Pub Date : 2025-11-29DOI: 10.1016/j.vibspec.2025.103876
Hongda Chen
Direct absorption spectroscopy is extensively utilized in combustion diagnostics, offering valuable assistance in industrial production and aerospace applications. However, current approaches for processing substantial volumes of overlapped spectra encounter challenges related to slow processing speeds and inadequate precision. This research introduces a time series prediction model that integrates a Transformer self-attention mechanism with Long Short-Term Memory networks for spectroscopy and thermodynamic diagnostics, as well as spectral prediction in absorption spectroscopy. The proposed model proficiently adeptly captures spectral characteristics, including concentration, temperature, and pressure, from intricate unknown spectra. The model was verified in TDLAS. The prediction standard deviation for simulated spectra is less than 0.1, while the relative error for actual spectra is less than 1 %. Future advancements may involve the integration of spectral denoising and analysis techniques, along with few-shot learning methods, validation and optimization of the use of broadband spectrometers, to further optimize combustion gas detection solutions in industrial and aerospace domains.
{"title":"Multivariable prediction of concentration and temperature pressure from absorption spectra using B-LSTM-transformer model","authors":"Hongda Chen","doi":"10.1016/j.vibspec.2025.103876","DOIUrl":"10.1016/j.vibspec.2025.103876","url":null,"abstract":"<div><div>Direct absorption spectroscopy is extensively utilized in combustion diagnostics, offering valuable assistance in industrial production and aerospace applications. However, current approaches for processing substantial volumes of overlapped spectra encounter challenges related to slow processing speeds and inadequate precision. This research introduces a time series prediction model that integrates a Transformer self-attention mechanism with Long Short-Term Memory networks for spectroscopy and thermodynamic diagnostics, as well as spectral prediction in absorption spectroscopy. The proposed model proficiently adeptly captures spectral characteristics, including concentration, temperature, and pressure, from intricate unknown spectra. The model was verified in TDLAS. The prediction standard deviation for simulated spectra is less than 0.1, while the relative error for actual spectra is less than 1 %. Future advancements may involve the integration of spectral denoising and analysis techniques, along with few-shot learning methods, validation and optimization of the use of broadband spectrometers, to further optimize combustion gas detection solutions in industrial and aerospace domains.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103876"},"PeriodicalIF":3.1,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683319","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}
Pub Date : 2025-11-29DOI: 10.1016/j.vibspec.2025.103875
Caleb R. Whatley , Nuwan K. Wijewardane , Chamika A. Silva , Mary Love Tagert , Raju Bheemanahalli , Prem Parajuli
The wetland delineation process is primarily based on the visual recognition of anaerobic soil indicators by trained individuals, and is a complex and subjective task that is prone to error. Therefore, an objective alternative is needed to identify wetland soil; however, no such method currently exists that is rapid and easy to deploy. Accordingly, the objective was to evaluate soil spectroscopic classification approach as a rapid, deployable alternative by testing its feasibility to differentiate wetland from non-wetland soils. This study used visible-near infrared and mid-infrared (MIR) ranges for this task. A total of 440 wetland and non-wetland soils were sampled across Mississippi followed by obtaining visible/near-infrared and MIR spectra under both fresh and dried conditions. Support Vector Classification (SVC) and Random Forest (RF) methods were then used to classify spectra based on wetland/non-wetland status with a 75 %/25 % calibration and validation split. This split was repeated for 50 iterations to obtain randomized calibration and validation sets for model calibration and achieve average model performance. The average classification accuracy across all models was ∼91 %, with the highest accuracy of 99.6 % achieved on MIR spectra. The accuracy, precision, and recall scores showed similar performances between SVC and RF ranging their values from ∼80 % - 100 %. This study showed the reliability and ease of wetland determinations using spectroscopy as an objective and rapid wetland recognition method, while reducing the need for an expert for determination.
{"title":"Can we use visible-near infrared and mid infrared spectroscopy as a tool for wetland soil identification?","authors":"Caleb R. Whatley , Nuwan K. Wijewardane , Chamika A. Silva , Mary Love Tagert , Raju Bheemanahalli , Prem Parajuli","doi":"10.1016/j.vibspec.2025.103875","DOIUrl":"10.1016/j.vibspec.2025.103875","url":null,"abstract":"<div><div>The wetland delineation process is primarily based on the visual recognition of anaerobic soil indicators by trained individuals, and is a complex and subjective task that is prone to error. Therefore, an objective alternative is needed to identify wetland soil; however, no such method currently exists that is rapid and easy to deploy. Accordingly, the objective was to evaluate soil spectroscopic classification approach as a rapid, deployable alternative by testing its feasibility to differentiate wetland from non-wetland soils. This study used visible-near infrared and mid-infrared (MIR) ranges for this task. A total of 440 wetland and non-wetland soils were sampled across Mississippi followed by obtaining visible/near-infrared and MIR spectra under both fresh and dried conditions. Support Vector Classification (SVC) and Random Forest (RF) methods were then used to classify spectra based on wetland/non-wetland status with a 75 %/25 % calibration and validation split. This split was repeated for 50 iterations to obtain randomized calibration and validation sets for model calibration and achieve average model performance. The average classification accuracy across all models was ∼91 %, with the highest accuracy of 99.6 % achieved on MIR spectra. The accuracy, precision, and recall scores showed similar performances between SVC and RF ranging their values from ∼80 % - 100 %. This study showed the reliability and ease of wetland determinations using spectroscopy as an objective and rapid wetland recognition method, while reducing the need for an expert for determination.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"142 ","pages":"Article 103875"},"PeriodicalIF":3.1,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625077","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}
Pub Date : 2025-11-01DOI: 10.1016/j.vibspec.2025.103866
Jan Rix , Tina Leonidou , Achim Temme , Ortrud Uckermann , Roberta Galli
The Brillouin shift is a measure of the longitudinal elastic modulus, which is both sensitive to changes in water content and elasticity of the solid part of cells and tissues. Raman spectroscopy can be combined with Brillouin spectroscopy to provide biochemical information. In this study, the Raman signal intensity in the fingerprint region was evaluated to extract additional information about tissue hydration and relate it to the Brillouin shift. Simultaneous and colocalized confocal Brillouin and Raman spectroscopy was performed with laser excitation at 780 nm. Solutions of relevant biomolecules (albumin and sucrose) and gels (gelatin and agarose) with different concentrations up to 30 % as well as glioblastoma organoids and human brain tissue were probed, and the Raman intensity in the spectral region 800 – 1500 cm⁻¹ and Brillouin shift was investigated. A strong linear correlation was found between Raman signal intensity, mass concentration and Brillouin shift for all analyzed solutions and gels, while different mixtures with the same total concentrations had similar Raman intensities. Different degrees of correlation were found between Brillouin shift and Raman intensity on GBM organoids, human brain tissue (epileptic hippocampus) and brain tumor (meningioma), indicating different contributions of tissue hydration and biomechanics to the Brillouin shift. In conclusion, combining fingerprint Raman spectroscopy with Brillouin microscopy is not only useful for extracting biochemical information that highlights changes in Brillouin parameters, but may also provide insight into the effects of local hydration driving the changes of Brillouin shift.
{"title":"Fingerprint Raman spectroscopy for identification of hydration-dependent Brillouin shift variations in brain tumor tissue","authors":"Jan Rix , Tina Leonidou , Achim Temme , Ortrud Uckermann , Roberta Galli","doi":"10.1016/j.vibspec.2025.103866","DOIUrl":"10.1016/j.vibspec.2025.103866","url":null,"abstract":"<div><div>The Brillouin shift is a measure of the longitudinal elastic modulus, which is both sensitive to changes in water content and elasticity of the solid part of cells and tissues. Raman spectroscopy can be combined with Brillouin spectroscopy to provide biochemical information. In this study, the Raman signal intensity in the fingerprint region was evaluated to extract additional information about tissue hydration and relate it to the Brillouin shift. Simultaneous and colocalized confocal Brillouin and Raman spectroscopy was performed with laser excitation at 780 nm. Solutions of relevant biomolecules (albumin and sucrose) and gels (gelatin and agarose) with different concentrations up to 30 % as well as glioblastoma organoids and human brain tissue were probed, and the Raman intensity in the spectral region 800 – 1500 cm⁻¹ and Brillouin shift was investigated. A strong linear correlation was found between Raman signal intensity, mass concentration and Brillouin shift for all analyzed solutions and gels, while different mixtures with the same total concentrations had similar Raman intensities. Different degrees of correlation were found between Brillouin shift and Raman intensity on GBM organoids, human brain tissue (epileptic hippocampus) and brain tumor (meningioma), indicating different contributions of tissue hydration and biomechanics to the Brillouin shift. In conclusion, combining fingerprint Raman spectroscopy with Brillouin microscopy is not only useful for extracting biochemical information that highlights changes in Brillouin parameters, but may also provide insight into the effects of local hydration driving the changes of Brillouin shift.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103866"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525772","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}
Pub Date : 2025-11-01DOI: 10.1016/j.vibspec.2025.103863
Roel van de Ven, Herma M. Cuppen, Daria R. Galimberti
Recent advancements in experimental techniques have provided spectra for single conformers for relatively large flexible molecules, creating an ideal environment to benchmark computational methods on their ability to identify the conformers responsible for the experimentally measured spectra. Here, we performed benchmarks of different functionals and basis sets using three molecules: diphenylalanine, delta-9-tetrahydrocannabinol, and the 3-O-Acetyl-2,4,6-tri-O-methyl-gluco-D-pyranosyl cation. For this task, a new type of spectral similarity score was introduced, the Logarithmic Convoluted Cosine Similarity (LCCS), which is able to quantify spectral differences in terms of both frequency and intensity mismatches. Our results show that, as long as hybrid functionals are selected, the most crucial factor for the correct conformer assignment is the basis set. In particular, polarization functions were found to play a crucial role. We analyzed three additional aspects of the problem: the scan of the potential energy surface to find candidate conformers, pre-optimization of candidate conformers, and the selection from the pool of conformers based on the energy. Our data indicate that for scanning the potential energy surface, the DFTB3 semi-empirical method is a good compromise between accuracy and low computational cost. For the pre-optimization, we found that GGA functionals and a small basis set with a polarization functional already achieve sufficient accuracy. Instead, in the pre-selection based on energy, hybrid functionals should be preferred, and in any case, an energy window of at least 15 kJ/mol should be employed for the conformer selection.
实验技术的最新进展为相对较大的柔性分子提供了单一构象的光谱，创造了一个理想的环境，以基准计算方法识别实验测量光谱的构象的能力。在这里，我们使用三种分子：二苯丙氨酸、德尔塔-9-四氢大麻酚和3- o-乙酰基-2,4,6-三- o-甲基-葡萄糖- d -吡喃基阳离子，对不同的功能和基集进行了基准测试。为此，引入了一种新的频谱相似度评分，即对数卷积余弦相似度（LCCS），它能够从频率和强度两方面量化频谱差异。结果表明，在选择混合泛函的情况下，基集是决定正确赋形体的最关键因素。特别是发现极化函数起着至关重要的作用。我们还分析了该问题的另外三个方面：扫描势能面寻找候选构象，候选构象的预优化以及基于能量的构象池选择。我们的数据表明，对于扫描势能面，DFTB3半经验方法在精度和低计算成本之间取得了很好的折衷。对于预优化，我们发现GGA泛函和一个带有极化泛函的小基集已经达到了足够的精度。相反，在基于能量的预选择中，应优先选择混合官能团，并且在任何情况下，应使用至少15 kJ/mol的能量窗口来选择构象。
{"title":"Influence of the computational methods in the conformational assignment of experimental infrared spectra","authors":"Roel van de Ven, Herma M. Cuppen, Daria R. Galimberti","doi":"10.1016/j.vibspec.2025.103863","DOIUrl":"10.1016/j.vibspec.2025.103863","url":null,"abstract":"<div><div>Recent advancements in experimental techniques have provided spectra for single conformers for relatively large flexible molecules, creating an ideal environment to benchmark computational methods on their ability to identify the conformers responsible for the experimentally measured spectra. Here, we performed benchmarks of different functionals and basis sets using three molecules: diphenylalanine, delta-9-tetrahydrocannabinol, and the 3-O-Acetyl-2,4,6-tri-O-methyl-gluco-D-pyranosyl cation. For this task, a new type of spectral similarity score was introduced, the Logarithmic Convoluted Cosine Similarity (LCCS), which is able to quantify spectral differences in terms of both frequency and intensity mismatches. Our results show that, as long as hybrid functionals are selected, the most crucial factor for the correct conformer assignment is the basis set. In particular, polarization functions were found to play a crucial role. We analyzed three additional aspects of the problem: the scan of the potential energy surface to find candidate conformers, pre-optimization of candidate conformers, and the selection from the pool of conformers based on the energy. Our data indicate that for scanning the potential energy surface, the DFTB3 semi-empirical method is a good compromise between accuracy and low computational cost. For the pre-optimization, we found that GGA functionals and a small basis set with a polarization functional already achieve sufficient accuracy. Instead, in the pre-selection based on energy, hybrid functionals should be preferred, and in any case, an energy window of at least 15 kJ/mol should be employed for the conformer selection.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103863"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416971","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}
Pub Date : 2025-11-01DOI: 10.1016/j.vibspec.2025.103865
Gang Liu , Peng Han , Hai Yang , Yilin Yao , Haibo Liang
Accurate measurement of gas concentrations in drilling fluids is essential for effective gas logging in drilling engineering, as both accuracy and speed significantly influence real-time monitoring and decision-making. To address the limitations of on-site infrared spectroscopy for single-component gas measurements—such as model mismatches, redundant wavenumbers across broad spectral ranges, and substantial concentration fluctuations that lead to slow processing and large errors, which fail to meet gas logging needs—this study introduces an optimized solution. The approach involves modifying the measurement model, utilizing local characteristic wavenumbers to eliminate redundant spectral information, and dividing local calibration sets to reduce the effects of concentration fluctuations. A novel modeling technique is developed by enhancing Competitive Adaptive Reweighted Sampling (CARS) with Interval Random Frog (IRF) and combining it with the Beetle Antennae Search (BAS) algorithm to optimize Support Vector Regression (SVR). Experimental results demonstrate that the proposed IRF-CARS-BAS-SVR method significantly outperforms traditional techniques in the quantitative analysis of single-component hydrocarbons, achieving an average prediction accuracy exceeding 98 %. This method improves both the speed and precision of infrared-based quantification of single-component gases and has been successfully deployed at various drilling sites, meeting operational requirements for gas logging.
{"title":"Research on quantitative analysis of single-component hydrocarbon gases based on optimized SVR with double-local strategy","authors":"Gang Liu , Peng Han , Hai Yang , Yilin Yao , Haibo Liang","doi":"10.1016/j.vibspec.2025.103865","DOIUrl":"10.1016/j.vibspec.2025.103865","url":null,"abstract":"<div><div>Accurate measurement of gas concentrations in drilling fluids is essential for effective gas logging in drilling engineering, as both accuracy and speed significantly influence real-time monitoring and decision-making. To address the limitations of on-site infrared spectroscopy for single-component gas measurements—such as model mismatches, redundant wavenumbers across broad spectral ranges, and substantial concentration fluctuations that lead to slow processing and large errors, which fail to meet gas logging needs—this study introduces an optimized solution. The approach involves modifying the measurement model, utilizing local characteristic wavenumbers to eliminate redundant spectral information, and dividing local calibration sets to reduce the effects of concentration fluctuations. A novel modeling technique is developed by enhancing Competitive Adaptive Reweighted Sampling (CARS) with Interval Random Frog (IRF) and combining it with the Beetle Antennae Search (BAS) algorithm to optimize Support Vector Regression (SVR). Experimental results demonstrate that the proposed IRF-CARS-BAS-SVR method significantly outperforms traditional techniques in the quantitative analysis of single-component hydrocarbons, achieving an average prediction accuracy exceeding 98 %. This method improves both the speed and precision of infrared-based quantification of single-component gases and has been successfully deployed at various drilling sites, meeting operational requirements for gas logging.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103865"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525771","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}
Pub Date : 2025-11-01DOI: 10.1016/j.vibspec.2025.103867
Xiangchu Li , Yu Ding , Jianan Xu , Yihua He , Qiang Tan , Maoyuan Pang , Weiye Yu , Jinyi Li , Guang Yang , Xinxin Liu
Ensuring the rapid and accurate assessment of nutritional components in milk powder is essential for quality control in the dairy industry. In this study, a Raman spectroscopy-based analytical framework is proposed, integrating principal component analysis (PCA) and uninformative variable elimination (UVE) for feature selection, to optimize spectral modeling and enhance detection performance. A portable Raman system was employed to acquire spectra from 40 commercial milk powder samples, including skimmed, low-fat, and full-fat variants. PCA was initially used to reduce dimensionality and identify informative spectral regions, which were further refined using UVE to eliminate redundant features. The optimized spectral subset was utilized to construct partial least squares regression (PLSR) models for fat and protein prediction, achieving R² values of 0.9865 and 0.9751, respectively, with substantial reductions in RMSEP and computational cost. Compared to full-spectrum models, the proposed approach reduced processing time by over 70 %, while maintaining high prediction accuracy. This study demonstrates the potential of integrating advanced chemometric methods with Raman spectroscopy for efficient, real-time nutritional analysis in milk powder quality monitoring.
{"title":"A PCA-UVE based feature selection strategy for nutritional component quantification in milk powder using Raman spectroscopy","authors":"Xiangchu Li , Yu Ding , Jianan Xu , Yihua He , Qiang Tan , Maoyuan Pang , Weiye Yu , Jinyi Li , Guang Yang , Xinxin Liu","doi":"10.1016/j.vibspec.2025.103867","DOIUrl":"10.1016/j.vibspec.2025.103867","url":null,"abstract":"<div><div>Ensuring the rapid and accurate assessment of nutritional components in milk powder is essential for quality control in the dairy industry. In this study, a Raman spectroscopy-based analytical framework is proposed, integrating principal component analysis (PCA) and uninformative variable elimination (UVE) for feature selection, to optimize spectral modeling and enhance detection performance. A portable Raman system was employed to acquire spectra from 40 commercial milk powder samples, including skimmed, low-fat, and full-fat variants. PCA was initially used to reduce dimensionality and identify informative spectral regions, which were further refined using UVE to eliminate redundant features. The optimized spectral subset was utilized to construct partial least squares regression (PLSR) models for fat and protein prediction, achieving R² values of 0.9865 and 0.9751, respectively, with substantial reductions in RMSE<sub>P</sub> and computational cost. Compared to full-spectrum models, the proposed approach reduced processing time by over 70 %, while maintaining high prediction accuracy. This study demonstrates the potential of integrating advanced chemometric methods with Raman spectroscopy for efficient, real-time nutritional analysis in milk powder quality monitoring.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103867"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624133","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}
Pub Date : 2025-11-01DOI: 10.1016/j.vibspec.2025.103864
Shuang Wang , Zian Li , Wei Lv , Guolin Zhang , Xiaoqi Fu , Juan Yang
Here, we fabricate magnetically recyclable CoFe2O4@TiO2@Ag nanorods (NRs) for in-situ surface-enhanced Raman scattering (SERS) monitoring of light-gated catalytic pathways. The hierarchical structure, comprising a magnetic CoFe2O4 NR core, a TiO2 interlayer, and Ag nanoparticles (NPs) shell, enables the SERS detection of rhodamine 6 G (R6G) at a low concentration of 10−8 mol L−1 with excellent signal reproducibility. In-situ SERS reveals distinct reduction mechanisms for 4-nitrothiophenol (4-NTP) governed by the excitation wavelength. Under 532 nm visible laser irradiation, plasmon-induced hot electrons from Ag NPs drive the selective conversion of 4-NTP to trans-dimercaptoazobenzene (DMAB) with a rate constant of 0.118 min−1. In contrast, under 365 nm UV light, TiO2-mediated electron transfer promotes the formation of cis-DMAB at a rate constant of 0.034 min−1. Reversible cis-trans isomerization is achieved by alternating the light sources. This work establishes CoFe2O4/TiO2/Ag NRs as a versatile platform for monitoring light-gated catalysis in real time, with promising applications in energy conversion, environmental remediation, and selective chemical transformations.
{"title":"In-situ SERS monitoring of light-gated reaction switching on magnetic-plasmonic CoFe2O4@TiO2@Ag nanorods","authors":"Shuang Wang , Zian Li , Wei Lv , Guolin Zhang , Xiaoqi Fu , Juan Yang","doi":"10.1016/j.vibspec.2025.103864","DOIUrl":"10.1016/j.vibspec.2025.103864","url":null,"abstract":"<div><div>Here, we fabricate magnetically recyclable CoFe<sub>2</sub>O<sub>4</sub>@TiO<sub>2</sub>@Ag nanorods (NRs) for <em>in-situ</em> surface-enhanced Raman scattering (SERS) monitoring of light-gated catalytic pathways. The hierarchical structure, comprising a magnetic CoFe<sub>2</sub>O<sub>4</sub> NR core, a TiO<sub>2</sub> interlayer, and Ag nanoparticles (NPs) shell, enables the SERS detection of rhodamine 6 G (R6G) at a low concentration of 10<sup>−8</sup> mol L<sup>−1</sup> with excellent signal reproducibility. <em>In-situ</em> SERS reveals distinct reduction mechanisms for 4-nitrothiophenol (4-NTP) governed by the excitation wavelength. Under 532 nm visible laser irradiation, plasmon-induced hot electrons from Ag NPs drive the selective conversion of 4-NTP to <em>trans</em>-dimercaptoazobenzene (DMAB) with a rate constant of 0.118 min<sup>−1</sup>. In contrast, under 365 nm UV light, TiO<sub>2</sub>-mediated electron transfer promotes the formation of <em>cis</em>-DMAB at a rate constant of 0.034 min<sup>−1</sup>. Reversible <em>cis</em>-<em>trans</em> isomerization is achieved by alternating the light sources. This work establishes CoFe<sub>2</sub>O<sub>4</sub>/TiO<sub>2</sub>/Ag NRs as a versatile platform for monitoring light-gated catalysis in real time, with promising applications in energy conversion, environmental remediation, and selective chemical transformations.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103864"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416972","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}
Pub Date : 2025-10-22DOI: 10.1016/j.vibspec.2025.103862
Maricela Toro-Alzate , Ana Isabel Cañas-Gutierrez , Monica Tatiana Parada-Sanchez
Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy has gained relevance as a non-invasive technique for analyzing the biochemical composition of biofluids such as saliva, with increasing interest in clinical diagnostics. However, spectral quality and repeatability are strongly influenced by sample preparation, saliva type, and drying method. This study aimed to standardize a protocol for the collection, processing, and spectroscopic acquisition of unstimulated saliva. Spectra were obtained from liquid, lyophilized, and air-dried samples of both whole and clarified saliva.
Liquid samples exhibited strong water absorption, masking characteristic biomolecular bands. Lyophilization produced macroscopically heterogeneous residues, resulting in variable band intensity and poor repeatability. In contrast, air-drying at room temperature yielded uniform and reproducible spectra, with 3 µL identified as the optimal sample volume. Clarified saliva, obtained by centrifugation, produced spectra with sharper and more consistent biomolecular bands, particularly in the Amide II region, by reducing biological noise from cells and particulate matter. Although signal intensity and SNR values were lower, these changes reflected reduced scattering rather than molecular loss. The Wilcoxon test confirmed significant differences between sample types only for the Amide II region (p < 0.05).
Overall, air-dried clarified saliva provides a clean, stable, and reproducible spectral matrix, supporting its suitability for reliable biochemical and diagnostic ATR-FTIR applications.
{"title":"Standardized protocol for unstimulated saliva analysis by ATR-FTIR spectroscopy","authors":"Maricela Toro-Alzate , Ana Isabel Cañas-Gutierrez , Monica Tatiana Parada-Sanchez","doi":"10.1016/j.vibspec.2025.103862","DOIUrl":"10.1016/j.vibspec.2025.103862","url":null,"abstract":"<div><div>Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy has gained relevance as a non-invasive technique for analyzing the biochemical composition of biofluids such as saliva, with increasing interest in clinical diagnostics. However, spectral quality and repeatability are strongly influenced by sample preparation, saliva type, and drying method. This study aimed to standardize a protocol for the collection, processing, and spectroscopic acquisition of unstimulated saliva. Spectra were obtained from liquid, lyophilized, and air-dried samples of both whole and clarified saliva.</div><div>Liquid samples exhibited strong water absorption, masking characteristic biomolecular bands. Lyophilization produced macroscopically heterogeneous residues, resulting in variable band intensity and poor repeatability. In contrast, air-drying at room temperature yielded uniform and reproducible spectra, with 3 µL identified as the optimal sample volume. Clarified saliva, obtained by centrifugation, produced spectra with sharper and more consistent biomolecular bands, particularly in the Amide II region, by reducing biological noise from cells and particulate matter. Although signal intensity and SNR values were lower, these changes reflected reduced scattering rather than molecular loss. The Wilcoxon test confirmed significant differences between sample types only for the Amide II region (p < 0.05).</div><div>Overall, air-dried clarified saliva provides a clean, stable, and reproducible spectral matrix, supporting its suitability for reliable biochemical and diagnostic ATR-FTIR applications.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103862"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362839","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}
Grating spectrometers are widely utilized to perform the single-shot spectral measurement with relatively high performance, while inherently suffering from a conflict between optical throughput and spectral resolution. As a key component, the entrance slit is required to be narrow for high spectral resolution, inevitably limiting the optical throughput and typically hindering the ultra-weak Raman detection. In this work, the deep learning-based spectral recovery has been proposed and preliminarily explored to numerically eliminate the spectral broadening along with a wider slit in a rapid and accurate way, without demand for complicated physical modification and time-consuming iterative calculation. Given the possible influence of spectral complexity on the spectral recovery, spectral reconstruction is performed in parallel with a set of generative adversarial networks (GANs), following the spectral segmentation and classification in terms of peak number with a convolutional neural network (CNN). For instance, the spectral recovery has been performed on the fiber-optic Raman detection of common drugs, where all the core diameters of excitation and collection fibers are and far larger than the detector pixel width of . Through the combination of CNN classification and GAN reconstruction, low-resolution spectra of the 200-m-width slit with 7 times higher throughput can be recovered to coincide well with those of the 15-m-width slit, achieving the optimal spectral resolution. Moreover, the signal-to-noise ratio can be improved by 3 times on average, promoting more efficient weak-light detection with the flexible fiber-optic probes.
{"title":"Throughput-enhanced grating spectrometers for fiber-optic Raman detection with deep learning-based spectral recovery","authors":"Huijie Wang, Xu Liu, Zichun Yang, Lang Huang, Xinhang Lou, Jianbo Zhu, Linwei Shang, Jianhua Yin","doi":"10.1016/j.vibspec.2025.103860","DOIUrl":"10.1016/j.vibspec.2025.103860","url":null,"abstract":"<div><div>Grating spectrometers are widely utilized to perform the single-shot spectral measurement with relatively high performance, while inherently suffering from a conflict between optical throughput and spectral resolution. As a key component, the entrance slit is required to be narrow for high spectral resolution, inevitably limiting the optical throughput and typically hindering the ultra-weak Raman detection. In this work, the deep learning-based spectral recovery has been proposed and preliminarily explored to numerically eliminate the spectral broadening along with a wider slit in a rapid and accurate way, without demand for complicated physical modification and time-consuming iterative calculation. Given the possible influence of spectral complexity on the spectral recovery, spectral reconstruction is performed in parallel with a set of generative adversarial networks (GANs), following the spectral segmentation and classification in terms of peak number with a convolutional neural network (CNN). For instance, the spectral recovery has been performed on the fiber-optic Raman detection of common drugs, where all the core diameters of excitation and collection fibers are <span><math><mrow><mn>200</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and far larger than the detector pixel width of <span><math><mrow><mn>15</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>. Through the combination of CNN classification and GAN reconstruction, low-resolution spectra of the 200-<span><math><mi>μ</mi></math></span>m-width slit with <span><math><mo>∼</mo></math></span>7 times higher throughput can be recovered to coincide well with those of the 15-<span><math><mi>μ</mi></math></span>m-width slit, achieving the optimal spectral resolution. Moreover, the signal-to-noise ratio can be improved by <span><math><mo>∼</mo></math></span>3 times on average, promoting more efficient weak-light detection with the flexible fiber-optic probes.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103860"},"PeriodicalIF":3.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362736","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}
Pub Date : 2025-10-18DOI: 10.1016/j.vibspec.2025.103861
Haoshaqiang Zhang , Xuguang Zhou , Cheng Chen
Osteoporosis is a common chronic bone metabolic disease, and its early diagnosis is important for preventing fractures and delaying the disease process. Raman spectroscopy, as a non-invasive and high-throughput molecular detection method, has shown unique advantages in bone tissue composition detection. However, limited by the high dimensionality, peak redundancy and biological variability of spectral data, traditional machine learning methods have bottlenecks in feature extraction and classification accuracy. To address this problem, this paper proposes a lightweight one-dimensional Double Attention Neural Network (DAN) based on Raman spectra, combining an encoder-decoder structure with a spatial-channel double attention mechanism for efficient intelligent diagnosis of osteoporosis. The proposed double-attention module effectively enhances the model's ability to perceive spectral structures and pathological patterns by modeling the position dependence between bands and feature focusing between channels in parallel via two independent paths. In this paper, the system is validated on a real clinical Raman dataset, and the DAN achieves optimal performance in all kinds of indexes, with an accuracy of 97.50 %, which is better than the traditional machine learning model and deep learning model. At the same time, this paper explores the contribution of the attention mechanism in depth by designing ablation experiments, and the results show that the double attention mechanism is significantly better than the model that only adopts a single spatial or channel attention in terms of both accuracy and robustness. With a parameter count of only 0.11 M and an inference overhead as low as 0.01 GFlops, the model has the advantage of lightweight deployment, as well as good interpretability and medical adaptability, which provides a new deep learning path for future spectral-based assisted diagnosis of osteoporosis.
{"title":"Lightweight double attention neural network based on Raman spectroscopy for diagnosis of osteoporosis","authors":"Haoshaqiang Zhang , Xuguang Zhou , Cheng Chen","doi":"10.1016/j.vibspec.2025.103861","DOIUrl":"10.1016/j.vibspec.2025.103861","url":null,"abstract":"<div><div>Osteoporosis is a common chronic bone metabolic disease, and its early diagnosis is important for preventing fractures and delaying the disease process. Raman spectroscopy, as a non-invasive and high-throughput molecular detection method, has shown unique advantages in bone tissue composition detection. However, limited by the high dimensionality, peak redundancy and biological variability of spectral data, traditional machine learning methods have bottlenecks in feature extraction and classification accuracy. To address this problem, this paper proposes a lightweight one-dimensional Double Attention Neural Network (DAN) based on Raman spectra, combining an encoder-decoder structure with a spatial-channel double attention mechanism for efficient intelligent diagnosis of osteoporosis. The proposed double-attention module effectively enhances the model's ability to perceive spectral structures and pathological patterns by modeling the position dependence between bands and feature focusing between channels in parallel via two independent paths. In this paper, the system is validated on a real clinical Raman dataset, and the DAN achieves optimal performance in all kinds of indexes, with an accuracy of 97.50 %, which is better than the traditional machine learning model and deep learning model. At the same time, this paper explores the contribution of the attention mechanism in depth by designing ablation experiments, and the results show that the double attention mechanism is significantly better than the model that only adopts a single spatial or channel attention in terms of both accuracy and robustness. With a parameter count of only 0.11 M and an inference overhead as low as 0.01 GFlops, the model has the advantage of lightweight deployment, as well as good interpretability and medical adaptability, which provides a new deep learning path for future spectral-based assisted diagnosis of osteoporosis.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"141 ","pages":"Article 103861"},"PeriodicalIF":3.1,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362734","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}
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