Applied Spectroscopy最新文献_第10页

Platform for Aldehyde and Ketone Quantitation Using Surface-Enhanced Raman Spectroscopy. 利用表面增强拉曼光谱进行醛和酮定量的平台。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-03-01 Epub Date: 2024-11-26 DOI: 10.1177/00037028241296845

Merwan Benhabib, Mark C Peterman

引用次数: 0

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs. 融合近红外、中红外和拉曼数据的机器学习方法，用于识别人体骨软骨塞中的软骨退化。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-03-01 Epub Date: 2024-11-08 DOI: 10.1177/00037028241285583

Valeria Tafintseva, Ervin Nippolainen, Vesa Virtanen, Johanne Heitmann Solheim, Boris Zimmermann, Simo Saarakkala, Heikki Kröger, Achim Kohler, Juha Töyräs, Isaac O Afara, Rubina Shaikh

{"title":"Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.","authors":"Valeria Tafintseva, Ervin Nippolainen, Vesa Virtanen, Johanne Heitmann Solheim, Boris Zimmermann, Simo Saarakkala, Heikki Kröger, Achim Kohler, Juha Töyräs, Isaac O Afara, Rubina Shaikh","doi":"10.1177/00037028241285583","DOIUrl":"10.1177/00037028241285583","url":null,"abstract":"Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system. We established the one-block and multi-block classification models using partial least squares discriminant analysis (PLSDA), random forest, and support vector machine (SVM) algorithms. Feature modeling by principal component analysis was tested for the SVM (PCA-SVM) models. The best one-block models were built using MIR and Raman data, discriminating healthy cartilage from damaged with an accuracy of 77.5% for MIR and 77.8% for Raman using the PCA-SVM algorithm, whereas the NIR data did not perform as well achieving only 68.5% accuracy for the best model using PCA-SVM. The multi-block approach allowed an improvement with an accuracy of 81.4% for the best model by PCA-SVM. Fusing three blocks using MIR, NIR, and Raman by multi-block PLSDA significantly improved the performance of the single-block models to 79.1% correct classification. The significance was proven by statistical testing using analysis of variance. Thus, the study suggests the potential and the complementary value of the fusion of different spectroscopic techniques and provides valuable data analysis tools for the diagnostics of cartilage health.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"385-395"},"PeriodicalIF":2.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602887","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}

引用次数: 0

Neurodevelopmental Process Monitoring of Cytosine Arabinoside-Exposed Neurons Using Raman Spectroscopy. 利用拉曼光谱监测暴露于阿拉伯苷的胞嘧啶神经元的神经发育过程

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-03-01 Epub Date: 2024-11-04 DOI: 10.1177/00037028241289147

Kosuke Hashimoto, Hidetoshi Sato

{"title":"Neurodevelopmental Process Monitoring of Cytosine Arabinoside-Exposed Neurons Using Raman Spectroscopy.","authors":"Kosuke Hashimoto, Hidetoshi Sato","doi":"10.1177/00037028241289147","DOIUrl":"10.1177/00037028241289147","url":null,"abstract":"Raman spectroscopy is used to monitor the development of live neurons exposed to cytosine arabinoside (ara-C). Ara-C is widely used to culture neurons and exclude non-neuronal cells. In this study, Raman spectra obtained from neurons exposed to ara-C were plotted using an analytical model of neuronal development to evaluate the impact of ara-C on neuronal development. After two days of culturing, neurons were exposed to ara-C for 24 h at final concentrations of 0 (control), 5, and 25 μM. Principal component analysis (PCA) was performed to build an analytical model for evaluating neurodevelopmental disorders caused by ara-C treatment. We projected the Raman spectra obtained from ara-C-treated cells onto the control group dataset. The distribution of PC1 scores for neurons exposed to ara-C at a final concentration of 5 μM was not significantly different from that of the control group. In contrast, under a final concentration of 25 μM, the data population at 10 and 15 days of culturing overlapped significantly with that of neurons at 4 days of normal culturing. These results suggest that Raman spectroscopy can detect very small physiological alterations in the neurons even after a short-term exposure (24 h) of ara-C. Our analytical method has high potential to evaluate the developmental stages for living neurons under exposure to chemicals.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"396-403"},"PeriodicalIF":2.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575007","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}

引用次数: 0

Reduction of Spectral Overlap in Spectral Surface-Enhanced Raman Spectroscopy Imaging Using a Dove Prism. 利用Dove棱镜减少光谱表面增强拉曼光谱成像中的光谱重叠。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-26 DOI: 10.1177/00037028251322540

Deben N Shoup, Abigail E Smith, Zachary D Schultz

{"title":"Reduction of Spectral Overlap in Spectral Surface-Enhanced Raman Spectroscopy Imaging Using a Dove Prism.","authors":"Deben N Shoup, Abigail E Smith, Zachary D Schultz","doi":"10.1177/00037028251322540","DOIUrl":"https://doi.org/10.1177/00037028251322540","url":null,"abstract":"The ability to combine microscopy and spectroscopy is beneficial for directly monitoring physical and biological processes. Spectral imaging approaches, where a transmission diffraction grating is placed near an imaging sensor to collect both the spatial image and spectrum for each object in the field of view, provide a relatively simple method to simultaneously collect images and spectroscopic responses on the same sensor. Initially demonstrated with fluorescence spectroscopy, the use of spectral imaging in Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) can provide a vibrational spectrum containing molecularly specific information that can inform on chemical changes. However, a major complication to this approach is the spectral overlap that occurs when objects are spaced closely together horizontally. In this work, we add a dove prism to a spectral imaging instrument developed for SERS imaging, enabling rotation of the collected SERS image and dispersed spectrum onto the imaging complementary metal-oxide semiconductor (CMOS) sensor. We demonstrate that this effectively reduces spectral overlap for emitters with clear separation between them and emitters with slightly overlapping point spread functions thereby facilitating collection of unambiguous spectra from each emitter.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251322540"},"PeriodicalIF":2.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514573","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}

引用次数: 0

Understanding and Employing (Non-)Linearities in Attenuated Total Reflection Spectroscopy. 衰减全反射光谱学中（非线性）线性的理解和应用。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-13 DOI: 10.1177/00037028251317540

Thomas G Mayerhöfer, Jürgen Popp

引用次数: 0

Multichannel Dilution Analysis Using a Single Peristaltic Pump Tube. 用单蠕动泵管进行多通道稀释分析。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-05 DOI: 10.1177/00037028251315753

Robbie M Huff, Willis B Jones, Bradley T Jones

{"title":"Multichannel Dilution Analysis Using a Single Peristaltic Pump Tube.","authors":"Robbie M Huff, Willis B Jones, Bradley T Jones","doi":"10.1177/00037028251315753","DOIUrl":"https://doi.org/10.1177/00037028251315753","url":null,"abstract":"Multi-channel dilution analysis (MCDA) is a novel, matrix-matched calibration method that automatically dilutes a standard solution using a tubing manifold to split a solution stream traveling between an autosampler and an analytical instrument. The manifold consists of separate tubing channels of various lengths, resulting in a stairstep of signal levels as different portions of the solution reach the instrument to be measured at different points in time. Multi-channel dilution analysis was initially developed on an inductively coupled plasma optical emission spectrometer (ICP-OES) equipped with a peristaltic pump with five channels, with three used to drive solution through individual tubes in the manifold and a fourth reserved for waste. The setup limited MCDA measurements from being realized on instruments that have fewer pump channels without employing an external pump. The use of a single peristaltic pump line to drive solution through the entire tubing manifold is proposed as a feasible way to overcome this hardware limitation and is applied to both ICP-OES and ICP mass spectrometry (ICP-MS). The method is validated through the analysis of certified reference materials, with recoveries for a suite of analytes ranging from 83% to 117%, with relative standard deviations on the order of 1%. The matrix matching capabilities of the method are demonstrated through the analysis of spiked matrices containing high concentrations of ethanol and calcium, and a comparison to traditional calibration techniques. In addition, the improvements in calibration statistics for ICP-MS measurements using MCDA are discussed.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251315753"},"PeriodicalIF":2.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254460","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}

引用次数: 0

Demixing and Analysis of Complex Biological Raman Hyperspectra Based on Peak Fitting, Amplitude Trend Clustering, and Spectrum Reconstruction. 基于峰值拟合、幅度趋势聚类和光谱重构的复杂生物拉曼超光谱分离与分析。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-02 DOI: 10.1177/00037028241311296

H Georg Schulze, Shreyas Rangan, Martha Z Vardaki, Michael W Blades, Robin F B Turner, James M Piret

{"title":"Demixing and Analysis of Complex Biological Raman Hyperspectra Based on Peak Fitting, Amplitude Trend Clustering, and Spectrum Reconstruction.","authors":"H Georg Schulze, Shreyas Rangan, Martha Z Vardaki, Michael W Blades, Robin F B Turner, James M Piret","doi":"10.1177/00037028241311296","DOIUrl":"https://doi.org/10.1177/00037028241311296","url":null,"abstract":"To better interpret the Raman spectra from mammalian cells, it is often desirable to reduce their complexity by decomposing them into the spectral contributions from individual macromolecules or types of macromolecules. Diverse methods exist for demixing complex spectra, each with different benefits and drawbacks. However, some methods require a library of component spectra that might not be available, while others are hampered by noise and peak congestion that includes many proximal overlapping peaks. Through rapid fitting of individual peaks in every spectrum of a Raman hyperspectral data set, we have obtained individual peak parameters from which we determined the trends for all the peak amplitudes. We then grouped similar trends with k-means clustering. Then we used the peak parameters of all the peaks in a given cluster to reconstruct a spectrum representative of that cluster. This method produced spectra that were less distorted by unrelated overlapping peaks or noise, were less congested than those in the hyperspectral set, and thereby improved peak identification and macromolecule recognition. We have demonstrated the application of the method with Raman spectra from a perchlorate-polystyrene model system and extended it to complex spectra from methanol-fixed mammalian cells. We were able to recover independent spectra of perchlorate and polystyrene in the model system and spectra pertaining to individual macromolecular types (proteins, nucleic acids, lipids) from the mammalian cell data. We discuss how imperfections in spectral preprocessing and peak fitting can adversely affect the results. In summary, we have provided a proof-of-concept for a novel mixture resolution method with different attributes than extant ones.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028241311296"},"PeriodicalIF":2.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078516","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}

引用次数: 0

Feature Selection and Spectral Indices for Identifying Maize Stress Types. 用于识别玉米胁迫类型的特征选择和光谱指数

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-01 Epub Date: 2024-09-23 DOI: 10.1177/00037028241279328

Yanru Li, Keming Yang, Bing Wu

{"title":"Feature Selection and Spectral Indices for Identifying Maize Stress Types.","authors":"Yanru Li, Keming Yang, Bing Wu","doi":"10.1177/00037028241279328","DOIUrl":"10.1177/00037028241279328","url":null,"abstract":"This study aims to identify different types of stress on maize leaves using feature selection and spectral index methods. Spectral data were collected from leaves under heavy metal, water, fertilizer stress, as well as under normal healthy conditions. Preprocessing steps such as continuum removal (CR), standard normal variable (SNV) transformation, multiple scattering correction (MSC), detrend correction (DT), and first-order derivative (FOD) were applied to the raw spectra. Various feature selection methods including ReliefF, chi-square test, recursive feature elimination (FRE), mutual information (MI), random forest (RF), and gradient boosting tree (GBT) were employed to determine the importance scores of different spectral bands, thus identifying sensitive spectral features capable of distinguishing various stress types. Spectral indices for stress type differentiation were constructed using label correlation method. Classification models were built using support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naive Bayes (GNB), extreme gradient boosting (XGBoost), RF, and adaptive boosting (AdaBoost) algorithms. Results indicate that the characteristic spectral bands for differentiating stress types are primarily distributed around the red edge (near 700-800 nm) and water absorption valley (near 1900 nm). Spectral indices constructed using combinations of spectral bands around the near-infrared plateau absorption valley (near 1185 nm) and water absorption valley (near 1460 nm) effectively differentiate maize stress types. Among the modeling classification algorithms, RF and AdaBoost algorithms exhibited optimal performance, demonstrating high classification accuracy on both training and validation sets. These findings hold promise for providing new technical support for maize stress monitoring and diagnosis in agricultural production.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"306-319"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142279786","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}

引用次数: 0

Detection of Nickel Atoms Released from Electrodes in Spark Discharges Using Laser-Induced Fluorescence. 利用激光诱导荧光检测火花放电中电极释放的镍原子

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-01 Epub Date: 2024-09-28 DOI: 10.1177/00037028241285150

Kailun Zhang, Ruike Bi, Johan Tidholm, Jakob Ängeby, Mattias Richter, Andreas Ehn

{"title":"Detection of Nickel Atoms Released from Electrodes in Spark Discharges Using Laser-Induced Fluorescence.","authors":"Kailun Zhang, Ruike Bi, Johan Tidholm, Jakob Ängeby, Mattias Richter, Andreas Ehn","doi":"10.1177/00037028241285150","DOIUrl":"10.1177/00037028241285150","url":null,"abstract":"The reduction of greenhouse gas emissions and the effort of carbon neutrality require the improvement of spark-ignition engines in terms of efficiency and capability to operate on renewable fuels. The electrode wear of spark plugs, used for ignition of novel fuels and lean mixtures, emerges as a significant challenge in this transition. Understanding the physical mechanism and influence of spark operation parameters of the wear process is thus important. Compared to the conventional methodology of performing long-term wear tests, laser-based optical diagnostics methods are capable of assessing electrode wear during one single or a few spark discharges. In this work, the necessary initial steps required for performing optical investigations using laser-induced fluorescence (LIF) are presented. Several excitation pathways of nickel atoms were investigated, and 336.96 nm was identified as the optimal one. This excitation approach yielded emissions between 338.75 and 353.58 nm, effectively avoiding the major interference from N2 plasma emission in spark discharges. Additionally, a linear relationship in fluorescence signal intensity with excitation energy up to 400 µJ was observed. These findings indicate the potential of LIF for in situ diagnostics of electrode wear, contributing to engine development in both efficiency and compatibility with sustainable fuels.","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"281-288"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142340055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solid State Vanadate Laser and 213 nm Rayleigh Rejection Filter Enable Miniaturized Deep Ultraviolet Raman Spectrometers. 固态钒酸盐激光器和 213 nm 瑞利抑制滤波器实现了微型化深紫外拉曼光谱仪。

IF 2.2 3区化学

Applied Spectroscopy Pub Date : 2025-02-01 Epub Date: 2024-09-26 DOI: 10.1177/00037028241280722

Sergei V Bykov, Sanford A Asher

引用次数: 0