Vibrational Spectroscopy最新文献

{"title":"Investigating temperature-dependent spectral changes in human saliva using SERS on Ag and Au surfaces","authors":"Michaela Klenotová ,&nbsp;Pavel Matějka","doi":"10.1016/j.vibspec.2025.103788","DOIUrl":"10.1016/j.vibspec.2025.103788","url":null,"abstract":"<div><div>Surface-enhanced Raman Scattering (SERS) Spectroscopy, combined with multivariate data analysis such as Principal Component Analysis (PCA), effectively detects subtle changes in complex biological samples. In this study, we applied SERS to identify subtle molecular changes in human saliva deposited on large nanostructured Ag and Au substrates, focusing on the influence of temperature variations ranging from 10°C to 45°C. The selected temperature intervals – 10°C (cooling technology), 23°C (laboratory temperature), 37°C (physiological temperature), 42°C (fever), and 45°C (extreme temperatures) – reflect real-world conditions that biological and medical samples may encounter during collection, storage, transport, and analysis. We aimed to determine whether saliva samples remain stable at these temperatures over four days or if significant changes occur. Furthermore, we investigated the reversibility of spectral alterations during thermal jumps, where samples were heated to 45°C and then cooled back to 10°C. To ensure reliability, we utilized a computer-controlled mapping stage and a thermostatic sample holder, allowing precise temperature control and repeated recordings at identical locations on the substrate. Attention was given to intensity changes of marker bands, including band ratios, such as the ratio of 1175 cm⁻¹ to 1005 cm⁻¹ bands (<strong>protein hydration</strong> marker), the ratio of 856 cm⁻¹ to 831 cm⁻¹ bands (<strong>hydrophobicity</strong> marker of the environment surrounding <strong>tyrosine</strong>), and the ratio of 1360 cm⁻¹ to 1340 cm⁻¹ bands (<strong>hydrophobicity</strong> marker of the environment surrounding <strong>tryptophan</strong>) at different temperatures. The protein hydration marker exhibited a progressive decrease with increasing temperature, indicating water loss from the protein environment. In contrast, the hydrophobicity markers for tyrosine and tryptophan residues showed an increasing trend, suggesting enhanced hydrophobicity and a temperature-dependent reorganization of the protein structure on the SERS-active surfaces. In addition to these markers, we monitored changes related to amino acid residue bands for each temperature during the stability tests and thermal cycling. The spectral changes were associated with water loss and the reorganization of molecules near the nanostructured plasmonic surface, indicating saliva's sensitivity to temperature conditions. Our findings emphasize the importance of maintaining proper storage conditions for saliva films on large-area substrates to preserve sample integrity and prevent the misinterpretation of temperature-induced spectral changes. This study contributes to best practices for SERS analysis of thermally sensitive materials, particularly biofluids, especially in the context of medical diagnostics.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103788"},"PeriodicalIF":2.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SERS analysis of saliva and its key components: The effects of various collection methods, sample dilution, excitation wavelengths, and enhancing substrates","authors":"Michaela Klenotová ,&nbsp;Pavel Matějka","doi":"10.1016/j.vibspec.2025.103787","DOIUrl":"10.1016/j.vibspec.2025.103787","url":null,"abstract":"<div><div>Recently, human saliva has become a subject of research as an excellent material for patient-friendly diagnostics. An increasing number of diagnostic tests utilize saliva due to its easy and noninvasive collection, eliminating the patient's stress. Simultaneously, developing Surface-Enhanced Raman Scattering (SERS) spectroscopy offers new possibilities for analyzing saliva's composition. Saliva is a complex biological material; many factors influence its composition, including medication use, diseases, stress, hormone levels, diet, age, and hydration. This complexity raises the question of whether it is possible to observe and definitively attribute changes in specific substances through SERS spectra. One of the key questions we posed is how the SERS spectrum will change with an increased level of α-amylase 1 A (AMY1A), an enzyme marker of acute stress. AMY1A forms complexes with proline-rich proteins (PRP). Thus, we examined whether similar spectral changes are observed with a PRP level increase in saliva. Another focus was lysozyme C (LYZ C), a nonspecific marker of infectious diseases. We examined how increased levels of LYZ C affect SERS spectra, particularly considering its sensitivity to changes in the ionic composition of saliva and its complexation with PRP and lactoferrin (LF). Moreover, we explored whether the albumin (HSA) level, which plays a vital role in regulating osmotic pressure, influences LYZ C activity and how it is manifested in SERS. Furthermore, we investigated the effect of saliva dilution and collection methods on SERS spectra. We searched for correlations with significant components such as AMY1A, HSA, LYZ C, LF, and Poly-L-proline (PLP is an analog of PRP). We showed the role of gold (Au) and silver (Ag) substrates, comparing the spectral differences. Solving the issues is crucial for the ability of SERS techniques to detect and/or monitor biomolecules in saliva and can lead to significant advancements in noninvasive diagnostics.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103787"},"PeriodicalIF":2.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authentication analysis of animal fats adulteration in nail polish simulation using Raman spectroscopy coupled with chemometrics","authors":"Nurrulhidayah Ahmad Fadzillah ,&nbsp;Amal Elgharbawy ,&nbsp;Mohammad Aizat Jamaluddin ,&nbsp;Nur Azira Tukiran ,&nbsp;Anjar Windarsih ,&nbsp;Abdul Rohman ,&nbsp;Siti Jamilah Mohd Sukri ,&nbsp;Nurul Widad Fitri Muhammad ,&nbsp;Anis Hamizah Hamid","doi":"10.1016/j.vibspec.2025.103785","DOIUrl":"10.1016/j.vibspec.2025.103785","url":null,"abstract":"<div><div>Cosmetics are being used daily by many people, and their consumption is on the rise every year. These products are adulterated with cheaper alternatives to increase their profit. As more cosmetics are available in the market, the authenticity of halal cosmetics has raised much concern among Muslim consumers throughout the world. Therefore, authentication analysis of cosmetic products is urgently needed. This study was conducted to detect beef tallow (BT), chicken fat (CF), lard (LD), and mutton fat (MF) in nail polish using Raman spectrometry combined with chemometrics. Partial least square-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were successfully used to differentiate animal fats into four subclasses. In addition, partial least square (PLS) and orthogonal PLS (OPLS) regression were adequate to detect and predict the levels of BT, CF, LD, and MF in nail polish with R²&gt; 0.990 both in calibration and validation models. The best prediction model for BT was from OPLS at the wavenumber range of 100–3200 cm⁻¹ with R²&gt; 0.990 and RMSEC as well as RMSEP lower than 2.0 %. Meanwhile PLS model demonstrated the best model to predict CF, LD, and MF was the PLS with R²&gt; 0.990 and RMSEC as well as RMSEP around 1–2.40 %. This study revealed the potential application of Raman spectroscopy in combination with chemometrics as an effective and efficient technique for authenticating nail polish base formulation adulterated with animal fats.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103785"},"PeriodicalIF":2.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Early Subtle Bruising in Strawberries Using VNIR Hyperspectral Imaging and Deep Learning","authors":"Runze Feng ,&nbsp;Xin Han ,&nbsp;Yubin Lan ,&nbsp;Xinyue Gou ,&nbsp;Jingzhi Zhang ,&nbsp;Huizheng Wang ,&nbsp;Shuo Zhao ,&nbsp;Fanxia Kong","doi":"10.1016/j.vibspec.2025.103786","DOIUrl":"10.1016/j.vibspec.2025.103786","url":null,"abstract":"<div><div>Detecting early surface bruising in strawberries during postharvest storage is crucial for maintaining product quality and reducing waste. In this paper, we combined visible-near infrared hyperspectral imaging (VNIR-HSI) technology with deep learning methods to efficiently detect early surface bruising in strawberries. Specifically, we created a hyperspectral image dataset of strawberries, captured in the 454–998 nm wavelength range at five intervals: 1, 12, 24, 36, and 48 hours after applying four levels of bruising: none, slight, moderate, and severe. To address the challenges of a limited sample size and redundant hyperspectral data, we employed data augmentation and two feature wavelength extraction techniques: Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS). We then developed several classification models, including SVM, CNN, CNN-LSTM, and CNN-BiLSTM. Experimental results showed that the CNN-BiLSTM model, which used feature wavelengths selected by CARS, achieved a 97.8 % classification accuracy for detecting slight bruising 12 hours post-treatment, with an average bruised area of 24.09 ± 6.38 mm². This performance surpassed the SVM, CNN, and CNN-LSTM models by 14.7, 10.5, and 4.5 percentage points, respectively. This study effectively classified early bruising in strawberries and visualized bruised areas, demonstrating significant improvements in detection and classification of early bruising, particularly for smaller areas.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103786"},"PeriodicalIF":2.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in single-molecule surface-enhanced Raman spectroscopy (SERS) for biosensing","authors":"Huaizhou Jin ,&nbsp;Yanlong Cai ,&nbsp;Chenhui Song ,&nbsp;Shangzhong Jin ,&nbsp;Qiang Lin","doi":"10.1016/j.vibspec.2025.103784","DOIUrl":"10.1016/j.vibspec.2025.103784","url":null,"abstract":"<div><div>Single-molecule (SM) detection and manipulation have revolutionized the field of biosensing, enabling unprecedented insights into the heterogeneity, dynamics, and interactions of biomolecules. This review focuses on the latest advances in single molecule Surface Enhanced Raman Spectroscopy (SM-SERS) techniques and approaches to confirm SM events, and examines four major approaches: bi-analyte SERS (BiASERS), plasmonic trapping, nanopore/slits, and chemical binding. We will discuss the development of these techniques as well as fabrication and application plasmonic nanostructures, and will explore the integration of these methods. Furthermore, we will discuss the challenges and future perspectives in the SM-SERS and the confirmation of SM events, focusing on improving sensitivity, reproducibility, and the ability to probe sub-angstrom molecular dynamics in order to provide a comprehensive overview.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103784"},"PeriodicalIF":2.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the vibrational modes of zwitterion glycine in aqueous solution","authors":"Mark Christie ,&nbsp;Mozhdeh Mohammadpour ,&nbsp;Jan Sefcik ,&nbsp;Karen Faulds ,&nbsp;Karen Johnston","doi":"10.1016/j.vibspec.2025.103783","DOIUrl":"10.1016/j.vibspec.2025.103783","url":null,"abstract":"<div><div>Vibrational spectroscopy is widely employed to probe and characterise chemical, biological and biomedical samples. Glycine solutions are relevant in a variety of biological and chemical systems, yet the reported experimental vibrational wavenumbers of the glycine zwitterion, which is the dominant species in aqueous solution, are inconsistent and incomplete. This study presents a procedure that obtained a complete set of vibrational frequencies for the glycine zwitterion in aqueous solution, apart from the two lowest wavenumber modes which are available from a previous THz study. Vibrational spectra were measured using IR and Raman spectroscopy, to obtain both IR and Raman-active modes for a range of different glycine solution concentrations using four different instruments. Insight from a literature survey of density functional theory calculations in implicit and explicit water was used to guide the deconvolution of the experimental spectra into vibrational modes, giving 22 out of 24 vibrational wavenumbers with a standard error of less than 3 cm⁻¹. This thorough analysis of the glycine vibrational spectra has enabled missing and erroneous wavenumbers in literature to be identified, and the systematic procedure for determining vibrational modes will pave the way for deeper quantitative analysis of glycine systems, and serve as a benchmark for computational method development.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103783"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An modified RamanNet model integrated with serum Raman spectroscopy for breast cancer screening","authors":"Ningning Sun ,&nbsp;Fei Xie ,&nbsp;Longfei Yin ,&nbsp;Houpu Yang ,&nbsp;Guohua Wu ,&nbsp;Shu Wang","doi":"10.1016/j.vibspec.2025.103782","DOIUrl":"10.1016/j.vibspec.2025.103782","url":null,"abstract":"<div><div>Based on the characteristics of spectral data, Nabil Ibtehaz et al. (2023) proposed a generalized neural network architecture for Raman spectroscopy analysis, called RamanNet. This paper applies it to breast cancer screening and proposes an modified RamanNet method to optimize the classification performance of breast cancer and healthy individuals. The modified model accelerates convergence and reduces overfitting by incorporating L2 regularization, removing TripletLoss, and adjusting the learning rate. Results demonstrate that the modified RamanNet achieved a higher accuracy (96.0 ± 1.7 %) and sensitivity (96.8 ± 3.0 %) in distinguishing between breast cancer patients and healthy controls, outperforming both the 1D-CNN (accuracy: 91.8 ± 2.9 %; sensitivity: 89.3 ± 5.1 %) and the original RamanNet (accuracy: 92.5 ± 3.2 %; sensitivity: 94.6 ± 5.6 %). Furthermore, the model demonstrated enhancements in training time, convergence speed and stability, which provides a new technological approach for non-invasive and rapid breast cancer screening with great potential for clinical application.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103782"},"PeriodicalIF":2.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a data fusion strategy combining FT-NIR and Vis/NIR-HSI for non-destructive prediction of critical quality attributes in traditional Chinese medicine particles","authors":"Ziqian Wang ,&nbsp;Xinhao Wan ,&nbsp;Xiaorong Luo ,&nbsp;Ming Yang ,&nbsp;Xuecheng Wang ,&nbsp;Zhijian Zhong ,&nbsp;Qing Tao ,&nbsp;Zhenfeng Wu","doi":"10.1016/j.vibspec.2025.103780","DOIUrl":"10.1016/j.vibspec.2025.103780","url":null,"abstract":"<div><div>This study explores the complementary capabilities of Fourier Transform Near Infrared Spectroscopy (FT-NIR) and Visible/Near Infrared Hyperspectral Imaging (Vis/NIR-HSI) in developing a data fusion strategy to predict the critical quality attributes (CQAs) of Traditional Chinese Medicine Particles (TCMP). The research emphasizes integrating these techniques into an advanced process analytical technology (PAT) platform. By leveraging the unique strengths of FT-NIR for molecular characterization and Vis/NIR-HSI for spatial quality assessment, the study evaluates multiple data fusion strategies to enhance prediction accuracy. Twenty batches of TCMP were produced using fluidized bed granulation, and their properties were characterized using FT-NIR and Vis/NIR-HSI. Comparative analysis revealed that FT-NIR outperformed Vis/NIR-HSI in standalone predictions of moisture content and particle size. Advanced fusion schemes were then developed to combine the complementary information from both spectral ranges, resulting in partial least squares (PLS) models. Among the three fusion levels evaluated, the high-level fusion strategy achieved the most accurate predictions for flowability, particle size, and moisture content. This study demonstrates that high-level fusion of FT-NIR and Vis/NIR-HSI data can significantly improve the efficiency and accuracy of CQAs prediction for TCMP. Moreover, the proposed approach facilitates rapid and non-destructive quality analysis of granular medicines, enables real-time online monitoring, and offers practical insights into advancing automated drug safety process control.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103780"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143202533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-technique analysis of the mural materials and techniques in the 5th cave of the five temple grottoes in Subei, China","authors":"Ping Li ,&nbsp;Biwen Shui ,&nbsp;Bin Zhang ,&nbsp;Yufei Liu ,&nbsp;Zhiyuan Yin ,&nbsp;Qiang Cui","doi":"10.1016/j.vibspec.2025.103781","DOIUrl":"10.1016/j.vibspec.2025.103781","url":null,"abstract":"<div><div>As part of the Dunhuang grottoes, the Five Temple Grottoes are notable for their overlapping mural structures, painted over multiple dynasties, offering valuable insights into Dunhuang's cultural and artistic evolution. However, due to historical changes and both human and natural impacts, Cave 5 is in poor condition, with various mural diseases. Research on the materials and techniques used in the Five Temple Grottoes is limited. In this study, we employed polarizing microscopy, laser particle size analysis, FT-IR, XRD, and SEM-EDX to analyze the materials and techniques of the collapsed murals in Cave 5. Results showed that Cave 5 murals consist of multiple layers, including clay texture pillars and paint layers. The Northern Zhou Dynasty murals used hematite, calcite, muscovite, and talc, reflecting techniques similar to the Mogao Grottoes. The Northern Song Dynasty murals incorporated hematite, azurite, chlorite, calcite, and gypsum. Additionally, clay in the Northern Zhou Dynasty murals had smaller particle sizes but higher clay content. The use of straw fiber in the Northern Zhou Dynasty murals contrasts with the flax fiber used in the Northern Song Dynasty murals. This study aims to understand the artistic materials and technological characteristics of the murals in Cave 5 and to provide scientific support for their protection and restoration.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103781"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wine composition detection utilizing 1DCNN and the self-attention mechanism","authors":"Keda Chen,&nbsp;Shengwei Wang,&nbsp;Shenghui Liu","doi":"10.1016/j.vibspec.2025.103768","DOIUrl":"10.1016/j.vibspec.2025.103768","url":null,"abstract":"<div><div>This study proposes a one-dimensional convolutional autoencoder model that incorporates self-attention mechanisms—1DCNN-ATTENTION-SAE. This model solves the problem of unstable prediction performance in quantitative modeling of multiple components in infrared spectroscopy, especially when dealing with complex nonlinear problems involving severe overlap of characteristic peak bands and difficulty in capturing high-dimensional nonlinear features. The model effectively captures long-term dependencies in infrared spectral data and is particularly suitable for the rapid detection of key components such as pH, total phenols, total sugars, and alcohol in wine. On the ATR-FTIR dataset of dry red wine, the proposed model demonstrates robust performance, achieving a root mean square error (RMSE) of 2.017 g/L and a coefficient of determination (R²) of 0.967 g/L. The RMSE represents the average prediction error across the chemical properties measured (pH, total phenols, total sugars, and alcohol). Similarly, the R² value reflects the overall predictive accuracy of the model for these properties. Additionally, the 1DCNN-ATTENTION-SAE model was further optimized by integrating the DeepHealth algorithm, which is based on the TRANSFORMER structure, forming the hybrid DeepHealth &amp; 1DCNN-ATTENTION-SAE feature fusion model. When applied to the near-infrared spectral dataset of open-source pharmaceuticals to predict bioactivity values, the hybrid model achieved an RMSE of 3.262 g/L and an R² of 0.914 g/L, validating its transfer learning capability in handling \"cross-instrument, cross-wavelength\" infrared spectral data.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103768"},"PeriodicalIF":2.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}