Applied Spectroscopy最新文献

{"title":"Determination of Laser-Induced Fluorescence Lifetimes Excited by Pulses of Comparable Duration.","authors":"Lize Coetzee, Esa Jaatinen","doi":"10.1177/00037028251332975","DOIUrl":"https://doi.org/10.1177/00037028251332975","url":null,"abstract":"<p><p>This paper presents a novel analytical technique for evaluating fluorescence lifetimes excited by a nanosecond pulsed laser using a linearized rate equation approach that accounts for the incident pulse temporal distribution, an equivalent instrument response function, and non-exponential fluorescence decay which limits the application of traditional fluorescence lifetime techniques in stand-off applications. The approach is applied to model the fluorescence of a variety of pharmaceutical powders and phosphorescing samples exhibiting non-exponential decay and compared to results obtained with the maximum entropy method. Fluorescence lifetimes are found to be 3-5  ns, typical for organic fluorescent powders, and phosphorescence lifetimes were on the order of hundreds of nanoseconds. The approach also shows potential for determining the composition of mixed samples and can be readily extended to model increasingly complex scenarios with additional fluorescing or phosphorescing components.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251332975"},"PeriodicalIF":2.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955096","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}

{"title":"Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes.","authors":"Imran Rehan, Kamran Rehan, Sabiha Sultana, Mujeeb Ur Rehman","doi":"10.1177/00037028251334383","DOIUrl":"https://doi.org/10.1177/00037028251334383","url":null,"abstract":"<p><p>Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a <i>k</i>-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251334383"},"PeriodicalIF":2.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960554","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}

{"title":"Perspective on the Capacity of the Rashomon Effect in Multivariate Data Analysis.","authors":"John H Kalivas","doi":"10.1177/00037028251330324","DOIUrl":"https://doi.org/10.1177/00037028251330324","url":null,"abstract":"<p><p>Presented is a perspective proposing to expand some fragmented spectroscopic modeling and data analysis practices by incorporating multivariate ideologies. For example, through recognizing the theory of analytic chemistry (TAC) by Booksh and Kowalski, it is common to use the multivariate processes (higher orders) of multiple wavelengths for regression and prediction or classification, fusing multiple instruments, or applying multi-way methods such as parallel factor analysis (PARAFAC). Each wavelength, instrument, PARAFAC order deliver different views of underlying sample-wise full matrix effects adding more information per dimension for improved data characterizations. Reasoned here is that model selection, figures of merit, and sample similarity assessments for model prediction reliability, outlier detection, or classification purposes can meaningfully progress by recognizing the multivariate principles of the TAC and additionally, the importance of the Rashomon effect. Applying the Rashomon effect with the TAC removes conventional fragmented data analysis approaches bringing a more wholeness to data analysis. Included in this discussion is that due to the Rashomon effect, interpretation of spectral models is not reasonable. For an uncommon view of these concepts, the perspective ends with drawing parallels between sample-wise matrix effects and the concepts explicate and implicate orders from physicist David Bohm's depiction of our physical and conscious world and universe. It is hoped that this perspective tempts reflection in your particular area of spectroscopy.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251330324"},"PeriodicalIF":2.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961144","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}

{"title":"Enhanced Carbon Dioxide Uptake in Drilled Hollow Core Fibers for Raman Spectroscopy.","authors":"Brandon Demory, Jorge Arteaga, Sarah Sahota-Dhillon, Sayantani Ghosh, Tiziana Bond, Allan Chang","doi":"10.1177/00037028251329418","DOIUrl":"https://doi.org/10.1177/00037028251329418","url":null,"abstract":"<p><p>Fiber-based Raman spectroscopy enhances the Raman signal by maximizing the overlap of the optical field and the gas species. However, filling the hollow-core fiber (HCF) with gas requires time that is dependent on the fiber core diameter, fiber length, and pressure of the gas. At ambient pressure, the fiber gas uptake is driven by diffusion into the fiber ends, severely limiting the response time of the system. By laser drilling access holes to the core along the length of the fiber, the uptake time of the gas is reduced, improving the system response time. In this work, we study the carbon dioxide (CO₂) sensor dynamics based on Raman signal intensity generated in HCFs. The signal intensity versus gas concentration is characterized by controlling the CO₂ concentration in the surrounding environment of the fiber. Next, we characterize the gas uptake time in HCFs as a function of fiber length. Finally, we optimize the access hole configuration along the fiber, demonstrating reduced sensor uptake time by a factor of three.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251329418"},"PeriodicalIF":2.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143966191","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}

{"title":"Advertising and Front Matter.","authors":"","doi":"10.1177/00037028251334196","DOIUrl":"https://doi.org/10.1177/00037028251334196","url":null,"abstract":"","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":"79 4","pages":"473-480"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960264","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}

{"title":"Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development.","authors":"George C-Y Chan, Gary M Hieftje, Nicoló Omenetto, Ove Axner, Arne Bengtson, Nicolas H Bings, Michael W Blades, Annemie Bogaerts, Mikhail A Bolshov, José A C Broekaert, WingTat Chan, José M Costa-Fernández, Stanley R Crouch, Alessandro De Giacomo, Alessandro D'Ulivo, Carsten Engelhard, Heinz Falk, Paul B Farnsworth, Stefan Florek, Gerardo Gamez, Igor B Gornushkin, Detlef Günther, David W Hahn, Wei Hang, Volker Hoffmann, Norbert Jakubowski, Vassili Karanassios, David W Koppenaal, R Kenneth Marcus, Reinhard Noll, John W Olesik, Vincenzo Palleschi, Ulrich Panne, Jorge Pisonero, Steven J Ray, Martín Resano, Richard E Russo, Alexander Scheeline, Benjamin W Smith, Ralph E Sturgeon, José-Luis Todolí, Elisabetta Tognoni, Frank Vanhaecke, Michael R Webb, James D Winefordner, Lu Yang, Jin Yu, Zhanxia Zhang","doi":"10.1177/00037028241263567","DOIUrl":"10.1177/00037028241263567","url":null,"abstract":"<p><p>The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings, and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review. A total of 48 scientists participated in this endeavor, contributing a combined list of 1055 individual articles spanning 17 sub-disciplines of spectrochemical analysis into what the current community views as \"key\" publications. Of these cited articles, 60 received nominations from four or more scientists, establishing them as the most indispensable reading materials. The outcome of this collaborative effort is intended to serve as a valuable resource not only for current practitioners in atomic spectroscopy but also for present and future students who represent coming generations of analytical atomic spectroscopists.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"481-735"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141330295","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}

{"title":"Advertising and Front Matter.","authors":"","doi":"10.1177/00037028251334196","DOIUrl":"https://doi.org/10.1177/00037028251334196","url":null,"abstract":"","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":"79 4","pages":"473-480"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109645","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}

{"title":"Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images Using Machine Learning.","authors":"Harrison Edmonds, Sudipta S Mukherjee, Brooke Holcombe, Kevin Yeh, Rohit Bhargava, Ayanjeet Ghosh","doi":"10.1177/00037028251325553","DOIUrl":"https://doi.org/10.1177/00037028251325553","url":null,"abstract":"<p><p>Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUC). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like principal component analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating the acquisition of more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets, and additional computational overhead. In this work, we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly six-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution and subsequently using the upscaled spectra to accurately predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251325553"},"PeriodicalIF":2.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750868","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}

{"title":"Dip or Step-Like Features in the Infrared Reflection Spectra of Heat-Treated Polyoxymethylene.","authors":"Naoto Nagai, Yuko Amaki","doi":"10.1177/00037028251328049","DOIUrl":"https://doi.org/10.1177/00037028251328049","url":null,"abstract":"<p><p>When injection-molded polyoxymethylene is heat-treated below its melting point, it shows increased polarized reflection along the injection direction, as confirmed through micro-infrared spectroscopy. A characteristic dip or step-like structure appears around 940 cm^-1. Previously, the origin of this structure was unclear. However, we have found that it can be explained by refining the calculation model to account for the relative permittivity perpendicular to the sample surface.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251328049"},"PeriodicalIF":2.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727515","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}

{"title":"Combining a Multispectral Camera and Spectrometer for Spectral Data Acquisition and Noninvasive Blood Composition Measurement.","authors":"Ling Lin, Honghui Zeng, Shuo Wang, Kang Wang, Gang Li","doi":"10.1177/00037028251327207","DOIUrl":"https://doi.org/10.1177/00037028251327207","url":null,"abstract":"<p><p>The dynamic spectroscopic method, as a noninvasive blood component measurement method, currently uses spectrometers as the main measurement instrument. However, spectrometers have limited accuracy in measuring light intensity at each wavelength, which restricts the measurement accuracy of the dynamic spectrum method. In this paper, a combination of a multispectral camera and a spectrometer is utilized for the first time to measure spectral photoplethysmography (PPG) signals. Both the high amplitude resolution and high accuracy of the multispectral camera in terms of sampling values and the advantage of the spectrometer in terms of the number of wavelengths are exploited. According to the experimental data, this method effectively improves the measurement results. In particular, when measuring for hemoglobin, the mean absolute percentage error (MAPE) decreased by 25.3% and 22.9%, respectively compared with a single spectrometer and a multispectral camera. For platelet measurements, the MAPE decreased by 28.9% and 22.8%, respectively. For total bilirubin measurements, the MAPE decreased by 14.5 and 26.3%, respectively. It demonstrates that the noninvasive blood component measurement method of a combined multispectral camera and spectrometer can effectively reduce the interference of non-target components and improve measurement accuracy.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028251327207"},"PeriodicalIF":2.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727444","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}