Vibrational Spectroscopy最新文献_第2页

Fingerprint Raman spectroscopy for identification of hydration-dependent Brillouin shift variations in brain tumor tissue 指纹拉曼光谱识别脑肿瘤组织中水合依赖的布里渊位移变化

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-11-01 DOI: 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.

布里渊位移是纵向弹性模量的量度，它对细胞和组织的固体部分的含水量和弹性的变化都很敏感。拉曼光谱可以与布里渊光谱相结合，提供生物化学信息。在这项研究中，评估指纹区域的拉曼信号强度，以提取有关组织水化的额外信息，并将其与布里布鲁因位移联系起来。在780 nm激光激发下进行同步共聚焦布里渊和拉曼光谱。对相关生物分子（白蛋白和蔗糖）、凝胶（明胶和琼脂糖）溶液（浓度高达30 %）以及胶质细胞瘤类器官和人脑组织进行探针，研究800 - 1500 cm⁻¹ 光谱区域的拉曼强度和布里温位移。在所有被分析的溶液和凝胶中，拉曼信号强度、质量浓度和布里渊位移之间存在很强的线性相关性，而不同的总浓度相同的混合物具有相似的拉曼强度。在GBM类器官、人脑组织（癫痫性海马）和脑肿瘤（脑膜瘤）上，布里渊位移与拉曼强度存在不同程度的相关性，表明组织水化和生物力学对布里渊位移的贡献不同。综上所述，将指纹拉曼光谱与布里渊显微镜相结合，不仅有助于提取布里渊参数变化的生化信息，而且可以深入了解局部水合作用对布里渊位移变化的影响。

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引用次数: 0

Influence of the computational methods in the conformational assignment of experimental infrared spectra 计算方法对实验红外光谱构象分配的影响

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-11-01 DOI: 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。

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引用次数: 0

Research on quantitative analysis of single-component hydrocarbon gases based on optimized SVR with double-local strategy 基于双局部优化SVR策略的单组分烃类气体定量分析研究

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-11-01 DOI: 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.

在钻井工程中，准确测量钻井液中的气体浓度对有效的气体测井至关重要，因为准确性和速度对实时监测和决策具有重要影响。为了解决现场红外光谱用于单组分气体测量的局限性，例如模型不匹配，宽光谱范围内的冗余波数，以及导致处理缓慢和误差大的浓度波动，这些都不能满足气体测井的需求，本研究引入了一种优化的解决方案。该方法包括修改测量模型，利用局部特征波数消除冗余的光谱信息，划分局部校准集以减少浓度波动的影响。提出了一种基于区间随机蛙（IRF）的竞争自适应重加权抽样（CARS）算法，并结合甲虫天线搜索（BAS）算法优化支持向量回归（SVR）的建模方法。实验结果表明，所提出的IRF-CARS-BAS-SVR方法在单组分烃定量分析方面明显优于传统技术，平均预测精度超过98 %。该方法提高了基于红外的单组分气体定量的速度和精度，并已成功应用于多个钻井现场，满足了气测井的作业要求。

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引用次数: 0

A PCA-UVE based feature selection strategy for nutritional component quantification in milk powder using Raman spectroscopy 基于PCA-UVE的拉曼光谱定量奶粉营养成分特征选择策略

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-11-01 DOI: 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 RMSE_P 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.

确保奶粉营养成分的快速准确评估是乳品行业质量控制的关键。本文提出了一种基于拉曼光谱的分析框架，结合主成分分析（PCA）和无信息变量消除（UVE）进行特征选择，以优化光谱建模，提高检测性能。采用便携式拉曼系统采集40种商业奶粉样品的光谱，包括脱脂、低脂和全脂变体。该方法首先利用主成分分析（PCA）降维并识别信息丰富的光谱区域，然后利用UVE对光谱区域进行进一步细化以消除冗余特征。利用优化后的谱子集构建脂肪和蛋白质预测的偏最小二乘回归（PLSR）模型，R²值分别为0.9865和0.9751，RMSEP和计算成本大幅降低。与全谱模型相比，该方法在保持较高预测精度的同时，减少了70%以上 %的处理时间。本研究展示了将先进的化学计量方法与拉曼光谱相结合，在奶粉质量监测中进行高效、实时营养分析的潜力。

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引用次数: 0

In-situ SERS monitoring of light-gated reaction switching on magnetic-plasmonic CoFe2O4@TiO2@Ag nanorods 磁等离子体CoFe2O4@TiO2@Ag纳米棒上光门控反应开关的原位SERS监测

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-11-01 DOI: 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.

在这里，我们制造了磁性可回收的CoFe2O4@TiO2@Ag纳米棒（nr），用于光门控催化途径的原位表面增强拉曼散射（SERS）监测。由磁性CoFe2O4 NR核、TiO2中间层和Ag纳米颗粒（NPs）壳组成的分层结构，使罗丹明6 G （R6G）在低浓度（10−8 mol L−1）下的SERS检测具有良好的信号再现性。原位SERS揭示了4-硝基苯酚（4-NTP）在不同激发波长下的不同还原机制。在532 nm可见激光照射下，Ag纳米粒子的等离子体诱导热电子驱动4-NTP选择性转化为反式二巯基偶氮苯（DMAB），速率常数为0.118 min−1。相反，在365 nm紫外光下，tio2介导的电子转移促进顺式dmab的形成，速率常数为0.034 min−1。可逆顺反异构化是通过交替光源实现的。这项工作建立了CoFe2O4/TiO2/Ag NRs作为实时监测光门控催化的多功能平台，在能量转换，环境修复和选择性化学转化方面具有广阔的应用前景。

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引用次数: 0

Standardized protocol for unstimulated saliva analysis by ATR-FTIR spectroscopy ATR-FTIR光谱分析非刺激唾液的标准化方案

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-10-22 DOI: 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.

衰减全反射傅里叶变换红外光谱（ATR-FTIR）作为一种非侵入性技术，在分析唾液等生物流体的生化成分方面已经获得了相关性，在临床诊断方面的兴趣越来越大。然而，光谱质量和可重复性受到样品制备、唾液类型和干燥方法的强烈影响。本研究旨在标准化非刺激唾液的采集、处理和光谱采集方案。光谱从液体、冻干和风干的整个唾液和澄清唾液样品中获得。液体样品表现出很强的吸水性，掩盖了特征的生物分子带。冻干产生宏观上不均匀的残基，导致条带强度变化，重复性差。相比之下，室温风干得到均匀且可重现的光谱，确定3 µL为最佳进样量。通过离心获得的澄清唾液，通过减少来自细胞和颗粒物质的生物噪声，产生了具有更清晰和更一致的生物分子带的光谱，特别是在酰胺II区。虽然信号强度和信噪比值较低，但这些变化反映的是散射减少，而不是分子损失。Wilcoxon检验证实，样品类型之间仅在Amide II区域存在显著差异（p <； 0.05）。总的来说，风干澄清唾液提供了一个干净，稳定，可重复的光谱矩阵，支持其适用于可靠的生化和诊断ATR-FTIR应用。

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引用次数: 0

Throughput-enhanced grating spectrometers for fiber-optic Raman detection with deep learning-based spectral recovery 基于深度学习光谱恢复的光纤拉曼检测吞吐量增强光栅光谱仪

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-10-20 DOI: 10.1016/j.vibspec.2025.103860

Huijie Wang, Xu Liu, Zichun Yang, Lang Huang, Xinhang Lou, Jianbo Zhu, Linwei Shang, Jianhua Yin

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

200 μ m

and far larger than the detector pixel width of

15 μ m

. Through the combination of CNN classification and GAN reconstruction, low-resolution spectra of the 200-

μ

m-width slit with

\sim

7 times higher throughput can be recovered to coincide well with those of the 15-

μ

m-width slit, achieving the optimal spectral resolution. Moreover, the signal-to-noise ratio can be improved by

\sim

3 times on average, promoting more efficient weak-light detection with the flexible fiber-optic probes.

光栅光谱仪被广泛应用于单次光谱测量，具有较高的性能，但其固有的缺点是光学吞吐量和光谱分辨率之间存在冲突。入口狭缝作为关键器件，为了实现高光谱分辨率，需要窄的狭缝，这不可避免地限制了光吞吐量，通常会阻碍超弱拉曼检测。本文提出并初步探索了基于深度学习的光谱恢复方法，在数值上快速准确地消除随狭缝变宽而产生的光谱展宽，不需要进行复杂的物理修正和耗时的迭代计算。考虑到光谱复杂性对光谱恢复的可能影响，在使用卷积神经网络（CNN）对光谱进行峰数分割和分类之后，使用一组生成式对抗网络（gan）并行进行光谱重建。例如，对常见药物的光纤拉曼检测进行了光谱恢复，其中激发和收集光纤的芯直径均为200μm，远大于检测器的像元宽度15μm。通过CNN分类和GAN重构相结合的方法，恢复了吞吐量提高约7倍的200 μm宽狭缝的低分辨率光谱，使其与15 μm宽狭缝的低分辨率光谱吻合良好，达到了最佳的光谱分辨率。此外，信噪比可以平均提高约3倍，从而促进柔性光纤探头更有效地检测弱光。

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引用次数: 0

Lightweight double attention neural network based on Raman spectroscopy for diagnosis of osteoporosis 基于拉曼光谱的轻量双注意神经网络诊断骨质疏松症

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-10-18 DOI: 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.

骨质疏松症是一种常见的慢性骨代谢性疾病，其早期诊断对于预防骨折和延缓疾病进程具有重要意义。拉曼光谱作为一种无创、高通量的分子检测方法，在骨组织成分检测中显示出独特的优势。然而，受光谱数据的高维性、峰冗余性和生物可变性等因素的限制，传统的机器学习方法在特征提取和分类精度方面存在瓶颈。针对这一问题，本文提出了一种基于拉曼光谱的轻量级一维双注意神经网络（DAN），将编码器-解码器结构与空间通道双注意机制相结合，实现了骨质疏松症的高效智能诊断。本文提出的双注意模块通过模拟波段之间的位置依赖和通道之间的特征聚焦，通过两条独立的路径并行，有效地增强了模型对光谱结构和病理模式的感知能力。本文在真实的临床Raman数据集上对系统进行了验证，DAN在各项指标上都达到了最优的性能，准确率达到97.50 %，优于传统的机器学习模型和深度学习模型。同时，本文通过设计烧蚀实验，深入探讨了注意机制的贡献，结果表明，双注意机制在准确性和鲁棒性方面都明显优于仅采用单一空间或通道注意的模型。该模型参数数仅为0.11 M，推理开销低至0.01 GFlops，具有轻量级部署、良好的可解释性和医学适应性等优点，为未来基于光谱的骨质疏松辅助诊断提供了新的深度学习路径。

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引用次数: 0

Comparative assessment of serum dilution and pure samples for Raman-based oral cancer detection: Evaluating dilution rate performance 血清稀释和纯样品用于拉曼检测口腔癌的比较评估：评估稀释率性能

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-10-17 DOI: 10.1016/j.vibspec.2025.103858

Mukta Sharma , Ajay Kumar , Chia-Lung Tsai , Shiang-Fu Huang , Yu-Li Hsieh , Liann-Be Chang

Raman spectroscopy is a powerful non-invasive tool for biomolecular analysis, offering potential for early oral cancer detection. However, the impact of serum dilution rates on classification performance remains underexplored. This study evaluates the diagnostic efficacy of pure, 1:1, and 1:2 diluted serum samples for classifying malignant, premalignant, and normal groups using machine learning techniques. Serum samples from 80 subjects (33 malignant, 27 premalignant, and 20 normal) were analyzed via Raman spectroscopy. Spectral data were preprocessed and reduced using Principal Component Analysis (PCA), followed by classification using Support Vector Machines (SVM) and Random Forest (RF). Notably, the 1:2 dilution rate (DR) demonstrated comparable diagnostic performance to pure serum while preserving critical spectral features. For PCA-SVM under 1:2 DR dilution (with 90.5% accuracy) and pure serum conditions (with 92.5% accuracy), the sensitivity and specificity for malignant samples were 93.9% and 87.2%, & 91% and 94%, respectively. For normal samples, these values were 95% and 100% for the 1:2 dilution, and 95% and 98.3% for pure serum. Although both classifiers differentiated successfully, PCA-SVM demonstrated slightly better performance compared to PCA-RF across dilution rates. Bar charts confirmed consistent spectral trends across DRs. The study demonstrates the potential of Raman spectroscopy combined with PCA-SVM for accurate oral cancer detection. The selected 1:2 DR balances sample conservation with robust diagnostic performance, offering a practical and efficient approach for clinical applications. This work emphasizes the feasibility of using diluted biosamples for machine learning-based cancer diagnostics, achieving performance comparable to pure samples.

拉曼光谱是一种强大的非侵入性生物分子分析工具，为早期口腔癌检测提供了潜力。然而，血清稀释率对分类性能的影响仍未得到充分探讨。本研究评估了使用机器学习技术对纯、1:1和1:2稀释血清样本进行恶性、癌前和正常组分类的诊断效果。采用拉曼光谱对80例受试者（33例恶性，27例癌前病变，20例正常）的血清样本进行分析。利用主成分分析（PCA）对光谱数据进行预处理和约简，然后利用支持向量机（SVM）和随机森林（RF）进行分类。值得注意的是，1:2稀释率（DR）与纯血清的诊断性能相当，同时保留了关键的光谱特征。在1:2 DR稀释（准确率为90.5%）和纯血清条件下（准确率为92.5%），PCA-SVM对恶性样本的敏感性和特异性分别为93.9%和87.2%，91%和94%。对于正常样品，1:2稀释时，这些值分别为95%和100%，纯血清为95%和98.3%。尽管两种分类器都能成功区分，但PCA-SVM在稀释率上比PCA-RF表现出稍好的性能。柱状图证实了各dr间一致的光谱趋势。本研究证明了拉曼光谱结合PCA-SVM对口腔癌进行准确检测的潜力。所选择的1:2 DR平衡了样本保存与强大的诊断性能，为临床应用提供了实用而有效的方法。这项工作强调了使用稀释生物样本进行基于机器学习的癌症诊断的可行性，实现了与纯样本相当的性能。

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引用次数: 0

Multimodal deep learning with relative position matrix for simultaneous nitride quantification in water by Raman spectroscopy 基于相对位置矩阵的多模态深度学习拉曼光谱同时定量水中氮化物

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-10-13 DOI: 10.1016/j.vibspec.2025.103859

Chunhong Lai , Xian Chen , Xiaoming Jiang , Jinhong Xiang , Hao Tang

Monitoring nitride concentrations in water is critical for water quality protection. Specifically, nitrite is a critical indicator of fertilizer runoff and microbial activity in stagnant water bodies like dams and reservoirs, and its presence poses a direct threat to human health. Raman spectroscopy enables simultaneous detection of multiple substances but suffers from weak signals, overlapping peaks, and difficulty in quantitative analysis. This study overcomes these limitations by proposing a novel method integrating the Relative Position Matrix (RPM) and a multimodal deep learning model. First, 1D Raman spectra of potassium nitrate and sodium nitrite were transformed into 2D images using the relative position matrix method to enhance the feature richness. After that, 1D spectral data and 2D image data were extracted by a multimodal data fusion model, the simultaneous prediction of nitrate and nitrite concentrations through two outputs. The results show that the model achieved an average determination coefficient (R²) of 0.9615 in the test set, with an average mean square error (MSE) of 0.0053 and mean absolute error (MAE) of 0.0317. The practical feasibility of the method was verified through quantitative analysis of actual water samples. Overall, this research provides an algorithmic foundation for accurately monitoring nitride concentration in water based on Raman spectroscopy

监测水中氮化物浓度对保护水质至关重要。具体来说，亚硝酸盐是水坝、水库等死水水体中肥料径流和微生物活动的重要指标，其存在对人类健康构成直接威胁。拉曼光谱可以同时检测多种物质，但存在信号弱、峰重叠、定量分析困难等问题。本研究通过提出一种结合相对位置矩阵（RPM）和多模态深度学习模型的新方法来克服这些局限性。首先，利用相对位置矩阵法将硝酸钾和亚硝酸钠的一维拉曼光谱转换成二维图像，增强特征丰富度；之后，采用多模态数据融合模型提取一维光谱数据和二维图像数据，通过两个输出同时预测硝酸盐和亚硝酸盐浓度。结果表明，该模型在检验集中的平均决定系数（R2）为0.9615，平均均方误差（MSE）为0.0053，平均绝对误差（MAE）为0.0317。通过对实际水样的定量分析，验证了该方法的实际可行性。总体而言，本研究为基于拉曼光谱的水中氮化物浓度精确监测提供了算法基础

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