Vibrational Spectroscopy最新文献_第7页

SERS analysis of saliva and its key components: The effects of various collection methods, sample dilution, excitation wavelengths, and enhancing substrates 唾液及其主要成分的 SERS 分析：各种采集方法、样品稀释、激发波长和增强基质的影响

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-07 DOI: 10.1016/j.vibspec.2025.103787

Michaela Klenotová , Pavel Matějka

Recently, human saliva has become a subject of research as an excellent material for patient-friendly diagnostics. An increasing number of diagnostic tests utilize saliva due to its easy and noninvasive collection, eliminating the patient's stress. Simultaneously, developing Surface-Enhanced Raman Scattering (SERS) spectroscopy offers new possibilities for analyzing saliva's composition. Saliva is a complex biological material; many factors influence its composition, including medication use, diseases, stress, hormone levels, diet, age, and hydration. This complexity raises the question of whether it is possible to observe and definitively attribute changes in specific substances through SERS spectra. One of the key questions we posed is how the SERS spectrum will change with an increased level of α-amylase 1 A (AMY1A), an enzyme marker of acute stress. AMY1A forms complexes with proline-rich proteins (PRP). Thus, we examined whether similar spectral changes are observed with a PRP level increase in saliva. Another focus was lysozyme C (LYZ C), a nonspecific marker of infectious diseases. We examined how increased levels of LYZ C affect SERS spectra, particularly considering its sensitivity to changes in the ionic composition of saliva and its complexation with PRP and lactoferrin (LF). Moreover, we explored whether the albumin (HSA) level, which plays a vital role in regulating osmotic pressure, influences LYZ C activity and how it is manifested in SERS. Furthermore, we investigated the effect of saliva dilution and collection methods on SERS spectra. We searched for correlations with significant components such as AMY1A, HSA, LYZ C, LF, and Poly-L-proline (PLP is an analog of PRP). We showed the role of gold (Au) and silver (Ag) substrates, comparing the spectral differences. Solving the issues is crucial for the ability of SERS techniques to detect and/or monitor biomolecules in saliva and can lead to significant advancements in noninvasive diagnostics.

最近，人类唾液作为一种对患者友好的诊断材料而成为研究的对象。越来越多的诊断测试利用唾液，因为它容易和无创收集，消除病人的压力。同时，表面增强拉曼散射（SERS）光谱的发展为分析唾液成分提供了新的可能性。唾液是一种复杂的生物材料；许多因素影响其成分，包括药物使用、疾病、压力、激素水平、饮食、年龄和水合作用。这种复杂性提出了一个问题，即是否有可能通过SERS光谱观察和明确地归因于特定物质的变化。我们提出的关键问题之一是SERS谱如何随着α-淀粉酶1 A （AMY1A）水平的增加而变化，AMY1A是急性应激的酶标志物。AMY1A与富含脯氨酸的蛋白（PRP）形成复合物。因此，我们研究了唾液中PRP水平升高是否观察到类似的光谱变化。另一个焦点是溶菌酶C (LYZ C)，一种传染性疾病的非特异性标志物。我们研究了LYZ C水平的增加如何影响SERS光谱，特别是考虑到它对唾液离子组成变化的敏感性以及它与PRP和乳铁蛋白（LF）的络合。此外，我们还探讨了在调节渗透压中起重要作用的白蛋白（HSA）水平是否影响LYZ C活性及其在SERS中的表现。此外，我们还研究了唾液稀释度和收集方法对SERS谱的影响。我们搜索了与AMY1A、HSA、LYZ C、LF和poly - l -脯氨酸（PLP是PRP的类似物）等重要成分的相关性。我们展示了金（Au）和银（Ag）衬底的作用，比较了光谱差异。解决这些问题对于SERS技术检测和/或监测唾液中的生物分子的能力至关重要，并且可以导致无创诊断的重大进步。

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引用次数: 0

Authentication analysis of animal fats adulteration in nail polish simulation using Raman spectroscopy coupled with chemometrics 利用拉曼光谱结合化学计量学对指甲油中动物脂肪掺假进行鉴定分析

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-06 DOI: 10.1016/j.vibspec.2025.103785

Nurrulhidayah Ahmad Fadzillah , Amal Elgharbawy , Mohammad Aizat Jamaluddin , Nur Azira Tukiran , Anjar Windarsih , Abdul Rohman , Siti Jamilah Mohd Sukri , Nurul Widad Fitri Muhammad , Anis Hamizah Hamid

Cosmetics are being used daily by many people, and their consumption is on the rise every year. These products are adulterated with cheaper alternatives to increase their profit. As more cosmetics are available in the market, the authenticity of halal cosmetics has raised much concern among Muslim consumers throughout the world. Therefore, authentication analysis of cosmetic products is urgently needed. This study was conducted to detect beef tallow (BT), chicken fat (CF), lard (LD), and mutton fat (MF) in nail polish using Raman spectrometry combined with chemometrics. Partial least square-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were successfully used to differentiate animal fats into four subclasses. In addition, partial least square (PLS) and orthogonal PLS (OPLS) regression were adequate to detect and predict the levels of BT, CF, LD, and MF in nail polish with R²> 0.990 both in calibration and validation models. The best prediction model for BT was from OPLS at the wavenumber range of 100–3200 cm⁻¹ with R²> 0.990 and RMSEC as well as RMSEP lower than 2.0 %. Meanwhile PLS model demonstrated the best model to predict CF, LD, and MF was the PLS with R²> 0.990 and RMSEC as well as RMSEP around 1–2.40 %. This study revealed the potential application of Raman spectroscopy in combination with chemometrics as an effective and efficient technique for authenticating nail polish base formulation adulterated with animal fats.

许多人每天都在使用化妆品，而且化妆品的消费量每年都在上升。这些产品掺入了更便宜的替代品以增加利润。随着市场上越来越多的化妆品，清真化妆品的真实性引起了全世界穆斯林消费者的极大关注。因此，迫切需要对化妆品进行认证分析。本研究采用拉曼光谱法结合化学计量学检测指甲油中的牛油（BT）、鸡脂（CF）、猪油（LD）和羊肉脂肪（MF）。偏最小二乘判别分析（PLS-DA）和层次聚类分析（HCA）成功地将动物脂肪分为四个亚类。此外，偏最小二乘（PLS）和正交PLS （ops）回归足以检测和预测指甲油中BT、CF、LD和MF的水平，校准和验证模型的R2>； 0.990。在100 ~ 3200 cm−1波数范围内，ops预测BT的最佳模型为R2>； 0.990，RMSEC和RMSEP均小于2.0 %。PLS模型预测CF、LD和MF的最佳模型为R2>； 0.990，RMSEC和RMSEP均在1-2.40 %左右。本研究揭示了拉曼光谱与化学计量学相结合作为一种有效和高效的鉴定掺假动物脂肪指甲油底料配方的潜在应用前景。

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引用次数: 0

Detection of Early Subtle Bruising in Strawberries Using VNIR Hyperspectral Imaging and Deep Learning 利用近红外高光谱成像和深度学习技术检测草莓早期细微瘀伤

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-04 DOI: 10.1016/j.vibspec.2025.103786

Runze Feng , Xin Han , Yubin Lan , Xinyue Gou , Jingzhi Zhang , Huizheng Wang , Shuo Zhao , Fanxia Kong

Detecting early surface bruising in strawberries during postharvest storage is crucial for maintaining product quality and reducing waste. In this paper, we combined visible-near infrared hyperspectral imaging (VNIR-HSI) technology with deep learning methods to efficiently detect early surface bruising in strawberries. Specifically, we created a hyperspectral image dataset of strawberries, captured in the 454–998 nm wavelength range at five intervals: 1, 12, 24, 36, and 48 hours after applying four levels of bruising: none, slight, moderate, and severe. To address the challenges of a limited sample size and redundant hyperspectral data, we employed data augmentation and two feature wavelength extraction techniques: Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS). We then developed several classification models, including SVM, CNN, CNN-LSTM, and CNN-BiLSTM. Experimental results showed that the CNN-BiLSTM model, which used feature wavelengths selected by CARS, achieved a 97.8 % classification accuracy for detecting slight bruising 12 hours post-treatment, with an average bruised area of 24.09 ± 6.38 mm². This performance surpassed the SVM, CNN, and CNN-LSTM models by 14.7, 10.5, and 4.5 percentage points, respectively. This study effectively classified early bruising in strawberries and visualized bruised areas, demonstrating significant improvements in detection and classification of early bruising, particularly for smaller areas.

在草莓采后储藏过程中，及早发现草莓表面的瘀伤对保持产品质量和减少浪费至关重要。在本文中，我们将可见-近红外高光谱成像（VNIR-HSI）技术与深度学习方法相结合，以有效地检测草莓的早期表面瘀伤。具体来说，我们创建了草莓的高光谱图像数据集，在454-998 nm波长范围内以五个间隔：1、12、24、36和48 小时在应用四个级别的瘀伤后捕获：无、轻微、中度和严重。为了解决有限的样本量和冗余的高光谱数据的挑战，我们采用了数据增强和两种特征波长提取技术：无信息变量消除（UVE）和竞争自适应重加权采样（CARS）。然后，我们开发了几种分类模型，包括SVM、CNN、CNN- lstm和CNN- bilstm。实验结果表明，使用CARS选择的特征波长的CNN-BiLSTM模型在处理12 小时后检测轻微擦伤的分类准确率达到97.8 %，平均擦伤面积为24.09 ± 6.38 mm²。该性能分别超过SVM、CNN和CNN- lstm模型14.7、10.5和4.5个百分点。这项研究有效地对草莓的早期瘀伤进行了分类，并对瘀伤区域进行了可视化，显示了早期瘀伤的检测和分类的显着改进，特别是对于较小的区域。

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引用次数: 0

Advances in single-molecule surface-enhanced Raman spectroscopy (SERS) for biosensing 用于生物传感的单分子表面增强拉曼光谱（SERS）的研究进展

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-03 DOI: 10.1016/j.vibspec.2025.103784

Huaizhou Jin , Yanlong Cai , Chenhui Song , Shangzhong Jin , Qiang Lin

Single-molecule (SM) detection and manipulation have revolutionized the field of biosensing, enabling unprecedented insights into the heterogeneity, dynamics, and interactions of biomolecules. This review focuses on the latest advances in single molecule Surface Enhanced Raman Spectroscopy (SM-SERS) techniques and approaches to confirm SM events, and examines four major approaches: bi-analyte SERS (BiASERS), plasmonic trapping, nanopore/slits, and chemical binding. We will discuss the development of these techniques as well as fabrication and application plasmonic nanostructures, and will explore the integration of these methods. Furthermore, we will discuss the challenges and future perspectives in the SM-SERS and the confirmation of SM events, focusing on improving sensitivity, reproducibility, and the ability to probe sub-angstrom molecular dynamics in order to provide a comprehensive overview.

单分子（SM）检测和操作已经彻底改变了生物传感领域，使人们对生物分子的异质性、动力学和相互作用有了前所未有的了解。本文综述了单分子表面增强拉曼光谱（SM-SERS）技术和方法的最新进展，并研究了四种主要方法：双分析物表面增强拉曼光谱（BiASERS）、等离子体捕获、纳米孔/狭缝和化学结合。我们将讨论这些技术的发展以及等离子体纳米结构的制造和应用，并将探索这些方法的集成。此外，我们将讨论SM- sers和SM事件确认的挑战和未来前景，重点是提高灵敏度，再现性和探测亚埃分子动力学的能力，以便提供一个全面的概述。

引用次数: 0

Uncovering the vibrational modes of zwitterion glycine in aqueous solution 揭示两性离子甘氨酸在水溶液中的振动模式

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-01 DOI: 10.1016/j.vibspec.2025.103783

Mark Christie , Mozhdeh Mohammadpour , Jan Sefcik , Karen Faulds , Karen Johnston

Vibrational spectroscopy is widely employed to probe and characterise chemical, biological and biomedical samples. Glycine solutions are relevant in a variety of biological and chemical systems, yet the reported experimental vibrational wavenumbers of the glycine zwitterion, which is the dominant species in aqueous solution, are inconsistent and incomplete. This study presents a procedure that obtained a complete set of vibrational frequencies for the glycine zwitterion in aqueous solution, apart from the two lowest wavenumber modes which are available from a previous THz study. Vibrational spectra were measured using IR and Raman spectroscopy, to obtain both IR and Raman-active modes for a range of different glycine solution concentrations using four different instruments. Insight from a literature survey of density functional theory calculations in implicit and explicit water was used to guide the deconvolution of the experimental spectra into vibrational modes, giving 22 out of 24 vibrational wavenumbers with a standard error of less than 3 cm⁻¹. This thorough analysis of the glycine vibrational spectra has enabled missing and erroneous wavenumbers in literature to be identified, and the systematic procedure for determining vibrational modes will pave the way for deeper quantitative analysis of glycine systems, and serve as a benchmark for computational method development.

振动光谱学广泛用于探测和表征化学、生物和生物医学样品。甘氨酸溶液与多种生物和化学系统有关，但作为水溶液中的优势种，甘氨酸两性离子的实验振动波数不一致且不完整。本研究提出了一个程序，获得了水溶液中甘氨酸两性离子的一整套振动频率，除了两个最低波数模式，这两个模式是从以前的太赫兹研究中获得的。利用红外光谱和拉曼光谱测量振动光谱，获得不同甘氨酸溶液浓度范围内使用四种不同仪器的红外和拉曼活性模式。利用对隐式和显式水的密度泛函理论计算的文献调查的见解，将实验光谱的反褶积引导到振动模式，给出24个振动波数中的22个，标准误差小于3 cm−1。对甘氨酸振动谱的深入分析使文献中缺失和错误的波数得以识别，并且确定振动模式的系统程序将为甘氨酸系统的更深入定量分析铺平道路，并作为计算方法开发的基准。

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引用次数: 0

An modified RamanNet model integrated with serum Raman spectroscopy for breast cancer screening 结合血清拉曼光谱的改进拉曼网模型用于乳腺癌筛查

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-06 DOI: 10.1016/j.vibspec.2025.103782

Ningning Sun , Fei Xie , Longfei Yin , Houpu Yang , Guohua Wu , Shu Wang

Based on the characteristics of spectral data, Nabil Ibtehaz et al. (2023) proposed a generalized neural network architecture for Raman spectroscopy analysis, called RamanNet. This paper applies it to breast cancer screening and proposes an modified RamanNet method to optimize the classification performance of breast cancer and healthy individuals. The modified model accelerates convergence and reduces overfitting by incorporating L2 regularization, removing TripletLoss, and adjusting the learning rate. Results demonstrate that the modified RamanNet achieved a higher accuracy (96.0 ± 1.7 %) and sensitivity (96.8 ± 3.0 %) in distinguishing between breast cancer patients and healthy controls, outperforming both the 1D-CNN (accuracy: 91.8 ± 2.9 %; sensitivity: 89.3 ± 5.1 %) and the original RamanNet (accuracy: 92.5 ± 3.2 %; sensitivity: 94.6 ± 5.6 %). Furthermore, the model demonstrated enhancements in training time, convergence speed and stability, which provides a new technological approach for non-invasive and rapid breast cancer screening with great potential for clinical application.

Nabil Ibtehaz等人（2023）根据光谱数据的特点，提出了一种用于拉曼光谱分析的广义神经网络架构，称为RamanNet。本文将其应用于乳腺癌筛查，提出了一种改进的RamanNet方法来优化乳腺癌与健康个体的分类性能。改进后的模型通过引入L2正则化、去除TripletLoss和调整学习率来加速收敛并减少过拟合。结果表明,改性RamanNet达到更高的精度(96.0 ±1.7  %)和敏感(96.8 ±3.0  %)在区分乳腺癌患者和健康对照组,表现优于1 d-cnn(精度:91.8 ±2.9  %;灵敏度：89.3 ± 5.1 %)和原始RamanNet(精度：92.5 ± 3.2 %；灵敏度:94.6 ±5.6  %)。此外，该模型在训练时间、收敛速度和稳定性方面均有所提高，为无创快速乳腺癌筛查提供了一种新的技术途径，具有很大的临床应用潜力。

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引用次数: 0

Development of a data fusion strategy combining FT-NIR and Vis/NIR-HSI for non-destructive prediction of critical quality attributes in traditional Chinese medicine particles 结合FT-NIR和Vis/NIR-HSI的数据融合策略用于中药颗粒关键质量属性的无损预测

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-01 DOI: 10.1016/j.vibspec.2025.103780

Ziqian Wang , Xinhao Wan , Xiaorong Luo , Ming Yang , Xuecheng Wang , Zhijian Zhong , Qing Tao , Zhenfeng Wu

This study explores the complementary capabilities of Fourier Transform Near Infrared Spectroscopy (FT-NIR) and Visible/Near Infrared Hyperspectral Imaging (Vis/NIR-HSI) in developing a data fusion strategy to predict the critical quality attributes (CQAs) of Traditional Chinese Medicine Particles (TCMP). The research emphasizes integrating these techniques into an advanced process analytical technology (PAT) platform. By leveraging the unique strengths of FT-NIR for molecular characterization and Vis/NIR-HSI for spatial quality assessment, the study evaluates multiple data fusion strategies to enhance prediction accuracy. Twenty batches of TCMP were produced using fluidized bed granulation, and their properties were characterized using FT-NIR and Vis/NIR-HSI. Comparative analysis revealed that FT-NIR outperformed Vis/NIR-HSI in standalone predictions of moisture content and particle size. Advanced fusion schemes were then developed to combine the complementary information from both spectral ranges, resulting in partial least squares (PLS) models. Among the three fusion levels evaluated, the high-level fusion strategy achieved the most accurate predictions for flowability, particle size, and moisture content. This study demonstrates that high-level fusion of FT-NIR and Vis/NIR-HSI data can significantly improve the efficiency and accuracy of CQAs prediction for TCMP. Moreover, the proposed approach facilitates rapid and non-destructive quality analysis of granular medicines, enables real-time online monitoring, and offers practical insights into advancing automated drug safety process control.

本研究探讨了傅里叶变换近红外光谱（FT-NIR）和可见光/近红外高光谱成像（Vis/NIR-HSI）在开发数据融合策略以预测中药颗粒（TCMP）的关键质量属性（CQAs）方面的互补能力。该研究强调将这些技术集成到先进的过程分析技术（PAT）平台中。通过利用FT-NIR在分子表征和Vis/NIR-HSI在空间质量评估方面的独特优势，研究评估了多种数据融合策略，以提高预测精度。采用流化床造粒法制备了20批中药制剂，并利用FT-NIR和Vis/NIR-HSI对其性能进行了表征。对比分析表明，FT-NIR在独立预测水分含量和粒径方面优于Vis/NIR-HSI。然后开发了先进的融合方案来结合来自两个光谱范围的互补信息，从而产生偏最小二乘（PLS）模型。在评估的三个融合水平中，高水平融合策略对流动性、粒径和水分含量的预测最为准确。本研究表明，FT-NIR和Vis/NIR-HSI数据的高水平融合可以显著提高中药cqa预测的效率和准确性。此外，所提出的方法有助于颗粒药物的快速和无损质量分析，实现实时在线监测，并为推进自动化药物安全过程控制提供实用见解。

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引用次数: 0

Multi-technique analysis of the mural materials and techniques in the 5th cave of the five temple grottoes in Subei, China 苏北五庙石窟第五窟壁画材料与技法的多技术分析

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-01 DOI: 10.1016/j.vibspec.2025.103781

Ping Li , Biwen Shui , Bin Zhang , Yufei Liu , Zhiyuan Yin , Qiang Cui

As part of the Dunhuang grottoes, the Five Temple Grottoes are notable for their overlapping mural structures, painted over multiple dynasties, offering valuable insights into Dunhuang's cultural and artistic evolution. However, due to historical changes and both human and natural impacts, Cave 5 is in poor condition, with various mural diseases. Research on the materials and techniques used in the Five Temple Grottoes is limited. In this study, we employed polarizing microscopy, laser particle size analysis, FT-IR, XRD, and SEM-EDX to analyze the materials and techniques of the collapsed murals in Cave 5. Results showed that Cave 5 murals consist of multiple layers, including clay texture pillars and paint layers. The Northern Zhou Dynasty murals used hematite, calcite, muscovite, and talc, reflecting techniques similar to the Mogao Grottoes. The Northern Song Dynasty murals incorporated hematite, azurite, chlorite, calcite, and gypsum. Additionally, clay in the Northern Zhou Dynasty murals had smaller particle sizes but higher clay content. The use of straw fiber in the Northern Zhou Dynasty murals contrasts with the flax fiber used in the Northern Song Dynasty murals. This study aims to understand the artistic materials and technological characteristics of the murals in Cave 5 and to provide scientific support for their protection and restoration.

作为敦煌石窟的一部分，五寺石窟以其重叠的壁画结构而闻名，这些壁画绘制于多个朝代，为了解敦煌的文化和艺术演变提供了宝贵的见解。然而，由于历史的变迁以及人为和自然的双重影响，5号洞的状况很差，壁画上有各种疾病。对五寺石窟所用材料和工艺的研究有限。本研究采用偏光显微镜、激光粒度分析、FT-IR、XRD、SEM-EDX等方法对5号洞坍塌壁画的材料和工艺进行了分析。结果表明，5洞壁画由多层组成，包括粘土纹理柱和油漆层。北周壁画使用赤铁矿、方解石、白云母和滑石，反映了与莫高窟相似的技术。北宋壁画中含有赤铁矿、蓝铜矿、绿泥石、方解石和石膏。此外，北周壁画中的粘土粒径较小，但粘土含量较高。北周壁画中稻草纤维的使用与北宋壁画中亚麻纤维的使用形成了鲜明的对比。本研究旨在了解5号洞壁画的艺术材料和工艺特点，为其保护修复提供科学依据。

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引用次数: 0

Wine composition detection utilizing 1DCNN and the self-attention mechanism 基于1DCNN和自注意机制的葡萄酒成分检测

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-15 DOI: 10.1016/j.vibspec.2025.103768

Keda Chen, Shengwei Wang, Shenghui Liu

This study proposes a one-dimensional convolutional autoencoder model that incorporates self-attention mechanisms—1DCNN-ATTENTION-SAE. This model solves the problem of unstable prediction performance in quantitative modeling of multiple components in infrared spectroscopy, especially when dealing with complex nonlinear problems involving severe overlap of characteristic peak bands and difficulty in capturing high-dimensional nonlinear features. The model effectively captures long-term dependencies in infrared spectral data and is particularly suitable for the rapid detection of key components such as pH, total phenols, total sugars, and alcohol in wine. On the ATR-FTIR dataset of dry red wine, the proposed model demonstrates robust performance, achieving a root mean square error (RMSE) of 2.017 g/L and a coefficient of determination (R²) of 0.967 g/L. The RMSE represents the average prediction error across the chemical properties measured (pH, total phenols, total sugars, and alcohol). Similarly, the R² value reflects the overall predictive accuracy of the model for these properties. Additionally, the 1DCNN-ATTENTION-SAE model was further optimized by integrating the DeepHealth algorithm, which is based on the TRANSFORMER structure, forming the hybrid DeepHealth & 1DCNN-ATTENTION-SAE feature fusion model. When applied to the near-infrared spectral dataset of open-source pharmaceuticals to predict bioactivity values, the hybrid model achieved an RMSE of 3.262 g/L and an R² of 0.914 g/L, validating its transfer learning capability in handling "cross-instrument, cross-wavelength" infrared spectral data.

本研究提出了一种包含自注意机制的一维卷积自编码器模型- 1dcnn -attention - sae。该模型解决了红外光谱中多组分定量建模预测性能不稳定的问题，特别是在处理特征峰带严重重叠和高维非线性特征难以捕获的复杂非线性问题时。该模型有效地捕获了红外光谱数据中的长期依赖关系，特别适用于快速检测葡萄酒中的关键成分，如pH值、总酚、总糖和酒精。在干红葡萄酒的ATR-FTIR数据集上，该模型表现出稳健的性能，均方根误差（RMSE）为2.017 g/L，决定系数（R²）为0.967 g/L。RMSE表示测量的化学性质（pH、总酚、总糖和醇）的平均预测误差。同样，R²值反映了模型对这些属性的总体预测精度。此外，通过整合基于TRANSFORMER结构的DeepHealth算法，进一步优化了1DCNN-ATTENTION-SAE模型，形成了混合DeepHealth &；1DCNN-ATTENTION-SAE特征融合模型。将混合模型应用于开源药物近红外光谱数据集预测生物活性值，RMSE为3.262 g/L， R²为0.914 g/L，验证了混合模型处理“跨仪器、跨波长”红外光谱数据的迁移学习能力。

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引用次数: 0

Sputter deposited silver film as an alternative tool for Raman signal enhancement in plasma-activated water 溅射沉积银膜作为等离子体活化水中拉曼信号增强的替代工具

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103765

Nilton F. Azevedo Neto , Samuel A. Marques , Felipe S. Miranda , Pedro W.P. Moreira Junior , Andre L.J. Pereira , Carlos J.L. Constantino , José H. Dias da Silva , Rodrigo S. Pessoa

Plasma-activated water (PAW), generated by non-thermal plasma, has shown great potential in various applications, including bacterial inactivation, agriculture, and disinfection, primarily attributed to the presence of reactive oxygen and nitrogen species (RONS). Traditional characterization methods for RONS in PAW often encounter limitations in sensitivity and specificity, particularly at low concentrations. In this study, we investigated the application of surface-enhanced Raman spectroscopy (SERS) for the characterization of PAW. A SERS substrate was prepared by sputter-depositing a silver (Ag) film onto a cover glass. The structural, topographic, and optical properties of the film were characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and reflectance spectroscopy. Utilizing the Ag film substrates, we observed a substantial enhancement in the Raman signals of deionized water compared to measurements on glass substrates, achieving an analytical enhancement factor (AEF) of approximately 30 for the O–H stretching band. The characterization of PAW using the SERS substrate enabled the acquisition of well-defined Raman spectra and facilitated the detection of nitrate ions (NO₃⁻) in PAW generated by a dielectric barrier discharge reactor. The results obtained from the PAW Raman spectra were further supported by changes in physicochemical properties, such as decreased pH and increased conductivity, as well as UV-Vis spectroscopy results. These findings demonstrate that sputter-deposited Ag films can serve as a valuable methodological tool for the characterization of PAW using Raman spectroscopy.

等离子体活化水（PAW）由非热等离子体产生，在各种应用中显示出巨大的潜力，包括细菌灭活，农业和消毒，主要归因于活性氧和氮物种（RONS）的存在。传统的表征方法在敏感性和特异性上往往存在局限性，特别是在低浓度下。在这项研究中，我们研究了表面增强拉曼光谱（SERS）在PAW表征中的应用。通过溅射沉积银（Ag）膜到覆盖玻璃上制备了SERS衬底。利用x射线衍射（XRD）、原子力显微镜（AFM）和反射光谱对膜的结构、形貌和光学性能进行了表征。利用Ag薄膜衬底，我们观察到去离子水的拉曼信号与玻璃衬底的测量结果相比有显著增强，O-H拉伸带的分析增强因子（AEF）约为30。使用SERS衬底对PAW进行表征，可以获得定义良好的拉曼光谱，并有助于在介质阻挡放电反应器产生的PAW中检测硝酸盐离子（NO₃⁻）。从PAW拉曼光谱得到的结果进一步得到了物理化学性质变化的支持，如pH值降低和电导率增加，以及UV-Vis光谱结果。这些发现表明，溅射沉积Ag薄膜可以作为一种有价值的方法工具，用拉曼光谱来表征PAW。

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引用次数: 0