Polyamide (PA) 66 molding defect studied with optical coherence tomography (OCT) and near-infrared (NIR) spectroscopy

An optical coherence tomography (OCT) system combined with near-infrared spectroscopy (NIRS) was developed to carry out simultaneously the cross-sectional observation and spectral measurement of a specific area inside a polymer sample. This OCT-NIRS system consists of a fiber-optic-based spectrometer combined with an OCT system and enables non-invasive imaging up to a depth of several millimeters and the recording of the NIR spectrum in the observed area. A subsequent analysis of the collected data will provide key information revealing the way in which the microscopic structure of the polymer is affected by the chemical composition around it. A structural defect inside a molded polyamide (PA) 66 sample was examined with the OCT-NIRS system to demonstrate how this technique can be utilized to characterize chemical composition as well as the morphological features inside the sample. A specific void was detected by OCT when the PA sample was molded without any drying treatment. The NIR spectrum collected around the void area of the undried PA was then compared with that of vacuum-dried PA by two-trace two-dimensional (2T2D) correlation analysis to identify a subtle but pertinent difference in the spectral features. The appearance of several correlation peaks in the 2T2D asynchronous correlation spectrum revealed that the OH group represented by the NIR band at 1446 nm is found in relative abundance around the void, which clearly reveals that the development of the void in the molded PA results from inadequate sample pretreatment.

Slice spectra approach to synchronous Two-dimensional correlation spectroscopy analysis for milk adulteration discriminate

The discrimination approach of adulterated milk was proposed combined synchronous two-trace two-dimensional (2T2D) correlation slice spectra at the characteristic wavebands of adulterant in milk with multivariate method. Two common adulterants, melamine and urea, were analyzed to demonstrate useful by the method. 2T2D (near infrared) NIR slice spectra at characteristic wavebands of adulterant were extracted from the synchronous 2T2D correlation spectra, and were input to construct the N-way partial least squares discriminant analysis (NPLS-DA) models. One-dimensional (1D) spectroscopy featuring all the present components in the samples combined with partial least squares discriminant analysis (PLS-DA) was also evaluated for comparison. The results indicated that for one kind of adulterant in model, prediction accuracies of slice spectral models were both 100% for melamine-adulterated and urea-adulterated samples discrimination. Moreover, for two kinds of adulterants in model, prediction accuracies of slice spectral models were 90.57% and 100% for melamine-adulterated and urea-adulterated discrimination, respectively, which was better than those of 1D whole models based on PLS-DA (only 81.13% and 98.15%, respectively). The comparison informs that the 2T2D slice spectra extracted at the characteristic wavebands of adulterant highlighted the adulterant spectral features and was obviously advantage to improve the discrimination accuracy. Meanwhile, the complexity of slice spectra is significantly reduced compared with the whole matrix of synchronous 2T2D correlation spectra.

Discrimination of adulterated milk using temperature-perturbed two-dimensional infrared correlation spectroscopy and multivariate analysis

The discrimination method for adulterated milk is proposed based on temperature-perturbed two-dimensional (2D) infrared correlation spectroscopy and N-way partial least squares discriminant analysis (NPLS-DA). Two brands of pure and adulterated milk samples were prepared. The mid-infrared spectra of all samples were obtained from 30 ℃ to 55 ℃ with an interval of 5 ℃. Under the perturbation of temperature, synchronous 2D correlation spectra were calculated to build discrimination models of pure milk and adulterated milk. In comparison, the NPLS-DA models were built based on three-dimensional (3D) stacked map (sample × temperature × wavenumber variable). For the NPLS-DA models of two brands of milk, the discrimination accuracy of unknown samples in the prediction set is 100% using temperature-perturbed 2D infrared correlation spectra, versus 77.8% using conventional 3D stacked map. The proposed method can be used as an alternative way for classifying pure and adulterated milk.

Two-trace two-dimensional (2T2D) correlation applied to a number of spectra beyond a simple pair

The application of two-trace two-dimensional (2T2D) correlation analysis to a number of spectra consisting of more than a simple pair is explored, especially when such spectra are randomly collected without knowing the sampling order. Calculation and interpretation of 2T2D correlation spectra are briefly reviewed, and a systematic procedure to identify the set of characteristic bands, which are mutually asynchronous and least overlapped with each other, is described. 2T2D correlation is applied to individual spectra by selecting a representative reference spectrum, such as the average of the whole dataset. A slice of an asynchronous 2T2D spectrum at a characteristic band is devoid of the spectral contribution from the species represented by the band. Since 2T2D analysis may be applied to the whole set of spectra, and each 2T2D asynchronous spectrum yields a set of slices for different characteristic bands, it is possible to generate a series of 2T2D slices obtained at a given characteristic band. By applying the generalized 2D correlation or a successive 2T2D analysis to such slices, one can obtain excellent estimates of the pure component spectra of the mixture, which are comparable to the results from other curve resolution techniques.

Estimating more than two pure component spectra from only two mixture spectra using two-dimensional correlation

A procedure is described to estimate the pure component spectra of mixtures from only a pair of available spectra even when there are more than two component species present in the system. In contrast, traditional multivariate curve resolution (MCR) technique cannot be used for such a case. The method relies on the use of two-trace two-dimensional (2T2D) correlation spectroscopy. Asynchronous 2T2D spectrum is used to identify the characteristic bands most strongly associated with the individual mixture component species. Correlation coefficients derived from the synchronous 2T2D spectrum are used to obtain a set of correlative filtering functions to distribute the spectral intensity of the average spectrum among the estimates of the pure component spectra. Efficacy of the method was demonstrated using a pair of ATR IR spectra obtained for two solution mixtures containing three main ingredients with very similar compositions. Relatively congested and overlapped spectral region was used first for the demonstration, and reasonable resolution was accomplished yielding a set of the estimates of pure component spectra with most of the expected pertinent features included. The analysis was then extended to a broader spectral region containing well-isolated spectral signatures of individual components for positive validation. While traditional MCR technique seems to perform better with a large number of spectra, this technique can be effectively used in conjunction with MCR to improve its stability and performance, especially under some challenging conditions.

Smart Error Sum Based on Hybrid Two-Trace Two-Dimensional (2T2D) Correlation Analysis

Based on hybrid 2D correlation analysis, we recently derived and introduced a "smart error sum," a sophisticated loss function that can be used for solving nonlinear inverse problems like the determination of optical constants and oscillator parameters from a series of optical spectra in the infrared spectral region. The advantage of the smart error sum compared to the conventional sum of squared errors lies in its ability to marginalize multiplicative systematic errors such as, for example, reflectance values above unity in transflection spectra. This is enabled by a transformation, which allows fits to not exclusively focus on forcing fit spectra to agree with experimental spectra at every wavenumber point by all means, but also to take correlations such as spectral similarities and their changes with certain perturbations into account. In this work, we extend our approach to accommodate the treatment of individual spectra, instead of only series, based on hybrid two-trace two-dimensional (2T2D) correlation analysis. We evaluate and prove the value of our approach by individually analyzing experimental transflection spectra of polymethyl methacrylate (PMMA) layers on gold substrates. The comparison of the results with those obtained by the original smart error sum based on the whole set of spectra as well as those resulting from conventional fitting of series and individual spectra (using the conventional sum of squared errors) confirms the validity and soundness of the 2T2D smart error sum.