Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy最新文献_第5页

A near-infrared viscosity probe for dynamic imaging of multilevel drug-induced liver injury 近红外黏度探头用于多级别药物性肝损伤的动态成像。

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-13 DOI: 10.1016/j.saa.2026.127470

Ziyi Wang , Keying Yang , Chen Yang , Bing Xue , Kaifu Ma , Zuonian Jin , Cailing Fan , Libo Jiang , Wei Shu

Drug-induced liver injury (DILI) disrupts hepatocellular homeostasis during the early phase. However, conventional detection methods often only become apparent after the injury has advanced. Cellular viscosity is a characteristic of the intracellular microenvironment, which regulates diffusion, membrane fluidity, organelle transport, and signaling, and can change rapidly under stress. As DILI can elicit mitochondrial dysfunction, endoplasmic reticulum stress, and lipid droplet remodeling, localized viscosity changes emerge early in the injury process. Here we report a near-infrared viscosity probe named WZY-1 based on a pyridinium-aryl molecular rotor. In high-viscosity media, the restriction of intramolecular rotation suppresses the twisted intramolecular charge transfer (TICT) and enhances the emission intensity. WZY-1 provides long-wavelength near-infrared emission with a large Stokes shift, maintains stable signals, shows tolerance to common ions and reactive species, and displays good biocompatibility. At the cellular level, WZY-1 can distinguish hepatocellular carcinoma cells from normal hepatic cells, and detect endogenous viscosity changes. In DILI models, it visualizes viscosity changes in cells, tissue, and live mice. Taken together, WZY-1 enables noninvasive, in situ, real-time readout of viscosity and supports early assessment of DILI across cellular, tissue, and animal levels.

药物性肝损伤（DILI）在早期阶段破坏肝细胞稳态。然而，传统的检测方法往往只有在损伤进展后才能显现出来。细胞黏度是细胞内微环境的一种特征，它调节扩散、膜流动性、细胞器运输和信号传导，在胁迫下可以迅速改变。由于DILI可引起线粒体功能障碍、内质网应激和脂滴重塑，因此局部粘度变化在损伤过程中较早出现。本文报道了一种基于吡啶-芳基分子转子的近红外粘度探针WZY-1。在高粘度介质中，分子内旋转的限制抑制了分子内扭曲电荷转移（TICT），增强了发射强度。WZY-1具有斯托克斯位移大的长波近红外发射，保持信号稳定，对常见离子和活性物质具有耐受性，具有良好的生物相容性。在细胞水平上，WZY-1可以区分肝癌细胞和正常肝细胞，检测内源性黏度变化。在DILI模型中，它可以可视化细胞、组织和活小鼠的粘度变化。总之，WZY-1能够无创、原位、实时读取粘度，并支持在细胞、组织和动物水平上对DILI进行早期评估。

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引用次数: 0

Detection of residual microbial biomarkers in bacterial cellulose using laser-induced fluorescence spectroscopy 激光诱导荧光光谱法检测细菌纤维素中残留的微生物生物标志物

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-13 DOI: 10.1016/j.saa.2026.127475

Petr Larionov , Nikolay Maslov , Natalia Pogorelova , Ilya Rozhin , Natalya Sarnitskaya , Vyacheslav Stupak , Irina Kirilova , Andrey Korytkin , Ilya Digel

Bacterial cellulose (BC) is a promising biomaterial for medical and biotechnological applications. However, microbial contaminants and their metabolic residues remain a critical limitation for its clinical use. Many of the BC purity tests are labor-intensive and time-consuming. This study investigates the feasibility of using laser-induced fluorescence (LIF) spectroscopy for monitoring microbial contamination in BC.

BC samples were obtained from a Medusomyces gisevii consortium and subjected to various purification protocols (alkaline, detergent and oxidative treatments). LIF spectra were recorded across 220–290 nm excitation wavelengths and analyzed chemometrically. For better interpretation of the results, the same samples were examined by laser scanning confocal microscopy (LSM).

The results reveal that both native and treated BC samples exhibit fluorescence features associated with tryptophan and tyrosine, indicative of microbial residues. Treatment with NaOH effectively reduced tryptophan-associated signals, while hydrogen peroxide diminished tyrosine-related fluorescence. None of the purification strategies completely eliminated these signals. A good correlation between the LIF and the more labor-consuming LSM data was observed. LIF showed the capability of rapid and reliable differentiation between treatment variants and provided spectral fingerprints linked to residual contamination. Future work may focus on standardizing LIF-based diagnostic protocols and integrating them into biotechnological workflows for contamination monitoring.

细菌纤维素（BC）是一种很有前景的医学和生物技术生物材料。然而，微生物污染物及其代谢残留物仍然是其临床应用的关键限制。许多BC纯度测试是劳动密集型和耗时的。本研究探讨了利用激光诱导荧光（LIF）光谱法监测BC中微生物污染的可行性。BC样品来自一个吉isevii Medusomyces财团，并进行了各种纯化方案（碱性，洗涤剂和氧化处理）。在220-290 nm激发波长范围内记录LIF光谱，并进行化学计量学分析。为了更好地解释结果，用激光扫描共聚焦显微镜（LSM）检查了相同的样品。结果表明，原生和处理过的BC样品都表现出与色氨酸和酪氨酸相关的荧光特征，表明微生物残留。NaOH处理有效地降低了色氨酸相关信号，而过氧化氢则降低了酪氨酸相关荧光。没有一种净化策略能完全消除这些信号。观察到LIF与更耗费人力的LSM数据之间存在良好的相关性。LIF显示了快速可靠区分处理变体的能力，并提供了与残留污染相关的光谱指纹图谱。未来的工作可能侧重于标准化基于生命动力学的诊断方案，并将其整合到污染监测的生物技术工作流程中。

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引用次数: 0

SERS-based deep learning approach for early detection of gestational diabetes mellitus 基于sers的深度学习方法用于妊娠期糖尿病的早期检测

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-13 DOI: 10.1016/j.saa.2026.127472

Huizhen Lin , Jiawang Chen , Yiming Chen , Dechan Lu

Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.

妊娠期糖尿病（GDM）的早期和准确诊断对于改善孕产妇和新生儿结局以及降低妊娠不良事件的风险至关重要。然而，目前临床对GDM的筛查方法在检测速度、灵敏度、便捷性等方面仍存在局限性，难以满足临床对早期妊娠快速筛查的需求。为了解决这一问题，我们提出了一种基于表面增强拉曼光谱（SERS）结合深度学习的GDM早期诊断策略，旨在实现快速准确的早期筛查。采用基于银纳米粒子（Ag NPs）的底物获得血清的特征SERS光谱。提出了一种融合主成分分析（PCA）降维和一维卷积神经网络（1D-CNN）特征学习的PCA- cnn模型。PCA-CNN模型有效地从血清SERS光谱中提取潜在的生物标志物特征，诊断准确率为93.7%，敏感性和特异性分别为0.95和0.93。此外，整个检测过程可在30 min内完成，每个样品所需血清量约为2.5 μL，且预处理最少，突出了效率和实用性。本研究为GDM早期筛查提供了一种兼具高诊断性能和临床适用性的新方法，为GDM的早期干预和临床管理提供了有希望的技术支持。

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引用次数: 0

Fabrication of AAO-based 3D particle-in-cavity nanostructures for ultrasensitive SERS detection 用于超灵敏SERS检测的aao基三维腔内粒子纳米结构的制备。

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-13 DOI: 10.1016/j.saa.2026.127473

Jun Dong , Yimeng Fan , Ziqing Fang , Guanwei Feng , Qingyan Han , Chengyun Zhang , Rong Chen , Jinchao Miao , Jianxia Qi , Wei Gao

Surface-enhanced Raman scattering (SERS) has emerged as a powerful tool for food safety analysis. We present a three-dimensional particle-in-cavity substrate that utilizes plasmonic cavity resonances for highly sensitive detection. Fabricated by a convenient transfer of AuNPs onto an anodic aluminum oxide (AAO) template, this design facilitates scalable production. The synergistic cavity-particle interplay yields a 4.24-fold SERS enhancement over a conventional flat Si substrate. The substrate demonstrated high sensitivity with a Rh6G enhancement factor of 1.77 × 10⁹, excellent uniformity showcasing relative standard deviations of 9.17% for Rh6G and 8.54% for crystal violet, and strong log-concentration linearity with correlation coefficients reaching 0.988 for Rh6G and 0.989 for Crystal Violet. It maintained 60% of its initial signal intensity after 30 days of storage and achieved an aspartame detection limit as low as 0.0078 g/L, demonstrating its excellent detection capability.

表面增强拉曼散射（SERS）已成为食品安全分析的有力工具。我们提出了一种三维腔内粒子衬底，利用等离子体腔共振进行高灵敏度检测。通过将aunp方便地转移到阳极氧化铝（AAO）模板上，该设计便于规模化生产。与传统的平面Si衬底相比，协同腔-粒子相互作用产生了4.24倍的SERS增强。该底物具有较高的灵敏度，Rh6G的增强因子为1.77 × 109，均匀性好，Rh6G的相对标准偏差为9.17%，结晶紫的相对标准偏差为8.54%，Rh6G的对数浓度线性较强，相关系数为0.988，结晶紫为0.989。贮藏30天后，其信号强度仍保持在初始信号强度的60%，阿斯巴甜的检出限低至0.0078 g/L，显示出优良的检测能力。

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引用次数: 0

A dual-probe SERS aptasensor for ultrasensitive detection of acrylamide in food 用于食品中丙烯酰胺超灵敏检测的双探针SERS配体传感器。

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-13 DOI: 10.1016/j.saa.2025.127322

Xiaoying Yang, Qian Liu, Lisha Xiang, Xiang Li, Tianxiang Li, Chao Kang, Dongmei Chen, Wanliang Yang

This study developed an aptamer sensor based on surface-enhanced Raman scattering (SERS) for the highly sensitive and selective detection of acrylamide (AAm) in food. Au@4MBA@Ag core-shell nanoparticles were synthesized and functionalized with thiol-modified AAm aptamers (Apt) to serve as signal probes. Meanwhile, Fe₃O₄@PEI@Ag magnetic nanoparticles were prepared and modified with complementary DNA (cDNA) to act as capture probes. The two probes were combined through Apt-cDNA hybridization to form a complete SERS sensing system. The structure and surface properties of Fe₃O₄@PEI@Ag were systematically characterized using various characterization methods, and its SERS enhancement performance was validated using Rhodamine 6G (R6G). Similarly, Au@4MBA@Ag was characterized to confirm its excellent SERS activity and reproducibility. The constructed SERS aptamer sensor achieves a detection limit as low as 3.26 × 10⁻¹⁰ M for AAm, and the aptamer sensor shows no response to structural analogues (such as acrylic acid and methacrylamide), demonstrating excellent specificity. In actual sample testing, the recovery rate was 90%–110%, highly consistent with HPLC detection results (recovery rate of 90%–110%). Therefore, this sensor offers advantages such as ease of operation, rapid detection, and strong resistance to interference, providing a reliable new method for trace detection of AAm in food.

本研究开发了一种基于表面增强拉曼散射（SERS）的适体传感器，用于食品中丙烯酰胺（AAm）的高灵敏度和选择性检测。合成了Au@4MBA@Ag核壳纳米粒子，并以巯基修饰的AAm适配体（Apt）进行功能化，作为信号探针。同时，制备了Fe₃O₄@PEI@Ag磁性纳米颗粒，并用互补DNA （cDNA）修饰作为捕获探针。通过Apt-cDNA杂交将两种探针结合，形成完整的SERS传感体系。采用多种表征方法系统表征了Fe₃O₄@PEI@Ag的结构和表面性能，并用罗丹明6G （R6G）验证了其SERS增强性能。同样，对Au@4MBA@Ag进行了表征，证实了其优异的SERS活性和再现性。所构建的SERS适体传感器对AAm的检测限低至3.26 × 10-10 M，且适体传感器对结构类似物（如丙烯酸和甲基丙烯酰胺）无响应，具有良好的特异性。在实际样品检测中，回收率为90% ~ 110%，与HPLC检测结果（回收率为90% ~ 110%）高度一致。因此，该传感器具有操作方便、检测速度快、抗干扰能力强等优点，为食品中AAm的痕量检测提供了一种可靠的新方法。

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引用次数: 0

An endoplasmic reticulum-targeting NIR fluorescent probe for viscosity imaging in vitro and vivo 一种内质网靶向近红外荧光探针，用于体外和体内黏度成像。

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-12 DOI: 10.1016/j.saa.2026.127471

Jian-Hua Tang , Jing-Jing Yu , Jun-Tao Niu , Tong Han , Jia-Le Cui , Yi-Ran Di , Ting Liang , Yan-Fei Kang , Hao-Jun Fan

The endoplasmic reticulum (ER), a central organelle, play critical roles in protein synthesis, folding and detoxification. Viscosity within the ER lumen is recognized as an essential physical property for maintaining its normal functions, and its dysregulation has been associated with numerous diseases and aging processes. Thus, detecting change of viscosity was meaningful in ER. In this work, a near-infrared (NIR) fluorescent probe (BEQ-ER) with a classic D-π-A structure is designed to measure viscosity fluctuation in ER relying on twisted intramolecular charge transfer (TICT) mechanism. BEQ-ER exhibited strong fluorescence at 682 nm under conditions of high viscosity due to suppressed intramolecular rotation. Moreover, the image results showed BEQ-ER can precisely target ER with a colocalization coefficient of 0.964, and high viscosity was detected in cancer cells. Importantly, BEQ-ER was shown to selectively illuminate tumor tissues in 4 T1 tumor-bearing mice. Therefore, this work provided a valuable tool for investigating disease mechanisms and progression through real-time monitoring of ER viscosity.

内质网（ER）是一种中枢细胞器，在蛋白质合成、折叠和解毒过程中起着至关重要的作用。内质网腔内的粘度被认为是维持其正常功能的基本物理特性，其失调与许多疾病和衰老过程有关。因此，检测内质网黏度的变化是有意义的。本文设计了一种具有经典D-π-A结构的近红外荧光探针（BEQ-ER），利用分子内电荷转移（TICT）机制来测量内质网中的粘度波动。由于抑制了分子内旋转，BEQ-ER在高粘度条件下在682 nm处表现出较强的荧光。此外，图像结果表明，BEQ-ER可以精确靶向ER，共定位系数为0.964，并且在癌细胞中检测到高粘度。重要的是，BEQ-ER被证明可以选择性地照亮4只T1荷瘤小鼠的肿瘤组织。因此，通过实时监测内质网黏度，这项工作为研究疾病机制和进展提供了有价值的工具。

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引用次数: 0

Comparative and exploratory study of ATR and diffuse reflectance mid-infrared spectroscopy for coal property analysis ATR与漫反射中红外光谱在煤物性分析中的比较与探索性研究

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-12 DOI: 10.1016/j.saa.2026.127467

Yu Liu, Jing-Yan Li, Yu-Peng Xu, Pu Chen, Dan Liu, Xiao-Li Chu

To evaluate mid-infrared sampling geometries for rapid coal analysis, attenuated total reflectance (ATR) and diffuse reflectance FTIR (DRF) were systematically compared, and multimodal fusion was explored. A total of 200 coal samples were analyzed for six key quality indices: ash, calorific value, volatile matter, fixed carbon, moisture, and sulfur. During data preprocessing, extended multiplicative scatter correction (EMSC) was applied to improve spectral stability, followed by correlation-based wavelength selection and cross-validated optimization of latent variables to construct partial least squares (PLS) regression models for each property. Notably, this study establishes a unified and reproducible benchmarking framework to disentangle sampling-geometry effects (surface-sensitive ATR and bulk-sensitive DRF) under strictly identical preprocessing, variable-selection, and cross-validation rules, and interprets the observed performance differences via chemically meaningful spectral contribution. In addition, we systematically benchmark three fusion levels (low/mid/high) within the same framework to clarify when multimodal integration is beneficial and when it is not. DRF achieved the most accurate ash prediction, whereas ATR performed better for volatile matter and moisture; calorific value and fixed carbon were comparable. Sulfur prediction remained challenging for both modalities. Low- and mid-level fusion showed no consistent synergistic gain, while high-level fusion improved prediction for five properties. Overall, the study provides actionable guidance for selecting FTIR modality and fusion strategy for practical coal quality assessment.

为了评估快速煤分析的中红外采样几何形状，系统地比较了衰减全反射（ATR）和漫反射FTIR (DRF)，并探索了多模态融合。共分析了200个煤样的6个关键质量指标：灰分、热值、挥发物、固定碳、水分和硫。在数据预处理过程中，采用扩展乘法散射校正（EMSC）提高光谱稳定性，然后进行基于相关性的波长选择和交叉验证的潜在变量优化，构建各属性的偏最小二乘（PLS）回归模型。值得注意的是，本研究建立了一个统一的、可重复的基准测试框架，在严格相同的预处理、变量选择和交叉验证规则下，分离采样几何效应（表面敏感的ATR和体积敏感的DRF），并通过化学有意义的光谱贡献来解释观察到的性能差异。此外，我们在同一框架内系统地对三种融合水平（低/中/高）进行基准测试，以阐明何时多模态集成是有益的，何时不是。DRF对灰分预测最准确，而ATR对挥发物和水分的预测效果更好；热值和固定碳具有可比性。对于这两种模式，硫预测仍然具有挑战性。低水平和中等水平的融合没有一致的协同增益，而高水平的融合改善了对五种特性的预测。总体而言，该研究为实际煤质评价中FTIR模式和融合策略的选择提供了可操作的指导。

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引用次数: 0

Flower-like FeMoO4 nanoparticles as a peroxidase mimic for sensitive colorimetric immunoassay of CEA 花状FeMoO4纳米颗粒作为过氧化物酶模拟物用于CEA的灵敏比色免疫测定

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-12 DOI: 10.1016/j.saa.2026.127468

Yuwei Pan , Xiaoyan Deng , Rong Zhang , Liuyan You , Jiaqing Guo , Youxiu Lin , Dianping Tang , Wenqiang Lai

Early and accessible detection of tumor biomarkers requires robust colorimetric readouts that can operate in complex matrices. We report flower-like hierarchical FeMoO₄ nanoparticles functioning as a peroxidase-like nanozyme and enable a cascade immunoassay for carcinoembryonic antigen. The nanoparticles were obtained by a solvothermal route and exhibit a single crystalline FeMoO₄ phase with uniform Fe, Mo, and O distributions, mixed Fe³⁺ and Fe²⁺ states, and a measurable fraction of surface non-lattice oxygen species (O_ads, including hydroxyl-related oxygen and adsorbed/weakly bound oxygen). These features support efficient H₂O₂ activation and TMB oxidation. FeMoO₄ was integrated with a glucose oxidase (GOx)-based enzyme signal reporter, in which GOx and the detection antibody (Ab2) were co-immobilized on gold nanoparticles (AuNPs) to form GOx-AuNP-Ab2 conjugate. In a sandwich immunoassay format, the captured enzyme-labeled conjugate generates H₂O₂ in proportion to the antigen concentration, which is subsequently converted by FeMoO₄ into a stable colorimetric signal at 652 nm. Reaction conditions were optimized for both the chromogenic branch and the enzymatic branch, and kinetic analysis gave Michaelis-Menten behavior with K_m values of 0.78 mM for H₂O₂ and 0.40 mM for TMB. Radical scavenging identified superoxide and hydroxyl radicals as the main active species, with singlet oxygen as a minor contributor. The CEA assay achieved a linear range of 0.1 to 60 ng mL⁻¹ and a limit of detection of 63 pg mL⁻¹, with a one month retention of 93% of the initial absorbance. Results for six clinical serum samples showed good agreement with a commercial ELISA at the 95% confidence level, and one CEA-negative serum was included as a negative control. These findings establish FeMoO₄ as a stable and manufacturable nanozyme platform for colorimetric immunoassays.

早期和可获得的肿瘤生物标志物检测需要强大的比色读数，可以在复杂的基质中操作。我们报道了花状分层的FeMoO4纳米颗粒作为过氧化物酶样纳米酶的功能，并使癌胚抗原的级联免疫测定成为可能。通过溶剂热法获得纳米颗粒，呈现出Fe、Mo和O均匀分布的单晶FeMoO4相，Fe3+和Fe2+混合态，表面非晶格氧（O_ads，包括羟基相关氧和吸附/弱结合氧）的可测量分数。这些特性支持高效的H2O2活化和TMB氧化。FeMoO4与基于葡萄糖氧化酶（GOx）的酶信号报告蛋白结合，将GOx与检测抗体（Ab2）共固定在金纳米颗粒（AuNPs）上，形成GOx- aunp -Ab2偶联物。在三明治免疫分析中，捕获的酶标记的偶联物与抗原浓度成比例地产生H2O2，随后由FeMoO4在652nm处转化为稳定的比色信号。对显色分支和酶促分支的反应条件进行了优化，动力学分析表明，H2O2和TMB的Km值分别为0.78 mM和0.40 mM，具有Michaelis-Menten行为。自由基清除鉴定超氧自由基和羟基自由基是主要的活性物种，单线态氧是次要的贡献者。CEA测定的线性范围为0.1 ~ 60 ng mL - 1，检测限为63 pg mL - 1，一个月的保留率为初始吸光度的93%。6份临床血清样本的结果与商用ELISA在95%的置信水平上吻合良好，并将1份cea阴性血清作为阴性对照。这些发现表明，FeMoO4是一种稳定的、可制造的纳米酶平台，可用于比色免疫测定。

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引用次数: 0

A research on applying the diffusion model algorithm for Infrared and Raman spectroscopy data augmentation to improve the accuracy of diseases 应用扩散模型算法增强红外和拉曼光谱数据以提高疾病诊断精度的研究。

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-12 DOI: 10.1016/j.saa.2026.127466

Xiangnan Chen , Cheng Chen , Xuguang Zhou , Xiaoyi Lv , Chen Chen

In recent years, Raman and Infrared spectroscopy have become important tools in disease diagnosis due to their high sensitivity and non-invasive detection advantages. Combined with deep learning methods, spectral data can achieve high-precision identification of a wide range of diseases. However, deep learning models rely on large amounts of high-quality data. In practice, spectral data often faces challenges such as limited sample size, noise interference, and device variability, leading to model overfitting and poor generalization. To overcome this bottleneck, this paper proposes a spectral data generation method based on a diffusion model. By encoding temporal and category label information separately and combining it with a multi-head attention mechanism, the method extracts the overall morphology and local subtle features of the spectrum at multiple scales. In the reverse reconstruction stage, the noise distribution is estimated based on implicit modeling and cross-attention is used to generate spectra of different categories under different labels. This method achieves accurate denoising under conditional constraints and progressively reconstructs information such as the positions of characteristic spectral peaks. This method generates high-quality, continuous spectral data, effectively enhancing the scale and quality of the dataset. Experimental results demonstrate that the proposed method can generate high-fidelity and diverse synthetic spectral samples. The Pearson correlation coefficients for the thyroid and SLE infrared datasets reached high values of 0.97719 and 0.9914, respectively, and the similarities for the Thyroid and SLE Raman datasets reached high values of 0.9531 and 0.9747, respectively. This method effectively expands the training dataset and alleviates data scarcity and uneven distribution. The diffusion model was applied to two disease diagnosis tasks for validation. Under the condition of optimal gain ratio,the model trained with augmented data significantly improved generalization performance and diagnostic accuracy. In the infrared spectroscopy classification task, the accuracy of SLE disease on EfficientNet improved from 69.70% to 90.91%, and the accuracy of benign and malignant thyroid tumors on EfficientNet improved from 66.67% to 89.47%. In the Raman spectroscopy classification task, the accuracy of SLE disease on MLP improved from 87.88% to 90.91%, and the accuracy of benign and malignant thyroid tumors on the Transformer model improved from 87.22% to 92.98%.This study provides an effective generative augmentation framework for small-sample spectral data analysis, with strong theoretical value and application prospects.

近年来，拉曼光谱和红外光谱以其高灵敏度和无创检测优势成为疾病诊断的重要工具。结合深度学习方法，光谱数据可以实现对大范围疾病的高精度识别。然而，深度学习模型依赖于大量高质量的数据。在实际应用中，频谱数据经常面临样本容量有限、噪声干扰和设备可变性等挑战，导致模型过拟合和泛化不良。为了克服这一瓶颈，本文提出了一种基于扩散模型的光谱数据生成方法。该方法通过对时间和类别标签信息分别进行编码，并结合多头注意机制，在多个尺度下提取光谱的整体形态和局部细微特征。在反向重建阶段，基于隐式建模估计噪声分布，并利用交叉注意生成不同标签下不同类别的频谱。该方法实现了在条件约束下的精确去噪，并逐步重建特征谱峰位置等信息。该方法生成了高质量的连续光谱数据，有效地提高了数据集的规模和质量。实验结果表明，该方法能够生成高保真度和多样化的合成光谱样本。甲状腺和SLE红外数据集的Pearson相关系数分别达到较高值0.97719和0.9914，甲状腺和SLE拉曼数据集的相似度分别达到较高值0.9531和0.9747。该方法有效地扩展了训练数据集，缓解了数据稀缺和分布不均的问题。将扩散模型应用于两个疾病诊断任务进行验证。在最佳增益比条件下，增强数据训练后的模型显著提高了泛化性能和诊断准确率。在红外光谱分类任务中，对SLE疾病的准确率从69.70%提高到90.91%，对甲状腺良恶性肿瘤的准确率从66.67%提高到89.47%。在拉曼光谱分类任务中，MLP对SLE疾病的准确率从87.88%提高到90.91%，Transformer模型对甲状腺良恶性肿瘤的准确率从87.22%提高到92.98%。本研究为小样本光谱数据分析提供了一种有效的生成增强框架，具有很强的理论价值和应用前景。

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引用次数: 0

Machine learning assisted raman spectroscopy for the classification of ovarian cancer cells 机器学习辅助拉曼光谱用于卵巢癌细胞的分类

IF 4.6 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Pub Date : 2026-01-10 DOI: 10.1016/j.saa.2026.127465

Yong-Jiang Li , Ye-Cheng Sun , Hao-Ran Li , Meng-Zhen Li , Ya-Ruo Gao , Tao Zhu , Hao Wen , Chun-Dong Xue , Xu-Qu Hu , Zhuo Yang , Kai-Rong Qin

Ovarian cancer is one of the most lethal gynecological malignancies, asymptomatic early progression, ineffective screening, and high histological heterogeneity. Accurate subtype classification and detection of chemotherapy resistance are critical for guiding personalized treatment strategies. Raman spectroscopy offers a label-free, non-destructive means of capturing biochemical fingerprints of cells, but its clinical potential is hindered by high spectral complexity and subtle inter-class variations. This study presents a machine learning–assisted Raman spectroscopy framework for the classification of ovarian cancer cell subtypes and their cisplatin resistance phenotypes. Raman spectra were acquired from normal ovarian epithelial cells (IOSE-80), four ovarian cancer cell lines (A2780, SKOV3, OVCAR-3, ES-2), and cisplatin-resistant variants (A2780-DDP, SKOV3-DDP). Three computational models were developed and systematically compared: a principal component analysis–support vector machine (PCA–SVM) algorithm and two convolutional neural network (CNN-Enhance and CNN-BiLSTM). Classification performance was assessed across three tasks: (i) discrimination of normal versus malignant cells, (ii) differentiation of cancer cells from their cisplatin-resistant variants, and (iii) classification of distinct cancer subtypes. Results show that Raman spectra reveal distinctive biochemical differences between normal and malignant cells, particularly in protein-, lipid-, and nucleic acid–related peaks. Both PCA–SVM and CNN achieved high classification accuracy (>90%) in most tasks, with PCA–SVM demonstrating greater stability and superior performance in subtype classification, while CNN showed advantages in specific cell-type detection. Notably, PCA–SVM achieved up to 100% accuracy in differentiating cisplatin-resistant phenotypes. These findings demonstrate that integrating Raman spectroscopy with machine learning enables label-free, and accurate classification of ovarian cancer subtypes and drug resistance, offering a promising pathway toward minimally invasive precision diagnostics and personalized cancer treatment planning.

卵巢癌是最致命的妇科恶性肿瘤之一，早期无症状进展，筛查无效，组织学异质性高。准确的亚型分类和化疗耐药检测对于指导个性化治疗策略至关重要。拉曼光谱提供了一种无标记、非破坏性的方法来捕获细胞的生化指纹，但其临床潜力受到高光谱复杂性和微妙的类间差异的阻碍。本研究提出了一种机器学习辅助的拉曼光谱框架，用于卵巢癌细胞亚型及其顺铂耐药表型的分类。从正常卵巢上皮细胞（IOSE-80）、4种卵巢癌细胞系（A2780、SKOV3、OVCAR-3、ES-2）和顺铂耐药变体（A2780- ddp、SKOV3- ddp）中获得拉曼光谱。开发了三种计算模型并进行了系统比较：主成分分析-支持向量机（PCA-SVM）算法和两个卷积神经网络（CNN-Enhance和CNN-BiLSTM）。通过三个任务评估分类性能：(i)正常细胞与恶性细胞的区分，（ii）癌细胞与顺铂耐药变体的分化，以及（iii）不同癌症亚型的分类。结果表明，正常细胞和恶性细胞的拉曼光谱显示出明显的生化差异，特别是在蛋白质、脂质和核酸相关峰上。PCA-SVM和CNN在大多数任务中都取得了较高的分类准确率（>90%），其中PCA-SVM在亚型分类方面表现出更大的稳定性和更优越的性能，而CNN在特定细胞类型检测方面表现出优势。值得注意的是，PCA-SVM在区分顺铂耐药表型方面达到了100%的准确率。这些发现表明，将拉曼光谱与机器学习相结合，可以实现无标签、准确的卵巢癌亚型和耐药性分类，为微创精确诊断和个性化癌症治疗计划提供了一条有希望的途径。

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