Applied Spectroscopy最新文献

{"title":"Machine Learning Augmented Detection of Trace Explosives on Surfaces Using Active Longwave Infrared Imaging.","authors":"William Barney, Mitesh Amin, William Herzog, Jason Jong, Max Kenngott, Keegan Quigley, Patrick Wen, Roderick R Kunz","doi":"10.1177/00037028261471887","DOIUrl":"https://doi.org/10.1177/00037028261471887","url":null,"abstract":"<p><p>There is a need for rapid non-contact detection of trace explosives on arbitrary surfaces for applications such as checkpoint screening and post-blast forensics. In this work, active longwave infrared imaging, using a tunable quantum cascade laser light source, is shown to be able to detect three common explosives on typical luggage material substrates at surface concentrations as low as 230 ng/cm². Use of a random forest machine learning algorithm, trained using synthetically produced spectra, allows performance beyond predicted limitations of speckle noise. The random forest algorithm further allows discrimination against ambient dust without specific training. Use of high collection angles reduces the dynamic range requirement of the system without impairing performance. Potential system improvements are discussed to meet the area coverage rate needs of several applications.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261471887"},"PeriodicalIF":2.2,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149073318","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":"Optical Trapping of Submicron DPPC Vesicles for Raman Spectroscopy Measurements.","authors":"K A Okotrub, A N Omelchenko, A A Borisova, N V Surovtsev","doi":"10.1177/00037028261495192","DOIUrl":"10.1177/00037028261495192","url":null,"abstract":"<p><p>Extracellular vesicles are promising biomarkers for non-invasive diagnostics, but their heterogeneity in size and composition challenges conventional analysis. Raman spectroscopy with optical tweezers can characterize individual vesicles, yet the full capabilities of this approach remain underexplored. Here, we investigate Raman spectroscopy integrated with optical tweezers for real-time characterization of submicron particles, focusing on vesicle trapping kinetics. Using 100-nm dipalmitoylphosphatidylcholine (DPPC) vesicles, we examine the effects of objective type (water immersion, oil immersion, dry), laser power, concentration, and polarization on trapping dynamics and Raman signals. Vesicle trapping kinetics were satisfactorily described using a kinetic balance model with a stretched exponential that separates the contributions of particle influx, thermal escape, and finite trap capacity. Our measurements reveal distinct regimes with different objectives: with water immersion the signal rapidly saturates because the deep, stiff trap suppresses escape; with oil immersion spherical aberrations reduce trap stiffness and depth, leading to a continuous increase in equilibrium signal with power. The characteristic trapping time universally depends on total laser power, independent of objective. At elevated concentrations, the stretching exponent parameter β drops from 1 to ∼0.6 and large-amplitude steps appear, indicating vesicle aggregation as a source of kinetic dispersion. Numerical simulations qualitatively support the interpretation that trap stiffness and depth govern the observed kinetic regimes. These findings provide practical guidelines for single-vesicle Raman experiments and show that kinetic analysis offers a sensitive label-free indicator of sample polydispersity and aggregation that can be applied to real biological samples.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261495192"},"PeriodicalIF":2.2,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149073309","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":"Rapid Authentication and Quality Assessment of Fish oil Using Near Infrared Spectroscopy.","authors":"Linlin Wu, Xianggang Yin, Qilin Xu, Xiaohan Zhao, Xin Chen, Jiayi Jiang, Yifeng Zhou, Jun Huang","doi":"10.1177/00037028261475399","DOIUrl":"https://doi.org/10.1177/00037028261475399","url":null,"abstract":"<p><p>A near-infrared (NIR) spectroscopy-based framework was developed to efficiently evaluate fish oil quality. The workflow focuses on three main objectives: quantification of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), detection of adulteration, and classification of purity. For EPA/DHA quantification, spectra were preprocessed, and partial least squares regression (PLSR) was compared with support vector regression, where PLSR achieved higher accuracy and more stable performance. For adulteration quantification, pure fish oil was spiked with rapeseed, soybean, corn, and palm oils at concentrations ranging from 0% to 50% (w/w). Linear discriminant analysis of NIR spectra revealed distinct separation between pure and highly adulterated samples, with partial overlap at intermediate levels. PLSR models predicted adulterant percentages for all four oils with prediction R² values ≥ 0.98 and low error. For purity classification, random forest models based on NIR spectra showed strong performance, and integration of selected gas chromatography variables through mid-level data fusion further improved accuracy to 91.7%. Overall, this NIR-based workflow provides a rapid, nondestructive, and reliable tool for label verification, supplier management, and routine quality assurance in fish oil products.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261475399"},"PeriodicalIF":2.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036283","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":"Probing Keratin Fiber Aging with Terahertz Time-Domain Spectroscopy: Linking Disulfide Cleavage and Crystallinity Loss.","authors":"Xun Zhang, Xianggui Zhang, Yalu Song, Bin Yang, Xiaoqiuyan Zhang, Min Hu","doi":"10.1177/00037028261469576","DOIUrl":"https://doi.org/10.1177/00037028261469576","url":null,"abstract":"<p><p>Bio-based protein fibers such as cashmere are valued for low weight and thermal insulation but are vulnerable to environmental aging (UV, hygrothermal, alkaline), which undermines hierarchical structure and limits service life in high-demand applications. Rapid, non-destructive assays that bridge chemical fingerprints with crystalline and supramolecular readouts are therefore needed. This study integrates Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), and terahertz time-domain spectroscopy (THz-TDS) to systematically characterize the multiscale aging of cashmere fibers under ultraviolet (UV) and alkaline conditions. Alkaline media preferentially and rapidly induce disulfide (S-S) bond scission, with the Raman I₅₁₁/I₁₄₅₁ ratio dropping from ∼0.65 to 0.15 (≈77% decrease) after 72 h in NaOH, whereas UV irradiation-by virtue of deeper penetration-predominantly compromises interior crystalline domains, reducing the XRD Segal crystallinity index from 0.74 to 0.09 (≈88% decrease) after 600 h. Both pathways drive a common structural evolution characterized by decreased α-helical content, increased amide III disorder (I₁₂₄₅/I₁₄₅₁↑), and a net loss of relative crystallinity (alkaline: 0.74→0.52, ≈30% decrease). THz-TDS, via the 1.5 THz absorption, records a concomitant attenuation (from 26.74 to 5.02 under UV, ≈81% decrease; to 8.32 under alkali, ≈69% decrease), indicating that low-frequency collective modes are jointly governed by crystallinity, intermolecular coupling, and chain-segment constraints. These results show that THz-TDS effectively complements FTIR/Raman/XRD, which are less responsive to ensemble interactions, and is therefore well suited for rapid, non-destructive aging assessment and pathway discrimination. In this multimodal framework, THz-TDS serves as an ensemble-level probe that captures low-frequency collective vibrations and intermolecular coupling, thereby complementing the local chemical fingerprints from FTIR/Raman and the crystallinity information from XRD. The proposed multimodal framework provides a methodological basis for durability evaluation and potential online monitoring of protein fibers, and it lays the groundwork for future multimodal quantitative modeling.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261469576"},"PeriodicalIF":2.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036285","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":"Synchrotron-Based X-ray Fluorescence Profiling of Asphaltene Deposits: Overcoming Atmospheric and Scattering Interferences for Enhanced Elemental Characterization.","authors":"Mohammad Rezaei-Pandari, Mohammad Ali Haddad, Roghaye Izan, Amir Sayid Hassan Rozatian, Messaoud Harfouche, Latif Ullah Khan","doi":"10.1177/00037028261474595","DOIUrl":"https://doi.org/10.1177/00037028261474595","url":null,"abstract":"<p><p>Asphaltene deposition remains a significant operational challenge in the petroleum industry, requiring precise elemental characterization to understand formation mechanisms and develop mitigation strategies. This study utilizes Synchrotron Radiation X-ray Fluorescence (SR-XRF) at the SESAME facility to profile asphaltene deposits from four southwestern Iranian oil wells. To enhance technical soundness and data reliability, the experimental spectral window was optimized to overcome interferences inherent in long air-path measurements, specifically targeting the mitigation of low-energy atmospheric absorption and high-energy Rayleigh/Compton scattering. A critical spectroscopic evaluation was performed to resolve the spectral overlap between atmospheric Krypton (Kr) <math><msub><mi>K</mi><mi>β</mi></msub></math> emission and the Strontium (Sr) <math><msub><mi>K</mi><mi>α</mi></msub></math> line, enabling a definitive identification of inorganic mineral inclusions. The analysis revealed complex elemental profiles comprising transition metals (Fe, Ni, Cu, Zn) and the heteroatom bromine (Br). Variations in nickel (Ni) intensity across samples suggest its role as a sample-intrinsic component, while high iron (Fe) levels indicate a mix of natural porphyrin complexes and extrinsic mineral contamination. These findings demonstrate that by resolving specific spectral artifacts, SR-XRF serves as a robust, non-destructive analytical tool for the characterization and proactive management of asphaltene-related challenges in petroleum production.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261474595"},"PeriodicalIF":2.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149023720","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":"EXPRESS: A Novel Er³⁺ Doped SiO₂-Al₂O₃-Na₂O-CaO Glass Materials for Near-Infrared Photonic Applications: Unveiling the Structural, Optical Absorption, Optical Bandgap Energy, Photoluminescence, and Spectroscopic Properties.","authors":"Y Dana Rao, Raghavendra Bairy, Ravi Nirlakalla, Ravanamma Rallapalli, S N Nazrin, Shubham Sharma, Sankar Narayan Das, Razaullah Hafiz Ullah, Aseel Smerat, Medhat M Helal","doi":"10.1177/00037028261490624","DOIUrl":"10.1177/00037028261490624","url":null,"abstract":"<p><p>This article is temporarily under embargo.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261490624"},"PeriodicalIF":2.2,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988002","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":"Comparison of Linear and Nonlinear Regression Models for Dual ED-XRF Analysis of Trace Arsenic and Lead in Rice-Based Foods.","authors":"Murphy Carroll, Zili Gao, Lili He","doi":"10.1177/00037028261492376","DOIUrl":"10.1177/00037028261492376","url":null,"abstract":"<p><p>Applications of energy dispersive x-ray fluorescence (ED-XRF) spectrometry for trace analysis of arsenic (As) and lead (Pb) in foods are limited by spectral interference around characteristic emission lines. To overcome this limitation, various chemometric data modeling approaches can be applied; however, dataset characteristics and model structure can significantly impact these approaches in their ability to accurately relate spectral features to analyte concentrations. This study compared linear and nonlinear multivariate regression models (partial least squares regression (PLSR) and artificial neural network (ANN), respectively) to model the spectral overlap and enable simultaneous analysis of trace As and Pb in rice-based foods. Calibration standards were developed through pelletization of powdered rice (4.0 ± 0.2 g) spiked with As and Pb (∼0-600 µg kg^-1). Standards were analyzed via ED-XRF and resultant spectra were used to develop calibration models utilizing PLSR and ANN modeling. To assess method accuracy and applicability in real-world scenarios, a certified reference material of rice, 11 commercial rice-based products, and 12 Pb-spiked commercial foods were analyzed. PLSR yielded stronger calibration performance (R²_As = 0.98, R²_Pb = 0.93) compared to ANN; however, ANN modeling achieved lower quantification limits for As in samples containing detectable and non-detectable Pb content (53 and 54 µg kg^-1, respectively). Both models achieved successful validation in the analysis of the certified reference material with errors of +12.7% (PLSR) and -19.4% (ANN) in the prediction of As. PLSR modeling demonstrated strong predictive capabilities in commercial rice-based samples containing non-detectable Pb content, obtaining a median absolute error of 10.7% in As quantification and 8 of 11 samples yielding less than 20% error. Conversely, in Pb-spiked commercial samples (76-417 µg kg^-1 Pb), the ANN outperformed PLSR, obtaining an average absolute error of 14.1% with 8 of 12 samples yielding less than 20% error in As determinations. PLSR modeling correctly identified low Pb content in samples containing non-detectable Pb content while also yielding 75% accuracy in the classification of samples containing Pb levels above current regulatory limits, supporting its use as a tool for semi-quantitative screening of Pb content in rice/rice-based foods at levels aligning with current regulations. This study compared PLSR and ANN modeling in their ability to statistically model and overcome As/Pb spectral overlap in ED-XRF, highlighting their advantages and limitations in specific analytical scenarios and demonstrating their potential to enable trace As and Pb screening in rice-based foods.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261492376"},"PeriodicalIF":2.2,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149001164","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":"A Low-Cost 3D-Printed Ocular Microspectrophotometer for Teaching Visible Spectroscopy.","authors":"I Orlando-Guerrero, U Ruiz-Corona, Z Hernández-Paxtian, C M Santibáñez-Camarillo, Ordaz-Hernández A","doi":"10.1177/00037028261492052","DOIUrl":"10.1177/00037028261492052","url":null,"abstract":"<p><p>Visible microspectrophotometry enables the nondestructive acquisition of spectral information from microscopic regions and constitutes a valuable tool for the experimental teaching of spectroscopy, microscopy, and optical instrumentation. This work presents the design, fabrication, and evaluation of a low-cost ocular microspectrophotometer (OMP), developed using 3D printing and designed for integration with conventional optical microscopes. The device incorporates a diffraction grating, optical apertures, and a CMOS sensor to acquire continuous spectra over the 433-827 nm wavelength range from microscopic regions approximately 40 µm in diameter. In addition, a Python-based algorithm was developed to convert spectral images into transmittance vectors and calibrate the pixel-to-wavelength relationship. The system achieved a spectral resolution of approximately 0.84 nm/pixel and was validated using reference optical filters. Its educational potential was demonstrated through the spectral characterization of the RGB subpixels of a smartphone LCD screen and the analysis of the transmittance of a plant leaf. These activities provide an effective means of introducing concepts such as additive color synthesis, spatial resolution, light-matter interaction, and digital image processing. The OMP constitutes an accessible and reproducible platform that integrates additive manufacturing, digital image processing, and hands-on experimentation for teaching spectroscopy, microscopy, and the design of scientific instrumentation.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261492052"},"PeriodicalIF":2.2,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975546","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":"EXPRESS: Detection of Food Contamination Using a Hybrid Raman and Laser-Induced Breakdown Spectroscopy (LIBS) System.","authors":"Sungho Shin, Iyll-Joon Doh, Kennedy Okeyo, Euiwon Bae, J Paul Robinson, Bartek Rajwa","doi":"10.1177/00037028261489365","DOIUrl":"https://doi.org/10.1177/00037028261489365","url":null,"abstract":"<p><p>Due to the prevalence of food fraud and its associated risks to human health, food safety and quality assurance systems must become more comprehensive, rapid, and accurate. Food fraud often involves contamination, which refers to the presence of harmful substances in food, including biological, chemical, and physical adulterants. Traditional detection methods, such as microbiological and chemical testing, are limited in sensitivity, are time-consuming, and often lack comprehensive analysis capabilities. This report presents the development of a hybrid Raman and laser-induced breakdown spectroscopy (LIBS) system, referred to as Hy-R-LIBS, for the quantitative analysis of food contaminants on fruit surfaces. In this work, the term <i>hybrid</i> denotes a system in which both spectroscopic modalities are co-housed within a single optical chassis, share a common optical path, and perform automated sequential acquisition on the same sample spot, enabling either independent or joint analysis of the resulting spectra. Raman spectroscopy provides molecular fingerprinting capability, while LIBS enables elemental analysis at low concentrations. The miniaturization of the combined system is feasible because Raman and LIBS share similar optical pathways. To overcome the inherently weak Raman scattering cross-section that limits sensitivity in portable systems, a surface-enhancement strategy based on silver nanoparticles (AgNPs) was employed, which improves both Raman scattering signals and LIBS plasma emission. The study demonstrated that AgNP-assisted surface enhancement improves both LIBS and Raman signals, as confirmed by the quantitative analysis of food contaminants on fruit peel. Pesticide and heavy -metal controls were used, and the limit of detection (LOD) for various contaminants was estimated. The results indicate the feasibility of the hybrid system and the enhancement achieved through surface-enhancement techniques. Overall, the Hy-R-LIBS system offers a promising approach for the quantitative analysis of food contaminants. The combination of these optical spectroscopic techniques enables comprehensive investigations of complex food matrices contaminated with both organic chemicals and metallic residues, providing valuable insights for food safety assurance and public health.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261489365"},"PeriodicalIF":2.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904989","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 SVD for Signal-Noise Separation in Spectroscopic Data.","authors":"Nicolas Spegazzini","doi":"10.1177/00037028261488448","DOIUrl":"10.1177/00037028261488448","url":null,"abstract":"<p><p>Singular Value Decomposition (SVD) is a primary tool for exploratory analysis and for separating signal from noise and background in spectroscopic data, but it does not account for baseline variation. Standard SVD can assign to baseline features singular values comparable in magnitude to those of genuine chemical components, obscuring the boundary between signal and noise in the singular value spectrum and complicating determination of the chemical rank. This Note describes a preprocessing-enhanced SVD algorithm in which a Fourier-domain first-derivative transformation is combined with weights derived from the noise present in the data before the decomposition is performed, suppressing baseline contributions without loss of true signal components. The abstract spectra and time-traces are returned to the original spectral and temporal domains by inverse transformation, and a sign-ambiguity resolution procedure based on correlation coefficients is included. The algorithm is demonstrated on both synthetic and experimental time-dependent FTIR spectroscopic data. Relative to standard SVD, the enhanced decomposition yields a flat noise floor beyond the retained components, confining the baseline contribution to the discarded subspace rather than dispersing it through the low-rank subspace, and the recovered abstract spectral and temporal vectors carry less baseline contamination. Although demonstrated with time-dependent FTIR data, the algorithm applies to any variable-dependent spectroscopic measurement that can be organized as a two-way matrix. The implementation is released as open-source software.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"37028261488448"},"PeriodicalIF":2.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904970","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}